Boosting structured additive quantile regression for longitudinal childhood obesity data.

PubMed

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

Efficient Regressions via Optimally Combining Quantile Information*

PubMed Central

Zhao, Zhibiao; Xiao, Zhijie

2014-01-01

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods. PMID:25484481

Non-stationary hydrologic frequency analysis using B-spline quantile regression

NASA Astrophysics Data System (ADS)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

Post-processing techniques to enhance reliability of assignment algorithm based performance measures.

DOT National Transportation Integrated Search

2011-01-01

This study develops an enhanced transportation planning framework by augmenting the sequential four-step : planning process with post-processing techniques. The post-processing techniques are incorporated through a feedback : mechanism and aim to imp...

Quantile rank maps: a new tool for understanding individual brain development.

PubMed

Chen, Huaihou; Kelly, Clare; Castellanos, F Xavier; He, Ye; Zuo, Xi-Nian; Reiss, Philip T

2015-05-01

We propose a novel method for neurodevelopmental brain mapping that displays how an individual's values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample. Copyright © 2015 Elsevier Inc. All rights reserved.

Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence.

PubMed

Borah, Bijan J; Basu, Anirban

2013-09-01

The quantile regression (QR) framework provides a pragmatic approach in understanding the differential impacts of covariates along the distribution of an outcome. However, the QR framework that has pervaded the applied economics literature is based on the conditional quantile regression method. It is used to assess the impact of a covariate on a quantile of the outcome conditional on specific values of other covariates. In most cases, conditional quantile regression may generate results that are often not generalizable or interpretable in a policy or population context. In contrast, the unconditional quantile regression method provides more interpretable results as it marginalizes the effect over the distributions of other covariates in the model. In this paper, the differences between these two regression frameworks are highlighted, both conceptually and econometrically. Additionally, using real-world claims data from a large US health insurer, alternative QR frameworks are implemented to assess the differential impacts of covariates along the distribution of medication adherence among elderly patients with Alzheimer's disease. Copyright © 2013 John Wiley & Sons, Ltd.

Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

ERIC Educational Resources Information Center

Helmreich, James E.; Krog, K. Peter

2018-01-01

We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…

Estimating risks to aquatic life using quantile regression

USGS Publications Warehouse

Schmidt, Travis S.; Clements, William H.; Cade, Brian S.

2012-01-01

One of the primary goals of biological assessment is to assess whether contaminants or other stressors limit the ecological potential of running waters. It is important to interpret responses to contaminants relative to other environmental factors, but necessity or convenience limit quantification of all factors that influence ecological potential. In these situations, the concept of limiting factors is useful for data interpretation. We used quantile regression to measure risks to aquatic life exposed to metals by including all regression quantiles (τ  =  0.05–0.95, by increments of 0.05), not just the upper limit of density (e.g., 90th quantile). We measured population densities (individuals/0.1 m2) of 2 mayflies (Rhithrogena spp., Drunella spp.) and a caddisfly (Arctopsyche grandis), aqueous metal mixtures (Cd, Cu, Zn), and other limiting factors (basin area, site elevation, discharge, temperature) at 125 streams in Colorado. We used a model selection procedure to test which factor was most limiting to density. Arctopsyche grandis was limited by other factors, whereas metals limited most quantiles of density for the 2 mayflies. Metals reduced mayfly densities most at sites where other factors were not limiting. Where other factors were limiting, low mayfly densities were observed despite metal concentrations. Metals affected mayfly densities most at quantiles above the mean and not just at the upper limit of density. Risk models developed from quantile regression showed that mayfly densities observed at background metal concentrations are improbable when metal mixtures are at US Environmental Protection Agency criterion continuous concentrations. We conclude that metals limit potential density, not realized average density. The most obvious effects on mayfly populations were at upper quantiles and not mean density. Therefore, we suggest that policy developed from mean-based measures of effects may not be as useful as policy based on the concept of limiting factors.

El Niño in the Pliocene

NASA Astrophysics Data System (ADS)

White, S. M.; Ravelo, A. C.

2016-12-01

It is unclear to what extent ENSO depends on mean climatic state. The Pliocene is an excellent test case because the tropical Pacific was markedly different than today, with a zonal temperature gradient as low as 1.5°C [Wara et al., 2005] and a deeper thermocline across the entire basin [Ford et al., 2015]. This would be expected to weaken the Bjerknes and thermocline feedbacks, thus strongly damping ENSO variability. However, it is possible that other relevant feedbacks evolved along with the Bjerknes and thermocline feedbacks, such that the net effect was only a small change in ENSO [Manucharyan and Fedorov, 2014]. Existing reconstructions of Pliocene ENSO [Scroxton et al., 2011; Watanabe et al., 2011] support the latter view, though not conclusively; a reanalysis of the Scroxton et al. data reveals lower Pliocene ENSO variability compared to the late Holocene. To reconstruct Pliocene ENSO, we perform Mg/Ca analyses on individual planktonic foraminifera from ODP 849 in the eastern equatorial Pacific, yielding a distribution of temperatures from each selected time interval. We use quantile-quantile plots to compare Pliocene temperature distributions to the late Holocene; differences in the warm tail are attributable to changes in El Niño events. Preliminary data show that the amplitude of El Niño events was similar to the late Holocene at 3.1 Ma, but was reduced at 4.5 Ma and at 5.0 Ma. At 5.5 Ma, El Niño amplitude appears similar to the late Holocene, though La Niña amplitude appears lower. These findings, along with additional data, will be discussed in the context of long-term trends in thermocline depth, zonal SST gradient, and Panamanian gateway throughflow.

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

PubMed

Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

2017-01-01

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

Quantile regression and clustering analysis of standardized precipitation index in the Tarim River Basin, Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Peng; Xia, Jun; Zhang, Yongyong; Han, Jian; Wu, Xia

2017-11-01

Because drought is a very common and widespread natural disaster, it has attracted a great deal of academic interest. Based on 12-month time scale standardized precipitation indices (SPI12) calculated from precipitation data recorded between 1960 and 2015 at 22 weather stations in the Tarim River Basin (TRB), this study aims to identify the trends of SPI and drought duration, severity, and frequency at various quantiles and to perform cluster analysis of drought events in the TRB. The results indicated that (1) both precipitation and temperature at most stations in the TRB exhibited significant positive trends during 1960-2015; (2) multiple scales of SPIs changed significantly around 1986; (3) based on quantile regression analysis of temporal drought changes, the positive SPI slopes indicated less severe and less frequent droughts at lower quantiles, but clear variation was detected in the drought frequency; and (4) significantly different trends were found in drought frequency probably between severe droughts and drought frequency.

Normative Feedback Effects on Learning a Timing Task

ERIC Educational Resources Information Center

Wulf, Gabriele; Chiviacowsky, Suzete; Lewthwaite, Rebecca

2010-01-01

This study investigated the influence of normative feedback on learning a sequential timing task. In addition to feedback about their performance per trial, two groups of participants received bogus normative feedback about a peer group's average block-to-block improvement after each block of 10 trials. Scores indicated either greater (better…

Preparation of Term Papers Based upon a Research-Process Model.

ERIC Educational Resources Information Center

Feldmann, Rodney Mansfield; Schloman, Barbara Frick

1990-01-01

Described is an alternative method of term paper preparation which provides a step-by-step sequence of assignments and provides feedback to the students at all stages in the preparation of the report. An example of this model is provided including 13 sequential assignments. (CW)

A quantile count model of water depth constraints on Cape Sable seaside sparrows

USGS Publications Warehouse

Cade, B.S.; Dong, Q.

2008-01-01

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992-2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (-50% to -83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5-10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.

Rank score and permutation testing alternatives for regression quantile estimates

USGS Publications Warehouse

Cade, B.S.; Richards, J.D.; Mielke, P.W.

2006-01-01

Performance of quantile rank score tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1) were evaluated by simulation for models with p = 2 and 6 predictors, moderate collinearity among predictors, homogeneous and hetero-geneous errors, small to moderate samples (n = 20–300), and central to upper quantiles (0.50–0.99). Test statistics evaluated were the conventional quantile rank score T statistic distributed as χ2 random variable with q degrees of freedom (where q parameters are constrained by H 0:) and an F statistic with its sampling distribution approximated by permutation. The permutation F-test maintained better Type I errors than the T-test for homogeneous error models with smaller n and more extreme quantiles τ. An F distributional approximation of the F statistic provided some improvements in Type I errors over the T-test for models with > 2 parameters, smaller n, and more extreme quantiles but not as much improvement as the permutation approximation. Both rank score tests required weighting to maintain correct Type I errors when heterogeneity under the alternative model increased to 5 standard deviations across the domain of X. A double permutation procedure was developed to provide valid Type I errors for the permutation F-test when null models were forced through the origin. Power was similar for conditions where both T- and F-tests maintained correct Type I errors but the F-test provided some power at smaller n and extreme quantiles when the T-test had no power because of excessively conservative Type I errors. When the double permutation scheme was required for the permutation F-test to maintain valid Type I errors, power was less than for the T-test with decreasing sample size and increasing quantiles. Confidence intervals on parameters and tolerance intervals for future predictions were constructed based on test inversion for an example application relating trout densities to stream channel width:depth.

Quantile regression via vector generalized additive models.

PubMed

Yee, Thomas W

2004-07-30

One of the most popular methods for quantile regression is the LMS method of Cole and Green. The method naturally falls within a penalized likelihood framework, and consequently allows for considerable flexible because all three parameters may be modelled by cubic smoothing splines. The model is also very understandable: for a given value of the covariate, the LMS method applies a Box-Cox transformation to the response in order to transform it to standard normality; to obtain the quantiles, an inverse Box-Cox transformation is applied to the quantiles of the standard normal distribution. The purposes of this article are three-fold. Firstly, LMS quantile regression is presented within the framework of the class of vector generalized additive models. This confers a number of advantages such as a unifying theory and estimation process. Secondly, a new LMS method based on the Yeo-Johnson transformation is proposed, which has the advantage that the response is not restricted to be positive. Lastly, this paper describes a software implementation of three LMS quantile regression methods in the S language. This includes the LMS-Yeo-Johnson method, which is estimated efficiently by a new numerical integration scheme. The LMS-Yeo-Johnson method is illustrated by way of a large cross-sectional data set from a New Zealand working population. Copyright 2004 John Wiley & Sons, Ltd.

Quantile uncertainty and value-at-risk model risk.

PubMed

Alexander, Carol; Sarabia, José María

2012-08-01

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks. © 2012 Society for Risk Analysis.

On the distortion of elevation dependent warming signals by quantile mapping

NASA Astrophysics Data System (ADS)

Jury, Martin W.; Mendlik, Thomas; Maraun, Douglas

2017-04-01

Elevation dependent warming (EDW), the amplification of warming under climate change with elevation, is likely to accelerate changes in e.g. cryospheric and hydrological systems. Responsible for EDW is a mixture of processes including snow albedo feedback, cloud formations or the location of aerosols. The degree of incorporation of this processes varies across state of the art climate models. In a recent study we were preparing bias corrected model output of CMIP5 GCMs and CORDEX RCMs over the Himalayan region for the glacier modelling community. In a first attempt we used quantile mapping (QM) to generate this data. A beforehand model evaluation showed that more than two third of the 49 included climate models were able to reproduce positive trend differences between areas of higher and lower elevations in winter, clearly visible in all of our five observational datasets used. Regrettably, we noticed that height dependent trend signals provided by models were distorted, most of the time in the direction of less EDW, sometimes even reversing EDW signals present in the models before the bias correction. As a consequence, we refrained from using quantile mapping for our task, as EDW poses one important factor influencing the climate in high altitudes for the nearer and more distant future, and used a climate change signal preserving bias correction approach. Here we present our findings of the distortion of the EDW temperature change by QM and discuss the influence of QM on different statistical properties as well as their modifications.

Numerical analysis of the accuracy of bivariate quantile distributions utilizing copulas compared to the GUM supplement 2 for oil pressure balance uncertainties

NASA Astrophysics Data System (ADS)

Ramnath, Vishal

2017-11-01

In the field of pressure metrology the effective area is Ae = A0 (1 + λP) where A0 is the zero-pressure area and λ is the distortion coefficient and the conventional practise is to construct univariate probability density functions (PDFs) for A0 and λ. As a result analytical generalized non-Gaussian bivariate joint PDFs has not featured prominently in pressure metrology. Recently extended lambda distribution based quantile functions have been successfully utilized for summarizing univariate arbitrary PDF distributions of gas pressure balances. Motivated by this development we investigate the feasibility and utility of extending and applying quantile functions to systems which naturally exhibit bivariate PDFs. Our approach is to utilize the GUM Supplement 1 methodology to solve and generate Monte Carlo based multivariate uncertainty data for an oil based pressure balance laboratory standard that is used to generate known high pressures, and which are in turn cross-floated against another pressure balance transfer standard in order to deduce the transfer standard's respective area. We then numerically analyse the uncertainty data by formulating and constructing an approximate bivariate quantile distribution that directly couples A0 and λ in order to compare and contrast its accuracy to an exact GUM Supplement 2 based uncertainty quantification analysis.

GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA

PubMed Central

Zheng, Qi; Peng, Limin; He, Xuming

2015-01-01

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal. PMID:26604424

Quantile regression analyses of associated factors for body mass index in Korean adolescents.

PubMed

Kim, T H; Lee, E K; Han, E

2015-05-01

This study examined the influence of home and school environments, and individual health-risk behaviours on body weight outcomes in Korean adolescents. This was a cross-sectional observational study. Quantile regression models to explore heterogeneity in the association of specific factors with body mass index (BMI) over the entire conditional BMI distribution was used. A nationally representative web-based survey for youths was used. Paternal education level of college or more education was associated with lower BMI for girls, whereas college or more education of mothers was associated with higher BMI for boys; for both, the magnitude of association became larger at the upper quantiles of the conditional BMI distribution. Girls with good family economic status were more likely to have higher BMIs than those with average family economic status, particularly at the upper quantile of the conditional BMI distribution. Attending a co-ed school was associated with lower BMI for both genders with a larger association at the upper quantiles. Substantial screen time for TV watching, video games, or internet surfing was associated with a higher BMI with a larger association at the upper quantiles for both girls and boys. Dental prevention was negatively associated with BMI, whereas suicide consideration was positively associated with BMIs of both genders with a larger association at a higher quantile. These findings suggest that interventions aimed at behavioural changes and positive parental roles are needed to effectively address high adolescent BMI. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

NASA Astrophysics Data System (ADS)

Sirenko, M. A.; Tarasenko, P. F.; Pushkarev, M. I.

2017-01-01

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

The effectiveness of drinking and driving policies for different alcohol-related fatalities: a quantile regression analysis.

PubMed

Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin

2013-09-27

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people's habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because "preemptive regulations" are more effective. For areas with high fatality rates (or high quantiles), "ex-post regulations" are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates.

Spatial quantile regression using INLA with applications to childhood overweight in Malawi.

PubMed

Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M

2015-04-01

Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Effectiveness of Drinking and Driving Policies for Different Alcohol-Related Fatalities: A Quantile Regression Analysis

PubMed Central

Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin

2013-01-01

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people’s habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because “preemptive regulations” are more effective. For areas with high fatality rates (or high quantiles), “ex-post regulations” are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates. PMID:24084673

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792

Beam shaping in high-power broad-area quantum cascade lasers using optical feedback

PubMed Central

Ferré, Simon; Jumpertz, Louise; Carras, Mathieu; Ferreira, Robson; Grillot, Frédéric

2017-01-01

Broad-area quantum cascade lasers with high output powers are highly desirable sources for various applications including infrared countermeasures. However, such structures suffer from strongly deteriorated beam quality due to multimode behavior, diffraction of light and self-focusing. Quantum cascade lasers presenting high performances in terms of power and heat-load dissipation are reported and their response to a nonlinear control based on optical feedback is studied. Applying optical feedback enables to efficiently tailor its near-field beam profile. The different cavity modes are sequentially excited by shifting the feedback mirror angle. Further control of the near-field profile is demonstrated using spatial filtering. The impact of an inhomogeneous gain as well as the influence of the cavity width are investigated. Compared to existing technologies, that are complex and costly, beam shaping with optical feedback is a more flexible solution to obtain high-quality mid-infrared sources. PMID:28287175

Beam shaping in high-power broad-area quantum cascade lasers using optical feedback.

PubMed

Ferré, Simon; Jumpertz, Louise; Carras, Mathieu; Ferreira, Robson; Grillot, Frédéric

2017-03-13

Broad-area quantum cascade lasers with high output powers are highly desirable sources for various applications including infrared countermeasures. However, such structures suffer from strongly deteriorated beam quality due to multimode behavior, diffraction of light and self-focusing. Quantum cascade lasers presenting high performances in terms of power and heat-load dissipation are reported and their response to a nonlinear control based on optical feedback is studied. Applying optical feedback enables to efficiently tailor its near-field beam profile. The different cavity modes are sequentially excited by shifting the feedback mirror angle. Further control of the near-field profile is demonstrated using spatial filtering. The impact of an inhomogeneous gain as well as the influence of the cavity width are investigated. Compared to existing technologies, that are complex and costly, beam shaping with optical feedback is a more flexible solution to obtain high-quality mid-infrared sources.

A technique for sequential segmental neuromuscular stimulation with closed loop feedback control.

PubMed

Zonnevijlle, Erik D H; Abadia, Gustavo Perez; Somia, Naveen N; Kon, Moshe; Barker, John H; Koenig, Steven; Ewert, D L; Stremel, Richard W

2002-01-01

In dynamic myoplasty, dysfunctional muscle is assisted or replaced with skeletal muscle from a donor site. Electrical stimulation is commonly used to train and animate the skeletal muscle to perform its new task. Due to simultaneous tetanic contractions of the entire myoplasty, muscles are deprived of perfusion and fatigue rapidly, causing long-term problems such as excessive scarring and muscle ischemia. Sequential stimulation contracts part of the muscle while other parts rest, thus significantly improving blood perfusion. However, the muscle still fatigues. In this article, we report a test of the feasibility of using closed-loop control to economize the contractions of the sequentially stimulated myoplasty. A simple stimulation algorithm was developed and tested on a sequentially stimulated neo-sphincter designed from a canine gracilis muscle. Pressure generated in the lumen of the myoplasty neo-sphincter was used as feedback to regulate the stimulation signal via three control parameters, thereby optimizing the performance of the myoplasty. Additionally, we investigated and compared the efficiency of amplitude and frequency modulation techniques. Closed-loop feedback enabled us to maintain target pressures within 10% deviation using amplitude modulation and optimized control parameters (correction frequency = 4 Hz, correction threshold = 4%, and transition time = 0.3 s). The large-scale stimulation/feedback setup was unfit for chronic experimentation, but can be used as a blueprint for a small-scale version to unveil the theoretical benefits of closed-loop control in chronic experimentation.

Modeling the human development index and the percentage of poor people using quantile smoothing splines

NASA Astrophysics Data System (ADS)

Mulyani, Sri; Andriyana, Yudhie; Sudartianto

2017-03-01

Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The parameter estimation in mean regression (with Ordinary Least Square or OLS) generates a problem if we apply it to the data with a symmetric, fat-tailed, or containing outlier. Hence, an alternative method is necessary to be used to that kind of data, for example quantile regression method. The quantile regression is a robust technique to the outlier. This model can explain the relationship between the response variable and the predictor variable, not only on the central tendency of the data (median) but also on various quantile, in order to obtain complete information about that relationship. In this study, a quantile regression is developed with a nonparametric approach such as smoothing spline. Nonparametric approach is used if the prespecification model is difficult to determine, the relation between two variables follow the unknown function. We will apply that proposed method to poverty data. Here, we want to estimate the Percentage of Poor People as the response variable involving the Human Development Index (HDI) as the predictor variable.

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

NASA Astrophysics Data System (ADS)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a univariate technique, and cannot incorporate information from additional covariates, for example ENSO state or physiographic controls on extreme rainfall within a region. Here, the univariate MQR model is extended to allow the use of multiple covariates. Multivariate monotone quantile regression (MMQR) is based on a single hidden-layer feedforward network with the quantile regression error function and partial monotonicity constraints. The MMQR model is demonstrated via Monte Carlo simulations and the estimation and visualization of regional trends in moderate rainfall extremes based on homogenized sub-daily precipitation data at stations in Canada.

Interquantile Shrinkage in Regression Models

PubMed Central

Jiang, Liewen; Wang, Huixia Judy; Bondell, Howard D.

2012-01-01

Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online. PMID:24363546

Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection

PubMed Central

Zheng, Qi; Peng, Limin

2016-01-01

Quantile regression provides a flexible platform for evaluating covariate effects on different segments of the conditional distribution of response. As the effects of covariates may change with quantile level, contemporaneously examining a spectrum of quantiles is expected to have a better capacity to identify variables with either partial or full effects on the response distribution, as compared to focusing on a single quantile. Under this motivation, we study a general adaptively weighted LASSO penalization strategy in the quantile regression setting, where a continuum of quantile index is considered and coefficients are allowed to vary with quantile index. We establish the oracle properties of the resulting estimator of coefficient function. Furthermore, we formally investigate a BIC-type uniform tuning parameter selector and show that it can ensure consistent model selection. Our numerical studies confirm the theoretical findings and illustrate an application of the new variable selection procedure. PMID:28008212

Toward a Better Understanding of Student Perceptions of Writing Feedback: A Mixed Methods Study

ERIC Educational Resources Information Center

Zumbrunn, Sharon; Marrs, Sarah; Mewborn, Caitlin

2016-01-01

This explanatory sequential mixed methods study investigated the writing feedback perceptions of middle and high school students (N = 598). The predictive and mediational roles of writing self-efficacy and perceptions of writing feedback on student writing self-regulation aptitude were examined using mediation regression analysis. To augment the…

Information Retrieval: A Sequential Learning Process.

ERIC Educational Resources Information Center

Bookstein, Abraham

1983-01-01

Presents decision-theoretic models which intrinsically include retrieval of multiple documents whereby system responds to request by presenting documents to patron in sequence, gathering feedback, and using information to modify future retrievals. Document independence model, set retrieval model, sequential retrieval model, learning model,…

Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois

USGS Publications Warehouse

Over, Thomas M.; Saito, Riki J.; Veilleux, Andrea G.; Sharpe, Jennifer B.; Soong, David T.; Ishii, Audrey L.

2016-06-28

This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions.The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter.This report also provides the following: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant.The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted annual maximum peak discharge records by streamgage are provided at https://doi.org/10.3133/sir20165050 for download.

Use of Flood Seasonality in Pooling-Group Formation and Quantile Estimation: An Application in Great Britain

NASA Astrophysics Data System (ADS)

Formetta, Giuseppe; Bell, Victoria; Stewart, Elizabeth

2018-02-01

Regional flood frequency analysis is one of the most commonly applied methods for estimating extreme flood events at ungauged sites or locations with short measurement records. It is based on: (i) the definition of a homogeneous group (pooling-group) of catchments, and on (ii) the use of the pooling-group data to estimate flood quantiles. Although many methods to define a pooling-group (pooling schemes, PS) are based on catchment physiographic similarity measures, in the last decade methods based on flood seasonality similarity have been contemplated. In this paper, two seasonality-based PS are proposed and tested both in terms of the homogeneity of the pooling-groups they generate and in terms of the accuracy in estimating extreme flood events. The method has been applied in 420 catchments in Great Britain (considered as both gauged and ungauged) and compared against the current Flood Estimation Handbook (FEH) PS. Results for gauged sites show that, compared to the current PS, the seasonality-based PS performs better both in terms of homogeneity of the pooling-group and in terms of the accuracy of flood quantile estimates. For ungauged locations, a national-scale hydrological model has been used for the first time to quantify flood seasonality. Results show that in 75% of the tested locations the seasonality-based PS provides an improvement in the accuracy of the flood quantile estimates. The remaining 25% were located in highly urbanized, groundwater-dependent catchments. The promising results support the aspiration that large-scale hydrological models complement traditional methods for estimating design floods.

Bias correction of daily satellite precipitation data using genetic algorithm

NASA Astrophysics Data System (ADS)

Pratama, A. W.; Buono, A.; Hidayat, R.; Harsa, H.

2018-05-01

Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was producted by blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

Distributional changes in rainfall and river flow in Sarawak, Malaysia

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

Climate change may not change the rainfall mean, but the variability and extremes. Therefore, it is required to explore the possible distributional changes of rainfall characteristics over time. The objective of present study is to assess the distributional changes in annual and northeast monsoon rainfall (November-January) and river flow in Sarawak where small changes in rainfall or river flow variability/distribution may have severe implications on ecology and agriculture. A quantile regression-based approach was used to assess the changes of scale and location of empirical probability density function over the period 1980-2014 at 31 observational stations. The results indicate that diverse variation patterns exist at all stations for annual rainfall but mainly increasing quantile trend at the lowers, and higher quantiles for the month of January and December. The significant increase in annual rainfall is found mostly in the north and central-coastal region and monsoon month rainfalls in the interior and north of Sarawak. Trends in river flow data show that changes in rainfall distribution have affected higher quantiles of river flow in monsoon months at some of the basins and therefore more flooding. The study reveals that quantile trend can provide more information of rainfall change which may be useful for climate change mitigation and adaptation planning.

Parameter Heterogeneity In Breast Cancer Cost Regressions – Evidence From Five European Countries

PubMed Central

Banks, Helen; Campbell, Harry; Douglas, Anne; Fletcher, Eilidh; McCallum, Alison; Moger, Tron Anders; Peltola, Mikko; Sveréus, Sofia; Wild, Sarah; Williams, Linda J.; Forbes, John

2015-01-01

Abstract We investigate parameter heterogeneity in breast cancer 1‐year cumulative hospital costs across five European countries as part of the EuroHOPE project. The paper aims to explore whether conditional mean effects provide a suitable representation of the national variation in hospital costs. A cohort of patients with a primary diagnosis of invasive breast cancer (ICD‐9 codes 174 and ICD‐10 C50 codes) is derived using routinely collected individual breast cancer data from Finland, the metropolitan area of Turin (Italy), Norway, Scotland and Sweden. Conditional mean effects are estimated by ordinary least squares for each country, and quantile regressions are used to explore heterogeneity across the conditional quantile distribution. Point estimates based on conditional mean effects provide a good approximation of treatment response for some key demographic and diagnostic specific variables (e.g. age and ICD‐10 diagnosis) across the conditional quantile distribution. For many policy variables of interest, however, there is considerable evidence of parameter heterogeneity that is concealed if decisions are based solely on conditional mean results. The use of quantile regression methods reinforce the need to consider beyond an average effect given the greater recognition that breast cancer is a complex disease reflecting patient heterogeneity. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd. PMID:26633866

Bayesian quantitative precipitation forecasts in terms of quantiles

NASA Astrophysics Data System (ADS)

Bentzien, Sabrina; Friederichs, Petra

2014-05-01

Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into reliability, resolutions and uncertainty parts. A quantile reliability plot gives detailed insights in the predictive performance of the quantile forecasts.

Distributed Wireless Power Transfer With Energy Feedback

NASA Astrophysics Data System (ADS)

Lee, Seunghyun; Zhang, Rui

2017-04-01

Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they can be constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of EB waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain practically, especially in a distributed system with ETs at separate locations. In this paper, we study practical and efficient channel training methods to achieve optimal EB in a distributed WET system. We propose two protocols with and without centralized coordination, respectively, where distributed ETs either sequentially or in parallel adapt their transmit phases based on a low-complexity energy feedback from the ER. The energy feedback only depends on the received power level at the ER, where each feedback indicates one particular transmit phase that results in the maximum harvested power over a set of previously used phases. Simulation results show that the two proposed training protocols converge very fast in practical WET systems even with a large number of distributed ETs, while the protocol with sequential ET phase adaptation is also analytically shown to converge to the optimal EB design with perfect CSI by increasing the training time. Numerical results are also provided to evaluate the performance of the proposed distributed EB and training designs as compared to other benchmark schemes.

Ventromedial prefrontal and anterior cingulate cortex adopt choice and default reference frames during sequential multialternative choice

PubMed Central

Boorman, Erie D; Rushworth, Matthew F; Behrens, Tim E

2013-01-01

Although damage to medial frontal cortex causes profound decision-making impairments, it has been difficult to pinpoint the relative contributions of key anatomical subdivisions. Here we use fMRI to examine the contributions of human ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate cortex (dACC) during sequential choices between multiple alternatives – two key features of choices made in ecological settings. By carefully constructing options whose current value at any given decision was dissociable from their longer-term value, we were able to examine choices in current and long-term frames of reference. We present evidence showing that activity at choice and feedback in vmPFC and dACC was tied to the current choice and the best long-term option, respectively. vmPFC, mid-cingulate, and PCC encoded the relative value between the chosen and next-best option at each sequential decision, whereas dACC encoded the relative value of adapting choices from the option with the highest value in the longer-term. Furthermore, at feedback we identify temporally dissociable effects that predict repetition of the current choice and adaptation away from the long-term best option in vmPFC and dACC, respectively. These functional dissociations at choice and feedback suggest that sequential choices are subject to competing cortical mechanisms. PMID:23392656

Association Between Awareness of Hypertension and Health-Related Quality of Life in a Cross-Sectional Population-Based Study in Rural Area of Northwest China.

PubMed

Mi, Baibing; Dang, Shaonong; Li, Qiang; Zhao, Yaling; Yang, Ruihai; Wang, Duolao; Yan, Hong

2015-07-01

Hypertensive patients have more complex health care needs and are more likely to have poorer health-related quality of life than normotensive people. The awareness of hypertension could be related to reduce health-related quality of life. We propose the use of quantile regression to explore more detailed relationships between awareness of hypertension and health-related quality of life. In a cross-sectional, population-based study, 2737 participants (including 1035 hypertensive patients and 1702 normotensive participants) completed the Short-Form Health Survey. A quantile regression model was employed to investigate the association of physical component summary scores and mental component summary scores with awareness of hypertension and to evaluate the associated factors. Patients who were aware of hypertension (N = 554) had lower scores than patients who were unaware of hypertension (N = 481). The median (IQR) of physical component summary scores: 48.20 (13.88) versus 53.27 (10.79), P < 0.01; the mental component summary scores: 50.68 (15.09) versus 51.70 (10.65), P = 0.03. adjusting for covariates, the quantile regression results suggest awareness of hypertension was associated with most physical component summary scores quantiles (P < 0.05 except 10th and 20th quantiles) in which the β-estimates from -2.14 (95% CI: -3.80 to -0.48) to -1.45 (95% CI: -2.42 to -0.47), as the same significant trend with some poorer mental component summary scores quantiles in which the β-estimates from -3.47 (95% CI: -6.65 to -0.39) to -2.18 (95% CI: -4.30 to -0.06). The awareness of hypertension has a greater effect on those with intermediate physical component summary status: the β-estimates were equal to -2.04 (95% CI: -3.51 to -0.57, P < 0.05) at the 40th and decreased further to -1.45 (95% CI: -2.42 to -0.47, P < 0.01) at the 90th quantile. Awareness of hypertension was negatively related to health-related quality of life in hypertensive patients in rural western China, which has a greater effect on mental component summary scores with the poorer status and on physical component summary scores with the intermediate status.

Regionalisation of a distributed method for flood quantiles estimation: Revaluation of local calibration hypothesis to enhance the spatial structure of the optimised parameter

NASA Astrophysics Data System (ADS)

Odry, Jean; Arnaud, Patrick

2016-04-01

The SHYREG method (Aubert et al., 2014) associates a stochastic rainfall generator and a rainfall-runoff model to produce rainfall and flood quantiles on a 1 km2 mesh covering the whole French territory. The rainfall generator is based on the description of rainy events by descriptive variables following probability distributions and is characterised by a high stability. This stochastic generator is fully regionalised, and the rainfall-runoff transformation is calibrated with a single parameter. Thanks to the stability of the approach, calibration can be performed against only flood quantiles associated with observated frequencies which can be extracted from relatively short time series. The aggregation of SHYREG flood quantiles to the catchment scale is performed using an areal reduction factor technique unique on the whole territory. Past studies demonstrated the accuracy of SHYREG flood quantiles estimation for catchments where flow data are available (Arnaud et al., 2015). Nevertheless, the parameter of the rainfall-runoff model is independently calibrated for each target catchment. As a consequence, this parameter plays a corrective role and compensates approximations and modelling errors which makes difficult to identify its proper spatial pattern. It is an inherent objective of the SHYREG approach to be completely regionalised in order to provide a complete and accurate flood quantiles database throughout France. Consequently, it appears necessary to identify the model configuration in which the calibrated parameter could be regionalised with acceptable performances. The revaluation of some of the method hypothesis is a necessary step before the regionalisation. Especially the inclusion or the modification of the spatial variability of imposed parameters (like production and transfer reservoir size, base flow addition and quantiles aggregation function) should lead to more realistic values of the only calibrated parameter. The objective of the work presented here is to develop a SHYREG evaluation scheme focusing on both local and regional performances. Indeed, it is necessary to maintain the accuracy of at site flood quantiles estimation while identifying a configuration leading to a satisfactory spatial pattern of the calibrated parameter. This ability to be regionalised can be appraised by the association of common regionalisation techniques and split sample validation tests on a set of around 1,500 catchments representing the whole diversity of France physiography. Also, the presence of many nested catchments and a size-based split sample validation make possible to assess the relevance of the calibrated parameter spatial structure inside the largest catchments. The application of this multi-objective evaluation leads to the selection of a version of SHYREG more suitable for regionalisation. References: Arnaud, P., Cantet, P., Aubert, Y., 2015. Relevance of an at-site flood frequency analysis method for extreme events based on stochastic simulation of hourly rainfall. Hydrological Sciences Journal: on press. DOI:10.1080/02626667.2014.965174 Aubert, Y., Arnaud, P., Ribstein, P., Fine, J.A., 2014. The SHYREG flow method-application to 1605 basins in metropolitan France. Hydrological Sciences Journal, 59(5): 993-1005. DOI:10.1080/02626667.2014.902061

Effects of a web-based tailored multiple-lifestyle intervention for adults: a two-year randomized controlled trial comparing sequential and simultaneous delivery modes.

PubMed

Schulz, Daniela N; Kremers, Stef P J; Vandelanotte, Corneel; van Adrichem, Mathieu J G; Schneider, Francine; Candel, Math J J M; de Vries, Hein

2014-01-27

Web-based computer-tailored interventions for multiple health behaviors can have a significant public health impact. Yet, few randomized controlled trials have tested this assumption. The objective of this paper was to test the effects of a sequential and simultaneous Web-based tailored intervention on multiple lifestyle behaviors. A randomized controlled trial was conducted with 3 tailoring conditions (ie, sequential, simultaneous, and control conditions) in the Netherlands in 2009-2012. Follow-up measurements took place after 12 and 24 months. The intervention content was based on the I-Change model. In a health risk appraisal, all respondents (N=5055) received feedback on their lifestyle behaviors that indicated whether they complied with the Dutch guidelines for physical activity, vegetable consumption, fruit consumption, alcohol intake, and smoking. Participants in the sequential (n=1736) and simultaneous (n=1638) conditions received tailored motivational feedback to change unhealthy behaviors one at a time (sequential) or all at the same time (simultaneous). Mixed model analyses were performed as primary analyses; regression analyses were done as sensitivity analyses. An overall risk score was used as outcome measure, then effects on the 5 individual lifestyle behaviors were assessed and a process evaluation was performed regarding exposure to and appreciation of the intervention. Both tailoring strategies were associated with small self-reported behavioral changes. The sequential condition had the most significant effects compared to the control condition after 12 months (T1, effect size=0.28). After 24 months (T2), the simultaneous condition was most effective (effect size=0.18). All 5 individual lifestyle behaviors changed over time, but few effects differed significantly between the conditions. At both follow-ups, the sequential condition had significant changes in smoking abstinence compared to the simultaneous condition (T1 effect size=0.31; T2 effect size=0.41). The sequential condition was more effective in decreasing alcohol consumption than the control condition at 24 months (effect size=0.27). Change was predicted by the amount of exposure to the intervention (total visiting time: beta=-.06; P=.01; total number of visits: beta=-.11; P<.001). Both interventions were appreciated well by respondents without significant differences between conditions. Although evidence was found for the effectiveness of both programs, no simple conclusive finding could be drawn about which intervention mode was more effective. The best kind of intervention may depend on the behavior that is targeted or on personal preferences and motivation. Further research is needed to identify moderators of intervention effectiveness. The results need to be interpreted in view of the high and selective dropout rates, multiple comparisons, and modest effect sizes. However, a large number of people were reached at low cost and behavioral change was achieved after 2 years. Nederlands Trial Register: NTR 2168; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=2168 (Archived by WebCite at http://www.webcitation.org/6MbUqttYB).

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

PubMed

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Proceedings of the NATO IST-128 Workshop: Assessing Mission Impact of Cyberattacks Held in Istanbul, Turkey on 15-17 June 2015

DTIC Science & Technology

2015-12-01

combine satisficing behaviour with learning and adaptation through environmental feedback. This a sequential decision making with one alternative...next action that an opponent will most likely take in a strategic interaction. Also, cognitive models derived from instance- based learning theory (IBL... through instance- based learning . In Y. Li (Ed.), Lecture Notes in Computer Science (Vol. 6818, pp. 281-293). Heidelberg: Springer Berlin. Gonzalez, C

Context based configuration management system

NASA Technical Reports Server (NTRS)

Gurram, Mohana M. (Inventor); Maluf, David A. (Inventor); Mederos, Luis A. (Inventor); Gawdiak, Yuri O. (Inventor)

2010-01-01

A computer-based system for configuring and displaying information on changes in, and present status of, a collection of events associated with a project. Classes of icons for decision events, configurations and feedback mechanisms, and time lines (sequential and/or simultaneous) for related events are displayed. Metadata for each icon in each class is displayed by choosing and activating the corresponding icon. Access control (viewing, reading, writing, editing, deleting, etc.) is optionally imposed for metadata and other displayed information.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.

2013-01-01

Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Using cross-sectional data for children aged 0-24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role.

An impact of environmental changes on flows in the reach scale under a range of climatic conditions

NASA Astrophysics Data System (ADS)

Karamuz, Emilia; Romanowicz, Renata J.

2016-04-01

The present paper combines detection and adequate identification of causes of changes in flow regime at cross-sections along the Middle River Vistula reach using different methods. Two main experimental set ups (designs) have been applied to study the changes, a moving three-year window and low- and high-flow event based approach. In the first experiment, a Stochastic Transfer Function (STF) model and a quantile-based statistical analysis of flow patterns were compared. These two methods are based on the analysis of changes of the STF model parameters and standardised differences of flow quantile values. In the second experiment, in addition to the STF-based also a 1-D distributed model, MIKE11 was applied. The first step of the procedure used in the study is to define the river reaches that have recorded information on land use and water management changes. The second task is to perform the moving window analysis of standardised differences of flow quantiles and moving window optimisation of the STF model for flow routing. The third step consists of an optimisation of the STF and MIKE11 models for high- and low-flow events. The final step is to analyse the results and relate the standardised quantile changes and model parameter changes to historical land use changes and water management practices. Results indicate that both models give consistent assessment of changes in the channel for medium and high flows. ACKNOWLEDGEMENTS This research was supported by the Institute of Geophysics Polish Academy of Sciences through the Young Scientist Grant no. 3b/IGF PAN/2015.

Estimating equivalence with quantile regression

USGS Publications Warehouse

Cade, B.S.

2011-01-01

Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.

Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era

NASA Technical Reports Server (NTRS)

Stanley, Thomas; Kirschbaum, Dalia B.; Huffman, George J.; Adler, Robert F.

2017-01-01

Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMMs successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics, but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.

A Study on Regional Rainfall Frequency Analysis for Flood Simulation Scenarios

NASA Astrophysics Data System (ADS)

Jung, Younghun; Ahn, Hyunjun; Joo, Kyungwon; Heo, Jun-Haeng

2014-05-01

Recently, climate change has been observed in Korea as well as in the entire world. The rainstorm has been gradually increased and then the damage has been grown. It is very important to manage the flood control facilities because of increasing the frequency and magnitude of severe rain storm. For managing flood control facilities in risky regions, data sets such as elevation, gradient, channel, land use and soil data should be filed up. Using this information, the disaster situations can be simulated to secure evacuation routes for various rainfall scenarios. The aim of this study is to investigate and determine extreme rainfall quantile estimates in Uijeongbu City using index flood method with L-moments parameter estimation. Regional frequency analysis trades space for time by using annual maximum rainfall data from nearby or similar sites to derive estimates for any given site in a homogeneous region. Regional frequency analysis based on pooled data is recommended for estimation of rainfall quantiles at sites with record lengths less than 5T, where T is return period of interest. Many variables relevant to precipitation can be used for grouping a region in regional frequency analysis. For regionalization of Han River basin, the k-means method is applied for grouping regions by variables of meteorology and geomorphology. The results from the k-means method are compared for each region using various probability distributions. In the final step of the regionalization analysis, goodness-of-fit measure is used to evaluate the accuracy of a set of candidate distributions. And rainfall quantiles by index flood method are obtained based on the appropriate distribution. And then, rainfall quantiles based on various scenarios are used as input data for disaster simulations. Keywords: Regional Frequency Analysis; Scenarios of Rainfall Quantile Acknowledgements This research was supported by a grant 'Establishing Active Disaster Management System of Flood Control Structures by using 3D BIM Technique' [NEMA-12-NH-57] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of Korea.

Ensuring the consistancy of Flow Direction Curve reconstructions: the 'quantile solidarity' approach

NASA Astrophysics Data System (ADS)

Poncelet, Carine; Andreassian, Vazken; Oudin, Ludovic

2015-04-01

Flow Duration Curves (FDCs) are a hydrologic tool describing the distribution of streamflows at a catchment outlet. FDCs are usually used for calibration of hydrological models, managing water quality and classifying catchments, among others. For gauged catchments, empirical FDCs can be computed from streamflow records. For ungauged catchments, on the other hand, FDCs cannot be obtained from streamflow records and must therefore be obtained in another manner, for example through reconstructions. Regression-based reconstructions are methods relying on the evaluation of quantiles separately from catchments' attributes (climatic or physical features).The advantage of this category of methods is that it is informative about the processes and it is non-parametric. However, the large number of parameters required can cause unwanted artifacts, typically reconstructions that do not always produce increasing quantiles. In this paper we propose a new approach named Quantile Solidarity (QS), which is applied under strict proxy-basin test conditions (Klemes, 1986) to a set of 600 French catchments. Half of the catchments are considered as gauged and used to calibrate the regression and compute residuals of the regression. The QS approach consists in a three-step regionalization scheme, which first links quantile values to physical descriptors, then reduces the number of regression parameters and finally exploits the spatial correlation of the residuals. The innovation is the utilisation of the parameters continuity across the quantiles to dramatically reduce the number of parameters. The second half of catchment is used as an independent validation set over which we show that the QS approach ensures strictly growing FDC reconstructions in ungauged conditions. Reference: V. KLEMEŠ (1986) Operational testing of hydrological simulation models, Hydrological Sciences Journal, 31:1, 13-24

Identifying Factors That Predict Promotion Time to E-4 and Re-Enlistment Eligibility for U.S. Marine Corps Field Radio Operators

DTIC Science & Technology

2014-12-01

Primary Military Occupational Specialty PRO Proficiency Q-Q Quantile - Quantile RSS Residual Sum of Squares SI Shop Information T&R Training and...construct multivariate linear regression models to estimate Marines’ Computed Tier Score and time to achieve E-4 based on their individual personal...Science (GS) score, ASVAB Mathematics Knowledge (MK) score, ASVAB Paragraph Comprehension (PC) score, weight , and whether a Marine receives a weight

Robust small area estimation of poverty indicators using M-quantile approach (Case study: Sub-district level in Bogor district)

NASA Astrophysics Data System (ADS)

Girinoto, Sadik, Kusman; Indahwati

2017-03-01

The National Socio-Economic Survey samples are designed to produce estimates of parameters of planned domains (provinces and districts). The estimation of unplanned domains (sub-districts and villages) has its limitation to obtain reliable direct estimates. One of the possible solutions to overcome this problem is employing small area estimation techniques. The popular choice of small area estimation is based on linear mixed models. However, such models need strong distributional assumptions and do not easy allow for outlier-robust estimation. As an alternative approach for this purpose, M-quantile regression approach to small area estimation based on modeling specific M-quantile coefficients of conditional distribution of study variable given auxiliary covariates. It obtained outlier-robust estimation from influence function of M-estimator type and also no need strong distributional assumptions. In this paper, the aim of study is to estimate the poverty indicator at sub-district level in Bogor District-West Java using M-quantile models for small area estimation. Using data taken from National Socioeconomic Survey and Villages Potential Statistics, the results provide a detailed description of pattern of incidence and intensity of poverty within Bogor district. We also compare the results with direct estimates. The results showed the framework may be preferable when direct estimate having no incidence of poverty at all in the small area.

The evolution of El Niño through the Pliocene

NASA Astrophysics Data System (ADS)

White, S. M.; Ravelo, A. C.

2017-12-01

ENSO is an important source of variability in the hydrological cycle, but its dependence on mean SSTs, thermocline depth, and other aspects of mean climate state remains unclear. The Pliocene (2.6-5.3 Ma) is an excellent test case because the tropical Pacific was markedly different than today, with much warmer SSTs in the eastern equatorial Pacific, a much lower east-west temperature difference, and a deeper thermocline across the entire basin [e.g. Ford et al., 2015; Lawrence et al., 2006; Wara et al., 2005]. This would be expected to weaken the Bjerknes and thermocline feedbacks, thus strongly dampening ENSO. However, paleoclimate data from the Pliocene show ENSO-like variability [Scroxton et al., 2011; Watanabe et al., 2011; Weiss et al., 2017], implying that opposing feedbacks may have counteracted changes in the Bjerknes and thermocline feedbacks, as suggested by Manucharyan and Fedorov [2014]. As of yet, however, reconstructions of Pliocene ENSO are too sparse to confidently ascribe changes in ENSO to changes in mean state parameters such as SST and thermocline depth. To generate a record of SST variability spanning the Pliocene, we analyzed individual planktonic foraminifera for Mg/Ca, yielding a distribution of temperatures representing monthly SST variability from each selected time interval. We used marine sediments from ODP 849 in the eastern equatorial Pacific, in ENSO's center of action. To compare Pliocene temperature distributions to those of the late Holocene, we use quantile-quantile plots. Differences in the warm "tail" of the distribution are attributable to changes in El Niño. We find that at 5.5 Ma, possibly before closure of the Panamanian seaway, the amplitude of El Niño events appears similar to those of the late Holocene, in agreement with contemporaneous coral data [Weiss et al., 2017]. During most of the Pliocene, El Niño was dampened, and strengthened to its present amplitude by 3.1 Ma, in concert with a long-term shoaling of the thermocline and development of the eastern Pacific cold tongue.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Funnel Libraries for Real-Time Robust Feedback Motion Planning

DTIC Science & Technology

2016-07-21

motion plans for a robot that are guaranteed to suc- ceed despite uncertainty in the environment, parametric model uncertainty, and disturbances...resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot . A major advantage of...the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable

Impact of body mass on job quality.

PubMed

Kim, Tae Hyun; Han, Euna

2015-04-01

The current study explores the association between body mass and job quality, a composite measurement of job characteristics, for adults. We use nationally representative data from the Korean Labor and Income Panel Study for the years 2005, 2007, and 2008 with 7282 person-year observations for men and 4611 for women. A Quality of Work Index (QWI) is calculated based on work content, job security, the possibilities for improvement, compensation, work conditions, and interpersonal relationships at work. The key independent variable is the body mass index (kg/m(2)) splined at 18.5, 25, and 30. For men, BMI is positively associated with the QWI only in the normal weight segment (+0.19 percentage points at the 10th, +0.28 at the 50th, +0.32 at the 75th, +0.34 at the 90th, and +0.48 at the 95th quantiles). A unit increase in the BMI for women is associated with a lower QWI at the lower quantiles in the normal weight segment (-0.28 at the 5th, -0.19 at the 10th, and -0.25 percentage points at the 25th quantiles) and at the upper quantiles in the overweight segment (-1.15 at the 90th and -1.66 percentage points at the 95th quantiles). The results imply a spill-over cost of overweight or obesity beyond its impact on health in terms of success in the labor market. Copyright © 2015 Elsevier B.V. All rights reserved.

Measuring racial/ethnic disparities across the distribution of health care expenditures.

PubMed

Cook, Benjamin Lê; Manning, Willard G

2009-10-01

To assess whether black-white and Hispanic-white disparities increase or abate in the upper quantiles of total health care expenditure, conditional on covariates. Nationally representative adult population of non-Hispanic whites, African Americans, and Hispanics from the 2001-2005 Medical Expenditure Panel Surveys. We examine unadjusted racial/ethnic differences across the distribution of expenditures. We apply quantile regression to measure disparities at the median, 75th, 90th, and 95th quantiles, testing for differences over the distribution of health care expenditures and across income and education categories. We test the sensitivity of the results to comparisons based only on health status and estimate a two-part model to ensure that results are not driven by an extremely skewed distribution of expenditures with a large zero mass. Black-white and Hispanic-white disparities diminish in the upper quantiles of expenditure, but expenditures for blacks and Hispanics remain significantly lower than for whites throughout the distribution. For most education and income categories, disparities exist at the median and decline, but remain significant even with increased education and income. Blacks and Hispanics receive significantly disparate care at high expenditure levels, suggesting prioritization of improved access to quality care among minorities with critical health issues.

Quantile Regression for Recurrent Gap Time Data

PubMed Central

Luo, Xianghua; Huang, Chiung-Yu; Wang, Lan

2014-01-01

Summary Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this article, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301–311), we extend the martingale-based estimating equation method considered by Peng and Huang (2008, Journal of the American Statistical Association 103, 637–649) for univariate survival data to analyze recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented to illustrate the methods developed in this article. PMID:23489055

The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations.

PubMed

Liu, Chunping; Laporte, Audrey; Ferguson, Brian S

2008-09-01

In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital - or nursing home - up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that, depending on the quantile estimated, the quantile regression approach may be a useful addition to the armamentarium of methods for estimating technical efficiency.

Confidence intervals for expected moments algorithm flood quantile estimates

USGS Publications Warehouse

Cohn, Timothy A.; Lane, William L.; Stedinger, Jery R.

2001-01-01

Historical and paleoflood information can substantially improve flood frequency estimates if appropriate statistical procedures are properly applied. However, the Federal guidelines for flood frequency analysis, set forth in Bulletin 17B, rely on an inefficient “weighting” procedure that fails to take advantage of historical and paleoflood information. This has led researchers to propose several more efficient alternatives including the Expected Moments Algorithm (EMA), which is attractive because it retains Bulletin 17B's statistical structure (method of moments with the Log Pearson Type 3 distribution) and thus can be easily integrated into flood analyses employing the rest of the Bulletin 17B approach. The practical utility of EMA, however, has been limited because no closed‐form method has been available for quantifying the uncertainty of EMA‐based flood quantile estimates. This paper addresses that concern by providing analytical expressions for the asymptotic variance of EMA flood‐quantile estimators and confidence intervals for flood quantile estimates. Monte Carlo simulations demonstrate the properties of such confidence intervals for sites where a 25‐ to 100‐year streamgage record is augmented by 50 to 150 years of historical information. The experiments show that the confidence intervals, though not exact, should be acceptable for most purposes.

Neural Correlates of Sequence Learning with Stochastic Feedback

ERIC Educational Resources Information Center

Averbeck, Bruno B.; Kilner, James; Frith, Christopher D.

2011-01-01

Although much is known about decision making under uncertainty when only a single step is required in the decision process, less is known about sequential decision making. We carried out a stochastic sequence learning task in which subjects had to use noisy feedback to learn sequences of button presses. We compared flat and hierarchical behavioral…

Effects of a Web-Based Tailored Multiple-Lifestyle Intervention for Adults: A Two-Year Randomized Controlled Trial Comparing Sequential and Simultaneous Delivery Modes

PubMed Central

Kremers, Stef PJ; Vandelanotte, Corneel; van Adrichem, Mathieu JG; Schneider, Francine; Candel, Math JJM; de Vries, Hein

2014-01-01

Background Web-based computer-tailored interventions for multiple health behaviors can have a significant public health impact. Yet, few randomized controlled trials have tested this assumption. Objective The objective of this paper was to test the effects of a sequential and simultaneous Web-based tailored intervention on multiple lifestyle behaviors. Methods A randomized controlled trial was conducted with 3 tailoring conditions (ie, sequential, simultaneous, and control conditions) in the Netherlands in 2009-2012. Follow-up measurements took place after 12 and 24 months. The intervention content was based on the I-Change model. In a health risk appraisal, all respondents (N=5055) received feedback on their lifestyle behaviors that indicated whether they complied with the Dutch guidelines for physical activity, vegetable consumption, fruit consumption, alcohol intake, and smoking. Participants in the sequential (n=1736) and simultaneous (n=1638) conditions received tailored motivational feedback to change unhealthy behaviors one at a time (sequential) or all at the same time (simultaneous). Mixed model analyses were performed as primary analyses; regression analyses were done as sensitivity analyses. An overall risk score was used as outcome measure, then effects on the 5 individual lifestyle behaviors were assessed and a process evaluation was performed regarding exposure to and appreciation of the intervention. Results Both tailoring strategies were associated with small self-reported behavioral changes. The sequential condition had the most significant effects compared to the control condition after 12 months (T1, effect size=0.28). After 24 months (T2), the simultaneous condition was most effective (effect size=0.18). All 5 individual lifestyle behaviors changed over time, but few effects differed significantly between the conditions. At both follow-ups, the sequential condition had significant changes in smoking abstinence compared to the simultaneous condition (T1 effect size=0.31; T2 effect size=0.41). The sequential condition was more effective in decreasing alcohol consumption than the control condition at 24 months (effect size=0.27). Change was predicted by the amount of exposure to the intervention (total visiting time: beta=–.06; P=.01; total number of visits: beta=–.11; P<.001). Both interventions were appreciated well by respondents without significant differences between conditions. Conclusions Although evidence was found for the effectiveness of both programs, no simple conclusive finding could be drawn about which intervention mode was more effective. The best kind of intervention may depend on the behavior that is targeted or on personal preferences and motivation. Further research is needed to identify moderators of intervention effectiveness. The results need to be interpreted in view of the high and selective dropout rates, multiple comparisons, and modest effect sizes. However, a large number of people were reached at low cost and behavioral change was achieved after 2 years. Trial Registration Nederlands Trial Register: NTR 2168; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=2168 (Archived by WebCite at http://www.webcitation.org/6MbUqttYB). PMID:24472854

Calibration of limited-area ensemble precipitation forecasts for hydrological predictions

NASA Astrophysics Data System (ADS)

Diomede, Tommaso; Marsigli, Chiara; Montani, Andrea; Nerozzi, Fabrizio; Paccagnella, Tiziana

2015-04-01

The main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003-2007 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The analog-based method seemed to be preferred because of its capability of correct position errors and spread deficiencies. A suitable spatial domain for the analog search can help to handle model spatial errors as systematic errors. However, the performance of the analog-based method may degrade in cases where a limited training dataset is available. A sensitivity test on the length of the training dataset over which to perform the analog search has been performed. The quantile-to-quantile mapping and linear regression methods were less effective, mainly because the forecast-analysis relation was not so strong for the available training dataset. A comparison between the calibration based on the deterministic reforecast and the calibration based on the full operational ensemble used as training dataset has been considered, with the aim to evaluate whether reforecasts are really worthy for calibration, given that their computational cost is remarkable. The verification of the calibration process was then performed by coupling ensemble precipitation forecasts with a distributed rainfall-runoff model. This test was carried out for a medium-sized catchment located in Emilia-Romagna, showing a beneficial impact of the analog-based method on the reduction of missed events for discharge predictions.

Modeling energy expenditure in children and adolescents using quantile regression

USDA-ARS?s Scientific Manuscript database

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obes...

Evaluating the parent-adolescent communication toolkit: Usability and preliminary content effectiveness of an online intervention.

PubMed

Toombs, Elaine; Unruh, Anita; McGrath, Patrick

2018-01-01

This study aimed to assess the Parent-Adolescent Communication Toolkit, an online intervention designed to help improve parent communication with their adolescents. Participant preferences for two module delivery systems (sequential and unrestricted module access) were identified. Usability assessment of the PACT intervention was completed using pre-test and posttest comparisons. Usability data, including participant completion and satisfaction ratings were examined. Parents ( N  =   18) of adolescents were randomized to a sequential or unrestricted chapter access group. Parent participants completed pre-test measures, the PACT intervention and posttest measures. Participants provided feedback for the intervention to improve modules and provided usability ratings. Adolescent pre- and posttest ratings were evaluated. Usability ratings were high and parent feedback was positive. The sequential module access groups rated the intervention content higher and completed more content than the unrestricted chapter access group, indicating support for the sequential access design. Parent mean posttest communication scores were significantly higher ( p  <   .05) than pre-test scores. No significant differences were detected for adolescent participants. Findings suggest that the Parent-Adolescent Communication Toolkit has potential to improve parent-adolescent communication but further effectiveness assessment is required.

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Peaks Over Threshold (POT): A methodology for automatic threshold estimation using goodness of fit p-value

NASA Astrophysics Data System (ADS)

Solari, Sebastián.; Egüen, Marta; Polo, María. José; Losada, Miguel A.

2017-04-01

Threshold estimation in the Peaks Over Threshold (POT) method and the impact of the estimation method on the calculation of high return period quantiles and their uncertainty (or confidence intervals) are issues that are still unresolved. In the past, methods based on goodness of fit tests and EDF-statistics have yielded satisfactory results, but their use has not yet been systematized. This paper proposes a methodology for automatic threshold estimation, based on the Anderson-Darling EDF-statistic and goodness of fit test. When combined with bootstrapping techniques, this methodology can be used to quantify both the uncertainty of threshold estimation and its impact on the uncertainty of high return period quantiles. This methodology was applied to several simulated series and to four precipitation/river flow data series. The results obtained confirmed its robustness. For the measured series, the estimated thresholds corresponded to those obtained by nonautomatic methods. Moreover, even though the uncertainty of the threshold estimation was high, this did not have a significant effect on the width of the confidence intervals of high return period quantiles.

Quantile regression applied to spectral distance decay

USGS Publications Warehouse

Rocchini, D.; Cade, B.S.

2008-01-01

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

Spectral distance decay: Assessing species beta-diversity by quantile regression

USGS Publications Warehouse

Rocchinl, D.; Nagendra, H.; Ghate, R.; Cade, B.S.

2009-01-01

Remotely sensed data represents key information for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (ols) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both ols and quantile regression. Nonetheless, ols regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression. ?? 2009 American Society for Photogrammetry and Remote Sensing.

Speckle pattern sequential extraction metric for estimating the focus spot size on a remote diffuse target.

PubMed

Yu, Zhan; Li, Yuanyang; Liu, Lisheng; Guo, Jin; Wang, Tingfeng; Yang, Guoqing

2017-11-10

The speckle pattern (line by line) sequential extraction (SPSE) metric is proposed by the one-dimensional speckle intensity level crossing theory. Through the sequential extraction of received speckle information, the speckle metrics for estimating the variation of focusing spot size on a remote diffuse target are obtained. Based on the simulation, we will give some discussions about the SPSE metric range of application under the theoretical conditions, and the aperture size will affect the metric performance of the observation system. The results of the analyses are verified by the experiment. This method is applied to the detection of relative static target (speckled jitter frequency is less than the CCD sampling frequency). The SPSE metric can determine the variation of the focusing spot size over a long distance, moreover, the metric will estimate the spot size under some conditions. Therefore, the monitoring and the feedback of far-field spot will be implemented laser focusing system applications and help the system to optimize the focusing performance.

Modeling energy expenditure in children and adolescents using quantile regression

PubMed Central

Yang, Yunwen; Adolph, Anne L.; Puyau, Maurice R.; Vohra, Firoz A.; Zakeri, Issa F.

2013-01-01

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5–18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children. PMID:23640591

Predicting Word Reading Ability: A Quantile Regression Study

ERIC Educational Resources Information Center

McIlraith, Autumn L.

2018-01-01

Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…

Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

USGS Publications Warehouse

Archfield, Stacey A.; Pugliese, Alessio; Castellarin, Attilio; Skøien, Jon O.; Kiang, Julie E.

2013-01-01

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

Breast cancer relatives' physical activity intervention needs and preferences: qualitative results.

PubMed

Hartman, Sheri J; Rosen, Rochelle K

2017-05-19

While many risk factors for breast cancer, such as family history, are not modifiable, some, however, can be modified. The study used formative qualitative research to learn about the physical activity intervention preferences and needs of first-degree female relatives (FDFRs) of breast cancer patients; that information was then used to develop a targeted physical activity intervention. Twenty FDFRs first completed a 12-week physical activity intervention and then attended two sequential focus groups (7 groups total). In the first set of focus groups participants provided feedback on the intervention. In the follow-up focus groups, proposed changes based on collected responses from the first groups were presented and participants provided feedback to further refine the intervention. Overall, we found strong interest for an intervention using breast cancer-related health concerns to promote positive behavior change. A theme underlying all of the feedback was the desire for a personalized intervention that was directly relevant to their lives. Participants wanted this personalization achieved through individually tailored content and incorporation of stories from other FDFRs. In order to successfully use concerns about breast cancer to motivate behavior change, participants also wanted a discussion about their individual risk factors for breast cancer including, but not limited to, lack of physical activity. This study demonstrates women's interest in receiving personalized information and highlights specific ways to individualize an intervention that increases motivation and engagement. Using a sequential qualitative approach was effective for formative intervention development. NCT03115658 (Retrospectively registered 4/13/17).

Testing a Dutch web-based tailored lifestyle programme among adults: a study protocol.

PubMed

Schulz, Daniela N; Kremers, Stef Pj; van Osch, Liesbeth Adm; Schneider, Francine; van Adrichem, Mathieu Jg; de Vries, Hein

2011-02-16

Smoking, high alcohol consumption, unhealthy eating habits and physical inactivity often lead to (chronic) diseases, such as cardiovascular diseases and cancer. Tailored online interventions have been proven to be effective in changing health behaviours. The aim of this study is to test and compare the effectiveness of two different tailoring strategies for changing lifestyle compared to a control group using a multiple health behaviour web-based approach. In our Internet-based tailored programme, the five lifestyle behaviours of smoking, alcohol intake, fruit consumption, vegetable consumption, and physical activity are addressed. This randomized controlled trial, conducted among Dutch adults, includes two experimental groups (i.e., a sequential behaviour tailoring condition and a simultaneous behaviour tailoring condition) and a control group. People in the sequential behaviour tailoring condition obtain feedback on whether their lifestyle behaviours meet the Dutch recommendations. Using a step-by-step approach, they are stimulated to continue with a computer tailored module to change only one unhealthy behaviour first. In the course of the study, they can proceed to change a second behaviour. People in the simultaneous behaviour tailoring condition receive computer tailored feedback about all their unhealthy behaviours during their first visit as a stimulation to change all unhealthy behaviours. The experimental groups can re-visit the website and can then receive ipsative feedback (i.e., current scores are compared to previous scores in order to give feedback about potential changes). The (difference in) effectiveness of the different versions of the programme will be tested and compared to a control group, in which respondents only receive a short health risk appraisal. Programme evaluations will assess satisfaction with and appreciation and personal relevance of the intervention among the respondents. Finally, potential subgroup differences pertaining to gender, age and socioeconomic status regarding the behaviour effects and programme evaluation will be assessed. Research regarding multiple behaviour change is in its infancy. We study how to offer multiple behaviour change interventions optimally. Using these results could strengthen the effectiveness of web-based computer-tailoring lifestyle programmes. This study will yield new results about the need for differential lifestyle approaches using Internet-based expert systems and potential differences in subgroups concerning the effectiveness and appreciation. Dutch Trial Register NTR2168.

Smooth quantile normalization.

PubMed

Hicks, Stephanie C; Okrah, Kwame; Paulson, Joseph N; Quackenbush, John; Irizarry, Rafael A; Bravo, Héctor Corrada

2018-04-01

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

A probability metric for identifying high-performing facilities: an application for pay-for-performance programs.

PubMed

Shwartz, Michael; Peköz, Erol A; Burgess, James F; Christiansen, Cindy L; Rosen, Amy K; Berlowitz, Dan

2014-12-01

Two approaches are commonly used for identifying high-performing facilities on a performance measure: one, that the facility is in a top quantile (eg, quintile or quartile); and two, that a confidence interval is below (or above) the average of the measure for all facilities. This type of yes/no designation often does not do well in distinguishing high-performing from average-performing facilities. To illustrate an alternative continuous-valued metric for profiling facilities--the probability a facility is in a top quantile--and show the implications of using this metric for profiling and pay-for-performance. We created a composite measure of quality from fiscal year 2007 data based on 28 quality indicators from 112 Veterans Health Administration nursing homes. A Bayesian hierarchical multivariate normal-binomial model was used to estimate shrunken rates of the 28 quality indicators, which were combined into a composite measure using opportunity-based weights. Rates were estimated using Markov Chain Monte Carlo methods as implemented in WinBUGS. The probability metric was calculated from the simulation replications. Our probability metric allowed better discrimination of high performers than the point or interval estimate of the composite score. In a pay-for-performance program, a smaller top quantile (eg, a quintile) resulted in more resources being allocated to the highest performers, whereas a larger top quantile (eg, being above the median) distinguished less among high performers and allocated more resources to average performers. The probability metric has potential but needs to be evaluated by stakeholders in different types of delivery systems.

Tractable Experiment Design via Mathematical Surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Brian J.

This presentation summarizes the development and implementation of quantitative design criteria motivated by targeted inference objectives for identifying new, potentially expensive computational or physical experiments. The first application is concerned with estimating features of quantities of interest arising from complex computational models, such as quantiles or failure probabilities. A sequential strategy is proposed for iterative refinement of the importance distributions used to efficiently sample the uncertain inputs to the computational model. In the second application, effective use of mathematical surrogates is investigated to help alleviate the analytical and numerical intractability often associated with Bayesian experiment design. This approach allows formore » the incorporation of prior information into the design process without the need for gross simplification of the design criterion. Illustrative examples of both design problems will be presented as an argument for the relevance of these research problems.« less

Influences of spatial and temporal variation on fish-habitat relationships defined by regression quantiles

Treesearch

Jason B. Dunham; Brian S. Cade; James W. Terrell

2002-01-01

We used regression quantiles to model potentially limiting relationships between the standing crop of cutthroat trout Oncorhynchus clarki and measures of stream channel morphology. Regression quantile models indicated that variation in fish density was inversely related to the width:depth ratio of streams but not to stream width or depth alone. The...

Superquantile/CVaR Risk Measures: Second-Order Theory

DTIC Science & Technology

2015-07-31

order superquantile risk minimization as well as superquantile regression , a proposed second-order version of quantile regression . Keywords...minimization as well as superquantile regression , a proposed second-order version of quantile regression . 15. SUBJECT TERMS 16. SECURITY...superquantilies, because it is deeply tied to generalized regression . The joint formula (3) is central to quantile regression , a well known alternative

An application of quantile random forests for predictive mapping of forest attributes

Treesearch

E.A. Freeman; G.G. Moisen

2015-01-01

Increasingly, random forest models are used in predictive mapping of forest attributes. Traditional random forests output the mean prediction from the random trees. Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It...

Implementation of combined SVM-algorithm and computer-aided perception feedback for pulmonary nodule detection

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Rannou, Didier; Brennan, Patrick C.

2012-02-01

This pilot study examines the effect of a novel decision support system in medical image interpretation. This system is based on combining image spatial frequency properties and eye-tracking data in order to recognize over and under calling errors. Thus, before it can be implemented as a detection aided schema, training is required during which SVMbased algorithm learns to recognize FP from all reported outcomes, and, FN from all unreported prolonged dwelled regions. Eight radiologists inspected 50 PA chest radiographs with the specific task of identifying lung nodules. Twentyfive cases contained CT proven subtle malignant lesions (5-20mm), but prevalence was not known by the subjects, who took part in two sequential reading sessions, the second, without and with support system feedback. MCMR ROC DBM and JAFROC analyses were conducted and demonstrated significantly higher scores following feedback with p values of 0.04, and 0.03 respectively, highlighting significant improvements in radiology performance once feedback was used. This positive effect on radiologists' performance might have important implications for future CAD-system development.

Regularized quantile regression for SNP marker estimation of pig growth curves.

PubMed

Barroso, L M A; Nascimento, M; Nascimento, A C C; Silva, F F; Serão, N V L; Cruz, C D; Resende, M D V; Silva, F L; Azevedo, C F; Lopes, P S; Guimarães, S E F

2017-01-01

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

Nonuniform sampling by quantiles.

PubMed

Craft, D Levi; Sonstrom, Reilly E; Rovnyak, Virginia G; Rovnyak, David

2018-03-01

A flexible strategy for choosing samples nonuniformly from a Nyquist grid using the concept of statistical quantiles is presented for broad classes of NMR experimentation. Quantile-directed scheduling is intuitive and flexible for any weighting function, promotes reproducibility and seed independence, and is generalizable to multiple dimensions. In brief, weighting functions are divided into regions of equal probability, which define the samples to be acquired. Quantile scheduling therefore achieves close adherence to a probability distribution function, thereby minimizing gaps for any given degree of subsampling of the Nyquist grid. A characteristic of quantile scheduling is that one-dimensional, weighted NUS schedules are deterministic, however higher dimensional schedules are similar within a user-specified jittering parameter. To develop unweighted sampling, we investigated the minimum jitter needed to disrupt subharmonic tracts, and show that this criterion can be met in many cases by jittering within 25-50% of the subharmonic gap. For nD-NUS, three supplemental components to choosing samples by quantiles are proposed in this work: (i) forcing the corner samples to ensure sampling to specified maximum values in indirect evolution times, (ii) providing an option to triangular backfill sampling schedules to promote dense/uniform tracts at the beginning of signal evolution periods, and (iii) providing an option to force the edges of nD-NUS schedules to be identical to the 1D quantiles. Quantile-directed scheduling meets the diverse needs of current NUS experimentation, but can also be used for future NUS implementations such as off-grid NUS and more. A computer program implementing these principles (a.k.a. QSched) in 1D- and 2D-NUS is available under the general public license. Copyright © 2018 Elsevier Inc. All rights reserved.

Nonuniform sampling by quantiles

NASA Astrophysics Data System (ADS)

Craft, D. Levi; Sonstrom, Reilly E.; Rovnyak, Virginia G.; Rovnyak, David

2018-03-01

A flexible strategy for choosing samples nonuniformly from a Nyquist grid using the concept of statistical quantiles is presented for broad classes of NMR experimentation. Quantile-directed scheduling is intuitive and flexible for any weighting function, promotes reproducibility and seed independence, and is generalizable to multiple dimensions. In brief, weighting functions are divided into regions of equal probability, which define the samples to be acquired. Quantile scheduling therefore achieves close adherence to a probability distribution function, thereby minimizing gaps for any given degree of subsampling of the Nyquist grid. A characteristic of quantile scheduling is that one-dimensional, weighted NUS schedules are deterministic, however higher dimensional schedules are similar within a user-specified jittering parameter. To develop unweighted sampling, we investigated the minimum jitter needed to disrupt subharmonic tracts, and show that this criterion can be met in many cases by jittering within 25-50% of the subharmonic gap. For nD-NUS, three supplemental components to choosing samples by quantiles are proposed in this work: (i) forcing the corner samples to ensure sampling to specified maximum values in indirect evolution times, (ii) providing an option to triangular backfill sampling schedules to promote dense/uniform tracts at the beginning of signal evolution periods, and (iii) providing an option to force the edges of nD-NUS schedules to be identical to the 1D quantiles. Quantile-directed scheduling meets the diverse needs of current NUS experimentation, but can also be used for future NUS implementations such as off-grid NUS and more. A computer program implementing these principles (a.k.a. QSched) in 1D- and 2D-NUS is available under the general public license.

Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts

USGS Publications Warehouse

Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin

2018-01-01

Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

Orbit control of a stratospheric satellite with parameter uncertainties

NASA Astrophysics Data System (ADS)

Xu, Ming; Huo, Wei

2016-12-01

When a stratospheric satellite travels by prevailing winds in the stratosphere, its cross-track displacement needs to be controlled to keep a constant latitude orbital flight. To design the orbit control system, a 6 degree-of-freedom (DOF) model of the satellite is established based on the second Lagrangian formulation, it is proven that the input/output feedback linearization theory cannot be directly implemented for the orbit control with this model, thus three subsystem models are deduced from the 6-DOF model to develop a sequential nonlinear control strategy. The control strategy includes an adaptive controller for the balloon-tether subsystem with uncertain balloon parameters, a PD controller based on feedback linearization for the tether-sail subsystem, and a sliding mode controller for the sail-rudder subsystem with uncertain sail parameters. Simulation studies demonstrate that the proposed control strategy is robust to uncertainties and satisfies high precision requirements for the orbit flight of the satellite.

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Matching a Distribution by Matching Quantiles Estimation

PubMed Central

Sgouropoulos, Nikolaos; Yao, Qiwei; Yastremiz, Claudia

2015-01-01

Motivated by the problem of selecting representative portfolios for backtesting counterparty credit risks, we propose a matching quantiles estimation (MQE) method for matching a target distribution by that of a linear combination of a set of random variables. An iterative procedure based on the ordinary least-squares estimation (OLS) is proposed to compute MQE. MQE can be easily modified by adding a LASSO penalty term if a sparse representation is desired, or by restricting the matching within certain range of quantiles to match a part of the target distribution. The convergence of the algorithm and the asymptotic properties of the estimation, both with or without LASSO, are established. A measure and an associated statistical test are proposed to assess the goodness-of-match. The finite sample properties are illustrated by simulation. An application in selecting a counterparty representative portfolio with a real dataset is reported. The proposed MQE also finds applications in portfolio tracking, which demonstrates the usefulness of combining MQE with LASSO. PMID:26692592

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

Comparability of a short food frequency questionnaire to assess diet quality: the DISCOVER study.

PubMed

Dehghan, Mahshid; Ge, Yipeng; El Sheikh, Wala; Bawor, Monica; Rangarajan, Sumathy; Dennis, Brittany; Vair, Judith; Sholer, Heather; Hutchinson, Nichole; Iordan, Elizabeth; Mackie, Pam; Samaan, Zainab

2017-09-01

This study aims to assess comparability of a short food frequency questionnaire (SFFQ) used in the Determinants of Suicide: Conventional and Emergent Risk Study (DISCOVER Study) with a validated comprehensive FFQ (CFFQ). A total of 127 individuals completed SFFQ and CFFQ. Healthy eating was measured using Healthy Eating Score (HES). Estimated food intake and healthy eating assessed by SFFQ was compared with the CFFQ. For most food groups and HES, the highest Spearman's rank correlation coefficients between the two FFQs were r > .60. For macro-nutrients, the correlations exceeded 0.4. Cross-classification of quantile analysis showed that participants were classified between 46% and 81% into the exact same quantiles, while 10% or less were misclassified into opposite quantiles. The Bland-Altman plots showed an acceptable level of agreement between the two dietary measurement methods. The SFFQ can be used for Canadian with psychiatric disorders to rank them based on their dietary intake.

Examining Predictive Validity of Oral Reading Fluency Slope in Upper Elementary Grades Using Quantile Regression.

PubMed

Cho, Eunsoo; Capin, Philip; Roberts, Greg; Vaughn, Sharon

2017-07-01

Within multitiered instructional delivery models, progress monitoring is a key mechanism for determining whether a child demonstrates an adequate response to instruction. One measure commonly used to monitor the reading progress of students is oral reading fluency (ORF). This study examined the extent to which ORF slope predicts reading comprehension outcomes for fifth-grade struggling readers ( n = 102) participating in an intensive reading intervention. Quantile regression models showed that ORF slope significantly predicted performance on a sentence-level fluency and comprehension assessment, regardless of the students' reading skills, controlling for initial ORF performance. However, ORF slope was differentially predictive of a passage-level comprehension assessment based on students' reading skills when controlling for initial ORF status. Results showed that ORF explained unique variance for struggling readers whose posttest performance was at the upper quantiles at the end of the reading intervention, but slope was not a significant predictor of passage-level comprehension for students whose reading problems were the most difficult to remediate.

Asymptotics of nonparametric L-1 regression models with dependent data

PubMed Central

ZHAO, ZHIBIAO; WEI, YING; LIN, DENNIS K.J.

2013-01-01

We investigate asymptotic properties of least-absolute-deviation or median quantile estimates of the location and scale functions in nonparametric regression models with dependent data from multiple subjects. Under a general dependence structure that allows for longitudinal data and some spatially correlated data, we establish uniform Bahadur representations for the proposed median quantile estimates. The obtained Bahadur representations provide deep insights into the asymptotic behavior of the estimates. Our main theoretical development is based on studying the modulus of continuity of kernel weighted empirical process through a coupling argument. Progesterone data is used for an illustration. PMID:24955016

Incremental impact of body mass status with modifiable unhealthy lifestyle behaviors on pharmaceutical expenditure.

PubMed

Kim, Tae Hyun; Lee, Eui-Kyung; Han, Euna

Overweight/obesity is a growing health risk in Korea. The impact of overweight/obesity on pharmaceutical expenditure can be larger if individuals have multiple risk factors and multiple comorbidities. The current study estimated the combined effects of overweight/obesity and other unhealthy behaviors on pharmaceutical expenditure. An instrumental variable quantile regression model was estimated using Korea Health Panel Study data. The current study extracted data from 3 waves (2009, 2010, and 2011). The final sample included 7148 person-year observations for adults aged 20 years or older. Overweight/obese individuals had higher pharmaceutical expenditure than their non-obese counterparts only at the upper quantiles of the conditional distribution of pharmaceutical expenditure (by 119% at the 90th quantile and 115% at the 95th). The current study found a stronger association at the upper quantiles among men (152%, 144%, and 150% at the 75th, 90th, and 95th quantiles, respectively) than among women (152%, 150%, and 148% at the 75th, 90th, and 95th quantiles, respectively). The association at the upper quantiles was stronger when combined with moderate to heavy drinking and no regular physical check-up, particularly among males. The current study confirms that the association of overweight/obesity with modifiable unhealthy behaviors on pharmaceutical expenditure is larger than with overweight/obesity alone. Assessing the effect of overweight/obesity with lifestyle risk factors can help target groups for public health intervention programs. Copyright © 2015 Elsevier Inc. All rights reserved.

Orosensory responsiveness and alcohol behaviour.

PubMed

Thibodeau, Margaret; Bajec, Martha; Pickering, Gary

2017-08-01

Consumption of alcoholic beverages is widespread through much of the world, and significantly impacts human health and well-being. We sought to determine the contribution of orosensation ('taste') to several alcohol intake measures by examining general responsiveness to taste and somatosensory stimuli in a convenience sample of 435 adults recruited from six cohorts. Each cohort was divided into quantiles based on their responsiveness to sweet, sour, bitter, salty, umami, metallic, and astringent stimuli, and the resulting quantiles pooled for analysis (Kruskal-Wallis ANOVA). Responsiveness to bitter and astringent stimuli was associated in a non-linear fashion with intake of all alcoholic beverage types, with the highest consumption observed in middle quantiles. Sourness responsiveness tended to be inversely associated with all measures of alcohol consumption. Regardless of sensation, the most responsive quantiles tended to drink less, although sweetness showed little relationship between responsiveness and intake. For wine, increased umami and metallic responsiveness tended to predict lower total consumption and frequency. A limited examination of individuals who abstain from all alcohol indicated a tendency toward higher responsiveness than alcohol consumers to sweetness, sourness, bitterness, and saltiness (biserial correlation), suggesting that broadly-tuned orosensory responsiveness may be protective against alcohol use and possibly misuse. Overall, these findings confirm the importance of orosensory responsiveness in mediating consumption of alcohol, and indicate areas for further research. Copyright © 2017. Published by Elsevier Inc.

Spatio-temporal characteristics of the extreme precipitation by L-moment-based index-flood method in the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Yin, Yixing; Chen, Haishan; Xu, Chong-Yu; Xu, Wucheng; Chen, Changchun; Sun, Shanlei

2016-05-01

The regionalization methods, which "trade space for time" by pooling information from different locations in the frequency analysis, are efficient tools to enhance the reliability of extreme quantile estimates. This paper aims at improving the understanding of the regional frequency of extreme precipitation by using regionalization methods, and providing scientific background and practical assistance in formulating the regional development strategies for water resources management in one of the most developed and flood-prone regions in China, the Yangtze River Delta (YRD) region. To achieve the main goals, L-moment-based index-flood (LMIF) method, one of the most popular regionalization methods, is used in the regional frequency analysis of extreme precipitation with special attention paid to inter-site dependence and its influence on the accuracy of quantile estimates, which has not been considered by most of the studies using LMIF method. Extensive data screening of stationarity, serial dependence, and inter-site dependence was carried out first. The entire YRD region was then categorized into four homogeneous regions through cluster analysis and homogenous analysis. Based on goodness-of-fit statistic and L-moment ratio diagrams, generalized extreme-value (GEV) and generalized normal (GNO) distributions were identified as the best fitted distributions for most of the sub-regions, and estimated quantiles for each region were obtained. Monte Carlo simulation was used to evaluate the accuracy of the quantile estimates taking inter-site dependence into consideration. The results showed that the root-mean-square errors (RMSEs) were bigger and the 90 % error bounds were wider with inter-site dependence than those without inter-site dependence for both the regional growth curve and quantile curve. The spatial patterns of extreme precipitation with a return period of 100 years were finally obtained which indicated that there are two regions with highest precipitation extremes and a large region with low precipitation extremes. However, the regions with low precipitation extremes are the most developed and densely populated regions of the country, and floods will cause great loss of human life and property damage due to the high vulnerability. The study methods and procedure demonstrated in this paper will provide useful reference for frequency analysis of precipitation extremes in large regions, and the findings of the paper will be beneficial in flood control and management in the study area.

Superquantile/CVaR Risk Measures: Second-Order Theory

DTIC Science & Technology

2014-07-17

order version of quantile regression . Keywords: superquantiles, conditional value-at-risk, second-order superquantiles, mixed superquan- tiles... quantile regression . 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 26 19a...second-order superquantiles is in the domain of generalized regression . We laid out in [16] a parallel methodology to that of quantile regression

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

PubMed Central

Müller, Christian; Schillert, Arne; Röthemeier, Caroline; Trégouët, David-Alexandre; Proust, Carole; Binder, Harald; Pfeiffer, Norbert; Beutel, Manfred; Lackner, Karl J.; Schnabel, Renate B.; Tiret, Laurence; Wild, Philipp S.; Blankenberg, Stefan

2016-01-01

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data. PMID:27272489

Peer feedback for examiner quality assurance on MRCGP International South Asia: a mixed methods study.

PubMed

Perera, D P; Andrades, Marie; Wass, Val

2017-12-08

The International Membership Examination (MRCGP[INT]) of the Royal College of General Practitioners UK is a unique collaboration between four South Asian countries with diverse cultures, epidemiology, clinical facilities and resources. In this setting good quality assurance is imperative to achieve acceptable standards of inter rater reliability. This study aims to explore the process of peer feedback for examiner quality assurance with regard to factors affecting the implementation and acceptance of the method. A sequential mixed methods approach was used based on focus group discussions with examiners (n = 12) and clinical examination convenors who acted as peer reviewers (n = 4). A questionnaire based on emerging themes and literature review was then completed by 20 examiners at the subsequent OSCE exam. Qualitative data were analysed using an iterative reflexive process. Quantitative data were integrated by interpretive analysis looking for convergence, complementarity or dissonance. The qualitative data helped understand the issues and informed the process of developing the questionnaire. The quantitative data allowed for further refining of issues, wider sampling of examiners and giving voice to different perspectives. Examiners stated specifically that peer feedback gave an opportunity for discussion, standardisation of judgments and improved discriminatory abilities. Interpersonal dynamics, hierarchy and perception of validity of feedback were major factors influencing acceptance of feedback. Examiners desired increased transparency, accountability and the opportunity for equal partnership within the process. The process was stressful for examiners and reviewers; however acceptance increased with increasing exposure to receiving feedback. The process could be refined to improve acceptability through scrupulous attention to training and selection of those giving feedback to improve the perceived validity of feedback and improved reviewer feedback skills to enable better interpersonal dynamics and a more equitable feedback process. It is important to highlight the role of quality assurance and peer feedback as a tool for continuous improvement and maintenance of standards to examiners during training. Examiner quality assurance using peer feedback was generally a successful and accepted process. The findings highlight areas for improvement and guide the path towards a model of feedback that is responsive to examiner views and cultural sensibilities.

Explaining the relation between pathological gambling and depression: Rumination as an underlying common cause.

PubMed

Krause, Kristian; Bischof, Anja; Lewin, Silvia; Guertler, Diana; Rumpf, Hans-Jürgen; John, Ulrich; Meyer, Christian

2018-05-30

Background and aims Symptoms of pathological gambling (SPG) and depression often co-occur. The nature of this relationship remains unclear. Rumination, which is well known to be associated with depression, might act as a common underlying factor explaining the frequent co-occurrence of both conditions. The aim of this study is to analyze associations between the rumination subfactors brooding and reflection and SPG. Methods Participants aged 14-64 years were recruited within an epidemiological study on pathological gambling in Germany. Cross-sectional data of 506 (80.4% male) individuals with a history of gambling problems were analyzed. The assessment included a standardized clinical interview. To examine the effects of rumination across different levels of problem gambling severity, sequential quantile regression was used to analyze the association between the rumination subfactors and SPG. Results Brooding (p = .005) was positively associated with the severity of problem gambling after adjusting for reflection, depressive symptoms, and sociodemographic variables. Along the distribution of problem gambling severity, findings hold for all but the lowest severity level. Reflection (p = .347) was not associated with the severity of problem gambling at the median. Along the distribution of problem gambling severity, there was an inverse association at only one quantile. Discussion and conclusions Brooding might be important in the development and maintenance of problem gambling. With its relations to depression and problem gambling, it might be crucial when it comes to explaining the high comorbidity rates between SPG and depression. The role of reflection in SPG remains inconclusive.

Involving young people in decision making about sequential cochlear implantation.

PubMed

Ion, Rebecca; Cropper, Jenny; Walters, Hazel

2013-11-01

The National Institute for Health and Clinical Excellence guidelines recommended young people who currently have one cochlear implant be offered assessment for a second, sequential implant, due to the reported improvements in sound localization and speech perception in noise. The possibility and benefits of group information and counselling assessments were considered. Previous research has shown advantages of group sessions involving young people and their families and such groups which also allow young people opportunity to discuss their concerns separately to their parents/guardians are found to be 'hugely important'. Such research highlights the importance of involving children in decision-making processes. Families considering a sequential cochlear implant were invited to a group information/counselling session, which included time for parents and children to meet separately. Fourteen groups were held with approximately four to five families in each session, totalling 62 patients. The sessions were facilitated by the multi-disciplinary team, with a particular psychological focus in the young people's session. Feedback from families has demonstrated positive support for this format. Questionnaire feedback, to which nine families responded, indicated that seven preferred the group session to an individual session and all approved of separate groups for the child and parents/guardians. Overall the group format and psychological focus were well received in this typically surgical setting and emphasized the importance of involving the young person in the decision-making process. This positive feedback also opens up the opportunity to use a group format in other assessment processes.

magicaxis: Pretty scientific plotting with minor-tick and log minor-tick support

NASA Astrophysics Data System (ADS)

Robotham, Aaron S. G.

2016-04-01

The R suite magicaxis makes useful and pretty plots for scientific plotting and includes functions for base plotting, with particular emphasis on pretty axis labelling in a number of circumstances that are often used in scientific plotting. It also includes functions for generating images and contours that reflect the 2D quantile levels of the data designed particularly for output of MCMC posteriors where visualizing the location of the 68% and 95% 2D quantiles for covariant parameters is a necessary part of the post MCMC analysis, can generate low and high error bars, and allows clipping of values, rejection of bad values, and log stretching.

Solvency supervision based on a total balance sheet approach

NASA Astrophysics Data System (ADS)

Pitselis, Georgios

2009-11-01

In this paper we investigate the adequacy of the own funds a company requires in order to remain healthy and avoid insolvency. Two methods are applied here; the quantile regression method and the method of mixed effects models. Quantile regression is capable of providing a more complete statistical analysis of the stochastic relationship among random variables than least squares estimation. The estimated mixed effects line can be considered as an internal industry equation (norm), which explains a systematic relation between a dependent variable (such as own funds) with independent variables (e.g. financial characteristics, such as assets, provisions, etc.). The above two methods are implemented with two data sets.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Managing more than the mean: Using quantile regression to identify factors related to large elk groups

USGS Publications Warehouse

Brennan, Angela K.; Cross, Paul C.; Creely, Scott

2015-01-01

Synthesis and applications. Our analysis of elk group size distributions using quantile regression suggests that private land, irrigation, open habitat, elk density and wolf abundance can affect large elk group sizes. Thus, to manage larger groups by removal or dispersal of individuals, we recommend incentivizing hunting on private land (particularly if irrigated) during the regular and late hunting seasons, promoting tolerance of wolves on private land (if elk aggregate in these areas to avoid wolves) and creating more winter range and varied habitats. Relationships to the variables of interest also differed by quantile, highlighting the importance of using quantile regression to examine response variables more completely to uncover relationships important to conservation and management.

Asymmetric impact of rainfall on India's food grain production: evidence from quantile autoregressive distributed lag model

NASA Astrophysics Data System (ADS)

Pal, Debdatta; Mitra, Subrata Kumar

2018-01-01

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

The Income-Health Relationship 'Beyond the Mean': New Evidence from Biomarkers.

PubMed

Carrieri, Vincenzo; Jones, Andrew M

2017-07-01

The relationship between income and health is one of the most explored topics in health economics but less is known about this relationship at different points of the health distribution. Analysis based solely on the mean may miss important information in other parts of the distribution. This is especially relevant when clinical concern is focused on the tail of the distribution and when evaluating the income gradient at different points of the distribution and decomposing income-related inequalities in health is of interest. We use the unconditional quantile regression approach to analyse the income gradient across the entire distribution of objectively measured blood-based biomarkers. We apply an Oaxaca-Blinder decomposition at various quantiles of the biomarker distributions to analyse gender differentials in biomarkers and to measure the contribution of income (and other covariates) to these differentials. Using data from the Health Survey for England, we find a non-linear relationship between income and health and a strong gradient with respect to income at the highest quantiles of the biomarker distributions. We find that there is heterogeneity in the association of health to income across genders, which accounts for a substantial percentage of the gender differentials in observed health. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

An evaluation of the effectiveness of a risk-based monitoring approach implemented with clinical trials involving implantable cardiac medical devices.

PubMed

Diani, Christopher A; Rock, Angie; Moll, Phil

2017-12-01

Background Risk-based monitoring is a concept endorsed by the Food and Drug Administration to improve clinical trial data quality by focusing monitoring efforts on critical data elements and higher risk investigator sites. BIOTRONIK approached this by implementing a comprehensive strategy that assesses risk and data quality through a combination of operational controls and data surveillance. This publication demonstrates the effectiveness of a data-driven risk assessment methodology when used in conjunction with a tailored monitoring plan. Methods We developed a data-driven risk assessment system to rank 133 investigator sites comprising 3442 subjects and identify those sites that pose a potential risk to the integrity of data collected in implantable cardiac device clinical trials. This included identification of specific risk factors and a weighted scoring mechanism. We conducted trend analyses for risk assessment data collected over 1 year to assess the overall impact of our data surveillance process combined with other operational monitoring efforts. Results Trending analyses of key risk factors revealed an improvement in the quality of data collected during the observation period. The three risk factors follow-up compliance rate, unavailability of critical data, and noncompliance rate correspond closely with Food and Drug Administration's risk-based monitoring guidance document. Among these three risk factors, 100% (12/12) of quantiles analyzed showed an increase in data quality. Of these, 67% (8/12) of the improving trends in worst performing quantiles had p-values less than 0.05, and 17% (2/12) had p-values between 0.05 and 0.06. Among the poorest performing site quantiles, there was a statistically significant decrease in subject follow-up noncompliance rates, protocol noncompliance rates, and incidence of missing critical data. Conclusion One year after implementation of a comprehensive strategy for risk-based monitoring, including a data-driven risk assessment methodology to target on-site monitoring visits, statistically significant improvement was seen in a majority of measurable risk factors at the worst performing site quantiles. For the three risk factors which are most critical to the overall compliance of cardiac rhythm management medical device studies: follow-up compliance rate, unavailability of critical data, and noncompliance rate, we measured significant improvement in data quality. Although the worst performing site quantiles improved but not significantly in some risk factors such as subject attrition, the data-driven risk assessment highlighted key areas on which to continue focusing both on-site and centralized monitoring efforts. Data-driven surveillance of clinical trial performance provides actionable observations that can improve site performance. Clinical trials utilizing risk-based monitoring by leveraging a data-driven quality assessment combined with specific operational procedures may lead to an improvement in data quality and resource efficiencies.

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach.

PubMed

Helbich, Marco; Klein, Nadja; Roberts, Hannah; Hagedoorn, Paulien; Groenewegen, Peter P

2018-06-20

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was: a) to assess antidepressant prescription rates in relation to green space, and b) to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15%, 50%, and 85% quantiles to estimate green space-prescription rate correlations, controlling for physical activity levels, socio-demographics, urbanicity, etc. RESULTS: The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28% needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79%. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies. Copyright © 2018 Elsevier Inc. All rights reserved.

Constructing inverse probability weights for continuous exposures: a comparison of methods.

PubMed

Naimi, Ashley I; Moodie, Erica E M; Auger, Nathalie; Kaufman, Jay S

2014-03-01

Inverse probability-weighted marginal structural models with binary exposures are common in epidemiology. Constructing inverse probability weights for a continuous exposure can be complicated by the presence of outliers, and the need to identify a parametric form for the exposure and account for nonconstant exposure variance. We explored the performance of various methods to construct inverse probability weights for continuous exposures using Monte Carlo simulation. We generated two continuous exposures and binary outcomes using data sampled from a large empirical cohort. The first exposure followed a normal distribution with homoscedastic variance. The second exposure followed a contaminated Poisson distribution, with heteroscedastic variance equal to the conditional mean. We assessed six methods to construct inverse probability weights using: a normal distribution, a normal distribution with heteroscedastic variance, a truncated normal distribution with heteroscedastic variance, a gamma distribution, a t distribution (1, 3, and 5 degrees of freedom), and a quantile binning approach (based on 10, 15, and 20 exposure categories). We estimated the marginal odds ratio for a single-unit increase in each simulated exposure in a regression model weighted by the inverse probability weights constructed using each approach, and then computed the bias and mean squared error for each method. For the homoscedastic exposure, the standard normal, gamma, and quantile binning approaches performed best. For the heteroscedastic exposure, the quantile binning, gamma, and heteroscedastic normal approaches performed best. Our results suggest that the quantile binning approach is a simple and versatile way to construct inverse probability weights for continuous exposures.

Impact of a Sequential Intervention on Albumin Utilization in Critical Care.

PubMed

Lyu, Peter F; Hockenberry, Jason M; Gaydos, Laura M; Howard, David H; Buchman, Timothy G; Murphy, David J

2016-07-01

Literature generally finds no advantages in mortality risk for albumin over cheaper alternatives in many settings. Few studies have combined financial and nonfinancial strategies to reduce albumin overuse. We evaluated the effect of a sequential multifaceted intervention on decreasing albumin use in ICU and explore the effects of different strategies. Prospective prepost cohort study. Eight ICUs at two hospitals in an academic healthcare system. Adult patients admitted to study ICUs from September 2011 to August 2014 (n = 22,004). Over 2 years, providers in study ICUs participated in an intervention to reduce albumin use involving monthly feedback and explicit financial incentives in the first year and internal guidelines and order process changes in the second year. Outcomes measured were albumin orders per ICU admission, direct albumin costs, and mortality. Mean (SD) utilization decreased 37% from 2.7 orders (6.8) per admission during the baseline to 1.7 orders (4.6) during the intervention (p < 0.001). Regression analysis revealed that the intervention was independently associated with 0.9 fewer orders per admission, a 42% relative decrease. This adjusted effect consisted of an 18% reduction in the probability of using any albumin (p < 0.001) and a 29% reduction in the number of orders per admission among patients receiving any (p < 0.001). Secondary analysis revealed that probability reductions were concurrent with internal guidelines and order process modification while reductions in quantity occurred largely during the financial incentives and feedback period. Estimated cost savings totaled $2.5M during the 2-year intervention. There was no significant difference in ICU or hospital mortality between baseline and intervention. A sequential intervention achieved significant reductions in ICU albumin use and cost savings without changes in patient outcomes, supporting the combination of financial and nonfinancial strategies to align providers with evidence-based practices.

Technical note: Combining quantile forecasts and predictive distributions of streamflows

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

2017-11-01

The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

Using Gamma and Quantile Regressions to Explore the Association between Job Strain and Adiposity in the ELSA-Brasil Study: Does Gender Matter?

PubMed

Fonseca, Maria de Jesus Mendes da; Juvanhol, Leidjaira Lopes; Rotenberg, Lúcia; Nobre, Aline Araújo; Griep, Rosane Härter; Alves, Márcia Guimarães de Mello; Cardoso, Letícia de Oliveira; Giatti, Luana; Nunes, Maria Angélica; Aquino, Estela M L; Chor, Dóra

2017-11-17

This paper explores the association between job strain and adiposity, using two statistical analysis approaches and considering the role of gender. The research evaluated 11,960 active baseline participants (2008-2010) in the ELSA-Brasil study. Job strain was evaluated through a demand-control questionnaire, while body mass index (BMI) and waist circumference (WC) were evaluated in continuous form. The associations were estimated using gamma regression models with an identity link function. Quantile regression models were also estimated from the final set of co-variables established by gamma regression. The relationship that was found varied by analytical approach and gender. Among the women, no association was observed between job strain and adiposity in the fitted gamma models. In the quantile models, a pattern of increasing effects of high strain was observed at higher BMI and WC distribution quantiles. Among the men, high strain was associated with adiposity in the gamma regression models. However, when quantile regression was used, that association was found not to be homogeneous across outcome distributions. In addition, in the quantile models an association was observed between active jobs and BMI. Our results point to an association between job strain and adiposity, which follows a heterogeneous pattern. Modelling strategies can produce different results and should, accordingly, be used to complement one another.

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

DOE PAGES

Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab; ...

2016-11-21

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach tomore » address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.« less

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach tomore » address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.« less

CASAS: Cancer Survival Analysis Suite, a web based application

PubMed Central

Rupji, Manali; Zhang, Xinyan; Kowalski, Jeanne

2017-01-01

We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/. PMID:28928946

CASAS: Cancer Survival Analysis Suite, a web based application.

PubMed

Rupji, Manali; Zhang, Xinyan; Kowalski, Jeanne

2017-01-01

We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.

Optical flip-flops in a polarization-encoded optical shadow-casting scheme.

PubMed

Rizvi, R A; Zubairy, M S

1994-06-10

We propose a novel scheme that optically implements various types of binary sequential logic elements. This is based on a polarization-encoded optical shadow-casting system. The proposed system architecture is capable of implementing synchronous as well as asynchronous sequential circuits owing to the inherent structural flexibility of optical shadow casting. By employing the proposed system, we present the design and implementation schemes of a J-K flip-flop and clocked R-S and D latches. The main feature of these flip-flops is that the propagation of the signal from the input plane to the output (i.e., processing) and from the output plane to the source plane (i.e., feedback) is all optical. Consequently the efficiency of these elements in terms of speed is increased. The only electronic part in the system is the detection of the outputs and the switching of the source plane.

A quantile regression approach can reveal the effect of fruit and vegetable consumption on plasma homocysteine levels.

PubMed

Verly, Eliseu; Steluti, Josiane; Fisberg, Regina Mara; Marchioni, Dirce Maria Lobo

2014-01-01

A reduction in homocysteine concentration due to the use of supplemental folic acid is well recognized, although evidence of the same effect for natural folate sources, such as fruits and vegetables (FV), is lacking. The traditional statistical analysis approaches do not provide further information. As an alternative, quantile regression allows for the exploration of the effects of covariates through percentiles of the conditional distribution of the dependent variable. To investigate how the associations of FV intake with plasma total homocysteine (tHcy) differ through percentiles in the distribution using quantile regression. A cross-sectional population-based survey was conducted among 499 residents of Sao Paulo City, Brazil. The participants provided food intake and fasting blood samples. Fruit and vegetable intake was predicted by adjusting for day-to-day variation using a proper measurement error model. We performed a quantile regression to verify the association between tHcy and the predicted FV intake. The predicted values of tHcy for each percentile model were calculated considering an increase of 200 g in the FV intake for each percentile. The results showed that tHcy was inversely associated with FV intake when assessed by linear regression whereas, the association was different when using quantile regression. The relationship with FV consumption was inverse and significant for almost all percentiles of tHcy. The coefficients increased as the percentile of tHcy increased. A simulated increase of 200 g in the FV intake could decrease the tHcy levels in the overall percentiles, but the higher percentiles of tHcy benefited more. This study confirms that the effect of FV intake on lowering the tHcy levels is dependent on the level of tHcy using an innovative statistical approach. From a public health point of view, encouraging people to increase FV intake would benefit people with high levels of tHcy.

Streamflow distribution maps for the Cannon River drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota

USGS Publications Warehouse

Smith, Erik A.; Sanocki, Chris A.; Lorenz, David L.; Jacobsen, Katrin E.

2017-12-27

Streamflow distribution maps for the Cannon River and St. Louis River drainage basins were developed by the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to illustrate relative and cumulative streamflow distributions. The Cannon River was selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, St. Louis) was subdivided into nested drainage basins: the Cannon River was subdivided into 152 nested drainage basins, and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated volumes of groundwater discharge (as base flow) and surface runoff flowing into all surface-water features were displayed under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.Streamflow distribution maps were developed using flow-duration curve exceedance-probability quantiles in conjunction with Soil-Water-Balance model outputs; both the flow-duration curve and Soil-Water-Balance models were built upon previously published U.S. Geological Survey reports. The selected streamflow distribution maps provide a proactive water management tool for State cooperators by illustrating flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms of surface-water flows, the streamflows can be evaluated in the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface-water appropriations. Presented streamflow distribution maps are foundational work intended to support the development of additional streamflow distribution maps that include statistical constraints on the selected flow conditions.

Quantile equivalence to evaluate compliance with habitat management objectives

USGS Publications Warehouse

Cade, Brian S.; Johnson, Pamela R.

2011-01-01

Equivalence estimated with linear quantile regression was used to evaluate compliance with habitat management objectives at Arapaho National Wildlife Refuge based on monitoring data collected in upland (5,781 ha; n = 511 transects) and riparian and meadow (2,856 ha, n = 389 transects) habitats from 2005 to 2008. Quantiles were used because the management objectives specified proportions of the habitat area that needed to comply with vegetation criteria. The linear model was used to obtain estimates that were averaged across 4 y. The equivalence testing framework allowed us to interpret confidence intervals for estimated proportions with respect to intervals of vegetative criteria (equivalence regions) in either a liberal, benefit-of-doubt or conservative, fail-safe approach associated with minimizing alternative risks. Simple Boolean conditional arguments were used to combine the quantile equivalence results for individual vegetation components into a joint statement for the multivariable management objectives. For example, management objective 2A required at least 809 ha of upland habitat with a shrub composition ≥0.70 sagebrush (Artemisia spp.), 20–30% canopy cover of sagebrush ≥25 cm in height, ≥20% canopy cover of grasses, and ≥10% canopy cover of forbs on average over 4 y. Shrub composition and canopy cover of grass each were readily met on >3,000 ha under either conservative or liberal interpretations of sampling variability. However, there were only 809–1,214 ha (conservative to liberal) with ≥10% forb canopy cover and 405–1,098 ha with 20–30%canopy cover of sagebrush ≥25 cm in height. Only 91–180 ha of uplands simultaneously met criteria for all four components, primarily because canopy cover of sagebrush and forbs was inversely related when considered at the spatial scale (30 m) of a sample transect. We demonstrate how the quantile equivalence analyses also can help refine the numerical specification of habitat objectives and explore specification of spatial scales for objectives with respect to sampling scales used to evaluate those objectives.

Heritability Across the Distribution: An Application of Quantile Regression

PubMed Central

Petrill, Stephen A.; Hart, Sara A.; Schatschneider, Christopher; Thompson, Lee A.; Deater-Deckard, Kirby; DeThorne, Laura S.; Bartlett, Christopher

2016-01-01

We introduce a new method for analyzing twin data called quantile regression. Through the application presented here, quantile regression is able to assess the genetic and environmental etiology of any skill or ability, at multiple points in the distribution of that skill or ability. This method is compared to the Cherny et al. (Behav Genet 22:153–162, 1992) method in an application to four different reading-related outcomes in 304 pairs of first-grade same sex twins enrolled in the Western Reserve Reading Project. Findings across the two methods were similar; both indicated some variation across the distribution of the genetic and shared environmental influences on non-word reading. However, quantile regression provides more details about the location and size of the measured effect. Applications of the technique are discussed. PMID:21877231

A sequential adaptation technique and its application to the Mark 12 IFF system

NASA Astrophysics Data System (ADS)

Bailey, John S.; Mallett, John D.; Sheppard, Duane J.; Warner, F. Neal; Adams, Robert

1986-07-01

Sequential adaptation uses only two sets of receivers, correlators, and A/D converters which are time multiplexed to effect spatial adaptation in a system with (N) adaptive degrees of freedom. This technique can substantially reduce the hardware cost over what is realizable in a parallel architecture. A three channel L-band version of the sequential adapter was built and tested for use with the MARK XII IFF (identify friend or foe) system. In this system the sequentially determined adaptive weights were obtained digitally but implemented at RF. As a result, many of the post RF hardware induced sources of error that normally limit cancellation, such as receiver mismatch, are removed by the feedback property. The result is a system that can yield high levels of cancellation and be readily retrofitted to currently fielded equipment.

A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; Lall, Upmanu; Troy, Tara; Devineni, Naresh

2016-10-01

We estimate local and regional Generalized Extreme Value (GEV) distribution parameters for flood frequency analysis in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. As prior information for the model, we assume that the GEV location and scale parameters for each site come from independent log-normal distributions, whose mean parameter scales with the drainage area. From empirical and theoretical arguments, the shape parameter for each site is shrunk towards a common mean. Non-informative prior distributions are assumed for the hyperparameters and the MCMC method is used to sample from the joint posterior distribution. The model is tested using annual maximum series from 20 streamflow gauges located in an 83,000 km2 flood prone basin in Southeast Brazil. The results show a significant reduction of uncertainty estimates of flood quantile estimates over the traditional GEV model, particularly for sites with shorter records. For return periods within the range of the data (around 50 years), the Bayesian credible intervals for the flood quantiles tend to be narrower than the classical confidence limits based on the delta method. As the return period increases beyond the range of the data, the confidence limits from the delta method become unreliable and the Bayesian credible intervals provide a way to estimate satisfactory confidence bands for the flood quantiles considering parameter uncertainties and regional information. In order to evaluate the applicability of the proposed hierarchical Bayesian model for regional flood frequency analysis, we estimate flood quantiles for three randomly chosen out-of-sample sites and compare with classical estimates using the index flood method. The posterior distributions of the scaling law coefficients are used to define the predictive distributions of the GEV location and scale parameters for the out-of-sample sites given only their drainage areas and the posterior distribution of the average shape parameter is taken as the regional predictive distribution for this parameter. While the index flood method does not provide a straightforward way to consider the uncertainties in the index flood and in the regional parameters, the results obtained here show that the proposed Bayesian method is able to produce adequate credible intervals for flood quantiles that are in accordance with empirical estimates.

Positive feedback : exploring current approaches in iterative travel demand model implementation.

DOT National Transportation Integrated Search

2012-01-01

Currently, the models that TxDOTs Transportation Planning and Programming Division (TPP) developed are : traditional three-step models (i.e., trip generation, trip distribution, and traffic assignment) that are sequentially : applied. A limitation...

Personalized technologist dose audit feedback for reducing patient radiation exposure from CT.

PubMed

Miglioretti, Diana L; Zhang, Yue; Johnson, Eric; Lee, Choonsik; Morin, Richard L; Vanneman, Nicholas; Smith-Bindman, Rebecca

2014-03-01

The aim of this study was to determine whether providing radiologic technologists with audit feedback on doses from CT examinations they conduct and education on dose-reduction strategies reduces patients' radiation exposure. This prospective, controlled pilot study was conducted within an integrated health care system from November 2010 to October 2011. Ten technologists at 2 facilities received personalized dose audit reports and education on dose-reduction strategies; 9 technologists at a control facility received no intervention. Radiation exposure was measured by the dose-length product (DLP) from CT scans performed before (n = 1,630) and after (n = 1,499) the intervention and compared using quantile regression. Technologists were surveyed before and after the intervention. For abdominal CT, DLPs decreased by 3% to 12% at intervention facilities but not at the control facility. For brain CT, DLPs significantly decreased by 7% to 12% at one intervention facility; did not change at the second intervention facility, which had the lowest preintervention DLPs; and increased at the control facility. Technologists were more likely to report always thinking about radiation exposure and associated cancer risk and optimizing settings to reduce exposure after the intervention. Personalized audit feedback and education can change technologists' attitudes about, and awareness of, radiation and can lower patient radiation exposure from CT imaging. Copyright © 2014 American College of Radiology. All rights reserved.

Spatio-temporal analysis of the extreme precipitation by the L-moment-based index-flood method in the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Yin, Yixing; Chen, Haishan; Xu, Chongyu; Xu, Wucheng; Chen, Changchun

2014-05-01

The regionalization methods which 'trade space for time' by including several at-site data records in the frequency analysis are an efficient tool to improve the reliability of extreme quantile estimates. With the main aims of improving the understanding of the regional frequency of extreme precipitation and providing scientific and practical background and assistance in formulating the regional development strategies for water resources management in one of the most developed and flood-prone regions in China, the Yangtze River Delta (YRD) region, in this paper, L-moment-based index-flood (LMIF) method, one of the popular regionalization methods, is used in the regional frequency analysis of extreme precipitation; attention was paid to inter-site dependence and its influence on the accuracy of quantile estimates, which hasn't been considered for most of the studies using LMIF method. Extensive data screening of stationarity, serial dependence and inter-site dependence was carried out first. The entire YRD region was then categorized into four homogeneous regions through cluster analysis and homogenous analysis. Based on goodness-of-fit statistic and L-moment ratio diagrams, Generalized extreme-value (GEV) and Generalized Normal (GNO) distributions were identified as the best-fit distributions for most of the sub regions. Estimated quantiles for each region were further obtained. Monte-Carlo simulation was used to evaluate the accuracy of the quantile estimates taking inter-site dependence into consideration. The results showed that the root mean square errors (RMSEs) were bigger and the 90% error bounds were wider with inter-site dependence than those with no inter-site dependence for both the regional growth curve and quantile curve. The spatial patterns of extreme precipitation with return period of 100 years were obtained which indicated that there are two regions with the highest precipitation extremes (southeastern coastal area of Zhejiang Province and the southwest part of Anhui Province) and a large region with low precipitation extremes in the north and middle parts of Zhejiang Province, Shanghai City and Jiangsu Province. However, the central areas with low precipitation extremes are the most developed and densely populated regions in the study area, thus floods will cause great loss of human life and property damage. These findings will contribute to formulating the regional development strategies for policymakers and stakeholders in water resource management against the menaces of frequently emerged floods.

Interpolating Non-Parametric Distributions of Hourly Rainfall Intensities Using Random Mixing

NASA Astrophysics Data System (ADS)

Mosthaf, Tobias; Bárdossy, András; Hörning, Sebastian

2015-04-01

The correct spatial interpolation of hourly rainfall intensity distributions is of great importance for stochastical rainfall models. Poorly interpolated distributions may lead to over- or underestimation of rainfall and consequently to wrong estimates of following applications, like hydrological or hydraulic models. By analyzing the spatial relation of empirical rainfall distribution functions, a persistent order of the quantile values over a wide range of non-exceedance probabilities is observed. As the order remains similar, the interpolation weights of quantile values for one certain non-exceedance probability can be applied to the other probabilities. This assumption enables the use of kernel smoothed distribution functions for interpolation purposes. Comparing the order of hourly quantile values over different gauges with the order of their daily quantile values for equal probabilities, results in high correlations. The hourly quantile values also show high correlations with elevation. The incorporation of these two covariates into the interpolation is therefore tested. As only positive interpolation weights for the quantile values assure a monotonically increasing distribution function, the use of geostatistical methods like kriging is problematic. Employing kriging with external drift to incorporate secondary information is not applicable. Nonetheless, it would be fruitful to make use of covariates. To overcome this shortcoming, a new random mixing approach of spatial random fields is applied. Within the mixing process hourly quantile values are considered as equality constraints and correlations with elevation values are included as relationship constraints. To profit from the dependence of daily quantile values, distribution functions of daily gauges are used to set up lower equal and greater equal constraints at their locations. In this way the denser daily gauge network can be included in the interpolation of the hourly distribution functions. The applicability of this new interpolation procedure will be shown for around 250 hourly rainfall gauges in the German federal state of Baden-Württemberg. The performance of the random mixing technique within the interpolation is compared to applicable kriging methods. Additionally, the interpolation of kernel smoothed distribution functions is compared with the interpolation of fitted parametric distributions.

A Study on Regional Frequency Analysis using Artificial Neural Network - the Sumjin River Basin

NASA Astrophysics Data System (ADS)

Jeong, C.; Ahn, J.; Ahn, H.; Heo, J. H.

2017-12-01

Regional frequency analysis means to make up for shortcomings in the at-site frequency analysis which is about a lack of sample size through the regional concept. Regional rainfall quantile depends on the identification of hydrologically homogeneous regions, hence the regional classification based on hydrological homogeneous assumption is very important. For regional clustering about rainfall, multidimensional variables and factors related geographical features and meteorological figure are considered such as mean annual precipitation, number of days with precipitation in a year and average maximum daily precipitation in a month. Self-Organizing Feature Map method which is one of the artificial neural network algorithm in the unsupervised learning techniques solves N-dimensional and nonlinear problems and be shown results simply as a data visualization technique. In this study, for the Sumjin river basin in South Korea, cluster analysis was performed based on SOM method using high-dimensional geographical features and meteorological factor as input data. then, for the results, in order to evaluate the homogeneity of regions, the L-moment based discordancy and heterogeneity measures were used. Rainfall quantiles were estimated as the index flood method which is one of regional rainfall frequency analysis. Clustering analysis using SOM method and the consequential variation in rainfall quantile were analyzed. This research was supported by a grant(2017-MPSS31-001) from Supporting Technology Development Program for Disaster Management funded by Ministry of Public Safety and Security(MPSS) of the Korean government.

A gentle introduction to quantile regression for ecologists

USGS Publications Warehouse

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

Solvency II solvency capital requirement for life insurance companies based on expected shortfall.

PubMed

Boonen, Tim J

2017-01-01

This paper examines the consequences for a life annuity insurance company if the solvency II solvency capital requirements (SCR) are calibrated based on expected shortfall (ES) instead of value-at-risk (VaR). We focus on the risk modules of the SCRs for the three risk classes equity risk, interest rate risk and longevity risk. The stress scenarios are determined using the calibration method proposed by EIOPA in 2014. We apply the stress-scenarios for these three risk classes to a fictitious life annuity insurance company. We find that for EIOPA's current quantile 99.5% of the VaR, the stress scenarios of the various risk classes based on ES are close to the stress scenarios based on VaR. Might EIOPA choose to calibrate the stress scenarios on a smaller quantile, the longevity SCR is relatively larger and the equity SCR is relatively smaller if ES is used instead of VaR. We derive the same conclusion if stress scenarios are determined with empirical stress scenarios.

Non-inferiority tests for anti-infective drugs using control group quantiles.

PubMed

Fay, Michael P; Follmann, Dean A

2016-12-01

In testing for non-inferiority of anti-infective drugs, the primary endpoint is often the difference in the proportion of failures between the test and control group at a landmark time. The landmark time is chosen to approximately correspond to the qth historic quantile of the control group, and the non-inferiority margin is selected to be reasonable for the target level q. For designing these studies, a troubling issue is that the landmark time must be pre-specified, but there is no guarantee that the proportion of control failures at the landmark time will be close to the target level q. If the landmark time is far from the target control quantile, then the pre-specified non-inferiority margin may not longer be reasonable. Exact variable margin tests have been developed by Röhmel and Kieser to address this problem, but these tests can have poor power if the observed control failure rate at the landmark time is far from its historic value. We develop a new variable margin non-inferiority test where we continue sampling until a pre-specified proportion of failures, q, have occurred in the control group, where q is the target quantile level. The test does not require any assumptions on the failure time distributions, and hence, no knowledge of the true [Formula: see text] control quantile for the study is needed. Our new test is exact and has power comparable to (or greater than) its competitors when the true control quantile from the study equals (or differs moderately from) its historic value. Our nivm R package performs the test and gives confidence intervals on the difference in failure rates at the true target control quantile. The tests can be applied to time to cure or other numeric variables as well. A substantial proportion of new anti-infective drugs being developed use non-inferiority tests in their development, and typically, a pre-specified landmark time and its associated difference margin are set at the design stage to match a specific target control quantile. If through changing standard of care or selection of a different population the target quantile for the control group changes from its historic value, then the appropriateness of the pre-specified margin at the landmark time may be questionable. Our proposed test avoids this problem by sampling until a pre-specified proportion of the controls have failed. © The Author(s) 2016.

Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

PubMed Central

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916

Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

PubMed

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

Quantiles for Finite Mixtures of Normal Distributions

ERIC Educational Resources Information Center

Rahman, Mezbahur; Rahman, Rumanur; Pearson, Larry M.

2006-01-01

Quantiles for finite mixtures of normal distributions are computed. The difference between a linear combination of independent normal random variables and a linear combination of independent normal densities is emphasized. (Contains 3 tables and 1 figure.)

Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations

NASA Astrophysics Data System (ADS)

Mehran, A.; AghaKouchak, A.; Phillips, T. J.

2014-02-01

The objective of this study is to cross-validate 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of precipitation against the Global Precipitation Climatology Project (GPCP) data, quantifying model pattern discrepancies, and biases for both entire distributions and their upper tails. The results of the volumetric hit index (VHI) analysis of the total monthly precipitation amounts show that most CMIP5 simulations are in good agreement with GPCP patterns in many areas but that their replication of observed precipitation over arid regions and certain subcontinental regions (e.g., northern Eurasia, eastern Russia, and central Australia) is problematical. Overall, the VHI of the multimodel ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (75th and 90th percentiles), all climate models display low skill in simulating precipitation, except over North America, the Amazon, and Central Africa. Analyses of total bias (B) in CMIP5 simulations reveal that most models overestimate precipitation over regions of complex topography (e.g., western North and South America and southern Africa and Asia), while underestimating it over arid regions. Also, while most climate model simulations show low biases over Europe, intermodel variations in bias over Australia and Amazonia are considerable. The quantile bias analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. It is found that a simple mean field bias removal improves the overall B and VHI values but does not make a significant improvement at high quantiles of precipitation.

Environmental determinants of different blood lead levels in children: a quantile analysis from a nationwide survey.

PubMed

Etchevers, Anne; Le Tertre, Alain; Lucas, Jean-Paul; Bretin, Philippe; Oulhote, Youssef; Le Bot, Barbara; Glorennec, Philippe

2015-01-01

Blood lead levels (BLLs) have substantially decreased in recent decades in children in France. However, further reducing exposure is a public health goal because there is no clear toxicological threshold. The identification of the environmental determinants of BLLs as well as risk factors associated with high BLLs is important to update prevention strategies. We aimed to estimate the contribution of environmental sources of lead to different BLLs in children in France. We enrolled 484 children aged from 6months to 6years, in a nationwide cross-sectional survey in 2008-2009. We measured lead concentrations in blood and environmental samples (water, soils, household settled dusts, paints, cosmetics and traditional cookware). We performed two models: a multivariate generalized additive model on the geometric mean (GM), and a quantile regression model on the 10th, 25th, 50th, 75th and 90th quantile of BLLs. The GM of BLLs was 13.8μg/L (=1.38μg/dL) (95% confidence intervals (CI): 12.7-14.9) and the 90th quantile was 25.7μg/L (CI: 24.2-29.5). Household and common area dust, tap water, interior paint, ceramic cookware, traditional cosmetics, playground soil and dust, and environmental tobacco smoke were associated with the GM of BLLs. Household dust and tap water made the largest contributions to both the GM and the 90th quantile of BLLs. The concentration of lead in dust was positively correlated with all quantiles of BLLs even at low concentrations. Lead concentrations in tap water above 5μg/L were also positively correlated with the GM, 75th and 90th quantiles of BLLs in children drinking tap water. Preventative actions must target household settled dust and tap water to reduce the BLLs of children in France. The use of traditional cosmetics should be avoided whereas ceramic cookware should be limited to decorative purposes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Exploration and Exploitation During Sequential Search

PubMed Central

Dam, Gregory; Körding, Konrad

2012-01-01

When we learn how to throw darts we adjust how we throw based on where the darts stick. Much of skill learning is computationally similar in that we learn using feedback obtained after the completion of individual actions. We can formalize such tasks as a search problem; among the set of all possible actions, find the action that leads to the highest reward. In such cases our actions have two objectives: we want to best utilize what we already know (exploitation), but we also want to learn to be more successful in the future (exploration). Here we tested how participants learn movement trajectories where feedback is provided as a monetary reward that depends on the chosen trajectory. We mathematically derived the optimal search policy for our experiment using decision theory. The search behavior of participants is well predicted by an ideal searcher model that optimally combines exploration and exploitation. PMID:21585479

Development and Application of a Stepwise Assessment Process for Rational Redesign of Sequential Skills-Based Courses.

PubMed

Gallimore, Casey E; Porter, Andrea L; Barnett, Susanne G

2016-10-25

Objective. To develop and apply a stepwise process to assess achievement of course learning objectives related to advanced pharmacy practice experiences (APPEs) preparedness and inform redesign of sequential skills-based courses. Design. Four steps comprised the assessment and redesign process: (1) identify skills critical for APPE preparedness; (2) utilize focus groups and course evaluations to determine student competence in skill performance; (3) apply course mapping to identify course deficits contributing to suboptimal skill performance; and (4) initiate course redesign to target exposed deficits. Assessment. Focus group participants perceived students were least prepared for skills within the Accreditation Council for Pharmacy Education's pre-APPE core domains of Identification and Assessment of Drug-related Problems and General Communication Abilities. Course mapping identified gaps in instruction, performance, and assessment of skills within aforementioned domains. Conclusions. A stepwise process that identified strengths and weaknesses of a course, was used to facilitate structured course redesign. Strengths of the process included input and corroboration from both preceptors and students. Limitations included feedback from a small number of pharmacy preceptors and increased workload on course coordinators.

L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals.

PubMed

Assaad, Houssein I; Choudhary, Pankaj K

2013-01-01

The L -statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L -statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L -statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation.

A quantile regression model for failure-time data with time-dependent covariates

PubMed Central

Gorfine, Malka; Goldberg, Yair; Ritov, Ya’acov

2017-01-01

Summary Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This article provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables. In addition, we present a doubly-robust estimator in the sense of Robins and Rotnitzky (1992, Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell, N. P., Dietz, K. and Farewell, V. T. (editors), AIDS Epidemiology. Boston: Birkhaäuser, pp. 297–331.). The asymptotic properties of the estimators are rigorously studied. Finite-sample properties are demonstrated by a simulation study. The utility of the proposed methodology is demonstrated using the Stanford heart transplant dataset. PMID:27485534

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

NASA Astrophysics Data System (ADS)

Schwanghart, Wolfgang; Scherler, Dirk

2017-12-01

The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms - quantile carving and the CRS algorithm - that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12 m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.

Quantification of Uncertainty in the Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.

2017-12-01

Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Association between the Infant and Child Feeding Index (ICFI) and nutritional status of 6- to 35-month-old children in rural western China.

PubMed

Qu, Pengfei; Mi, Baibing; Wang, Duolao; Zhang, Ruo; Yang, Jiaomei; Liu, Danmeng; Dang, Shaonong; Yan, Hong

2017-01-01

The objective of this study was to determine the relationship between the quality of feeding practices and children's nutritional status in rural western China. A sample of 12,146 pairs of 6- to 35-month-old children and their mothers were recruited using stratified multistage cluster random sampling in rural western China. Quantile regression was used to analyze the relationship between the Infant and Child Feeding Index (ICFI) and children's nutritional status. In rural western China, 24.37% of all infants and young children suffer from malnutrition. Of this total, 19.57%, 8.74% and 4.63% of infants and children are classified as stunting, underweight and wasting, respectively. After adjusting for covariates, the quantile regression results suggested that qualified ICFI (ICFI > 13.8) was associated with all length and HAZ quantiles (P<0.05) and had a greater effect on the following: poor length and HAZ, the β-estimates (length) from 0.76 cm (95% CI: 0.53 to 0.99 cm) to 0.34 cm (95% CI: 0.09 to 0.59 cm) and the β-estimates (HAZ) from 0.17 (95% CI: 0.10 to 0.24) to 0.11 (95% CI: 0.04 to 0.19). Qualified ICFI was also associated with most weight quantiles (P<0.05 except the 80th and 90th quantiles) and poor and intermediate WAZ quantiles (P<0.05 including the 10th, 20th 30th and 40th quantiles). Additionally, qualified ICFI had a greater effect on poor weight and WAZ quantiles in which the β-estimates (weight) were from 0.20 kg (95% CI: 0.14 to 0.26 kg) to 0.06 kg (95% CI: 0.00 to 0.12 kg) and the β-estimates (WAZ) were from 0.14 (95% CI: 0.08 to 0.21) to 0.05 (95% CI: 0.01 to 0.10). Feeding practices were associated with the physical development of infants and young children, and proper feeding practices had a greater effect on poor physical development in infants and young children. For mothers in rural western China, proper guidelines and messaging on complementary feeding practices are necessary.

[Spatial heterogeneity in body condition of small yellow croaker in Yellow Sea and East China Sea based on mixed-effects model and quantile regression analysis].

PubMed

Liu, Zun-Lei; Yuan, Xing-Wei; Yan, Li-Ping; Yang, Lin-Lin; Cheng, Jia-Hua

2013-09-01

By using the 2008-2010 investigation data about the body condition of small yellow croaker in the offshore waters of southern Yellow Sea (SYS), open waters of northern East China Sea (NECS), and offshore waters of middle East China Sea (MECS), this paper analyzed the spatial heterogeneity of body length-body mass of juvenile and adult small yellow croakers by the statistical approaches of mean regression model and quantile regression model. The results showed that the residual standard errors from the analysis of covariance (ANCOVA) and the linear mixed-effects model were similar, and those from the simple linear regression were the highest. For the juvenile small yellow croakers, their mean body mass in SYS and NECS estimated by the mixed-effects mean regression model was higher than the overall average mass across the three regions, while the mean body mass in MECS was below the overall average. For the adult small yellow croakers, their mean body mass in NECS was higher than the overall average, while the mean body mass in SYS and MECS was below the overall average. The results from quantile regression indicated the substantial differences in the allometric relationships of juvenile small yellow croakers between SYS, NECS, and MECS, with the estimated mean exponent of the allometric relationship in SYS being 2.85, and the interquartile range being from 2.63 to 2.96, which indicated the heterogeneity of body form. The results from ANCOVA showed that the allometric body length-body mass relationships were significantly different between the 25th and 75th percentile exponent values (F=6.38, df=1737, P<0.01) and the 25th percentile and median exponent values (F=2.35, df=1737, P=0.039). The relationship was marginally different between the median and 75th percentile exponent values (F=2.21, df=1737, P=0.051). The estimated body length-body mass exponent of adult small yellow croakers in SYS was 3.01 (10th and 95th percentiles = 2.77 and 3.1, respectively). The estimated body length-body mass relationships were significantly different from the lower and upper quantiles of the exponent (F=3.31, df=2793, P=0.01) and the median and upper quantiles (F=3.56, df=2793, P<0.01), while no significant difference was observed between the lower and median quantiles (F=0.98, df=2793, P=0.43).

Spline methods for approximating quantile functions and generating random samples

NASA Technical Reports Server (NTRS)

Schiess, J. R.; Matthews, C. G.

1985-01-01

Two cubic spline formulations are presented for representing the quantile function (inverse cumulative distribution function) of a random sample of data. Both B-spline and rational spline approximations are compared with analytic representations of the quantile function. It is also shown how these representations can be used to generate random samples for use in simulation studies. Comparisons are made on samples generated from known distributions and a sample of experimental data. The spline representations are more accurate for multimodal and skewed samples and to require much less time to generate samples than the analytic representation.

Intersection of All Top Quantile

EPA Pesticide Factsheets

This layer combines the Top quantiles of the CES, CEVA, and EJSM layers so that viewers can see the overlap of 00e2??hot spots00e2?? for each method. This layer was created by James Sadd of Occidental College of Los Angeles

Intersection of Screening Methods High Quantile

EPA Pesticide Factsheets

This layer combines the high quantiles of the CES, CEVA, and EJSM layers so that viewers can see the overlap of 00e2??hot spots00e2?? for each method. This layer was created by James Sadd of Occidental College of Los Angeles

Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations

DOE PAGES

Mehran, Ali; AghaKouchak, Amir; Phillips, Thomas J.

2014-02-25

Numerous studies have emphasized that climate simulations are subject to various biases and uncertainties. The objective of this study is to cross-validate 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of precipitation against the Global Precipitation Climatology Project (GPCP) data, quantifying model pattern discrepancies and biases for both entire data distributions and their upper tails. The results of the Volumetric Hit Index (VHI) analysis of the total monthly precipitation amounts show that most CMIP5 simulations are in good agreement with GPCP patterns in many areas, but that their replication of observed precipitation over arid regions and certain sub-continentalmore » regions (e.g., northern Eurasia, eastern Russia, central Australia) is problematical. Overall, the VHI of the multi-model ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (e.g., the 75th and 90th percentiles), all climate models display low skill in simulating precipitation, except over North America, the Amazon, and central Africa. Analyses of total bias (B) in CMIP5 simulations reveal that most models overestimate precipitation over regions of complex topography (e.g. western North and South America and southern Africa and Asia), while underestimating it over arid regions. Also, while most climate model simulations show low biases over Europe, inter-model variations in bias over Australia and Amazonia are considerable. The Quantile Bias (QB) analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. Lastly, we found that a simple mean-field bias removal improves the overall B and VHI values, but does not make a significant improvement in these model performance metrics at high quantiles of precipitation.« less

Socio-demographic, clinical characteristics and utilization of mental health care services associated with SF-6D utility scores in patients with mental disorders: contributions of the quantile regression.

PubMed

Prigent, Amélie; Kamendje-Tchokobou, Blaise; Chevreul, Karine

2017-11-01

Health-related quality of life (HRQoL) is a widely used concept in the assessment of health care. Some generic HRQoL instruments, based on specific algorithms, can generate utility scores which reflect the preferences of the general population for the different health states described by the instrument. This study aimed to investigate the relationships between utility scores and potentially associated factors in patients with mental disorders followed in inpatient and/or outpatient care settings using two statistical methods. Patients were recruited in four psychiatric sectors in France. Patient responses to the SF-36 generic HRQoL instrument were used to calculate SF-6D utility scores. The relationships between utility scores and patient socio-demographic, clinical characteristics, and mental health care utilization, considered as potentially associated factors, were studied using OLS and quantile regressions. One hundred and seventy six patients were included. Women, severely ill patients and those hospitalized full-time tended to report lower utility scores, whereas psychotic disorders (as opposed to mood disorders) and part-time care were associated with higher scores. The quantile regression highlighted that the size of the associations between the utility scores and some patient characteristics varied along with the utility score distribution, and provided more accurate estimated values than OLS regression. The quantile regression may constitute a relevant complement for the analysis of factors associated with utility scores. For policy decision-making, the association of full-time hospitalization with lower utility scores while part-time care was associated with higher scores supports the further development of alternatives to full-time hospitalizations.

Differential effects of dietary diversity and maternal characteristics on linear growth of children aged 6-59 months in sub-Saharan Africa: a multi-country analysis.

PubMed

Amugsi, Dickson A; Dimbuene, Zacharie T; Kimani-Murage, Elizabeth W; Mberu, Blessing; Ezeh, Alex C

2017-04-01

To investigate the differential effects of dietary diversity (DD) and maternal characteristics on child linear growth at different points of the conditional distribution of height-for-age Z-score (HAZ) in sub-Saharan Africa. Secondary analysis of data from nationally representative cross-sectional samples of singleton children aged 0-59 months, born to mothers aged 15-49 years. The outcome variable was child HAZ. Quantile regression was used to perform the multivariate analysis. The most recent Demographic and Health Surveys from Ghana, Nigeria, Kenya, Mozambique and Democratic Republic of Congo (DRC). The present analysis was restricted to children aged 6-59 months (n 31 604). DD was associated positively with HAZ in the first four quantiles (5th, 10th, 25th and 50th) and the highest quantile (90th) in Nigeria. The largest effect occurred at the very bottom (5th quantile) and the very top (90th quantile) of the conditional HAZ distribution. In DRC, DD was significantly and positively associated with HAZ in the two lower quantiles (5th, 10th). The largest effects of maternal education occurred at the lower end of the conditional HAZ distribution in Ghana, Nigeria and DRC. Maternal BMI and height also had positive effects on HAZ at different points of the conditional distribution of HAZ. Our analysis shows that the association between DD and maternal factors and HAZ differs along the conditional HAZ distribution. Intervention measures need to take into account the heterogeneous effect of the determinants of child nutritional status along the different percentiles of the HAZ distribution.

Influences of spatial and temporal variation on fish-habitat relationships defined by regression quantiles

USGS Publications Warehouse

Dunham, J.B.; Cade, B.S.; Terrell, J.W.

2002-01-01

We used regression quantiles to model potentially limiting relationships between the standing crop of cutthroat trout Oncorhynchus clarki and measures of stream channel morphology. Regression quantile models indicated that variation in fish density was inversely related to the width:depth ratio of streams but not to stream width or depth alone. The spatial and temporal stability of model predictions were examined across years and streams, respectively. Variation in fish density with width:depth ratio (10th-90th regression quantiles) modeled for streams sampled in 1993-1997 predicted the variation observed in 1998-1999, indicating similar habitat relationships across years. Both linear and nonlinear models described the limiting relationships well, the latter performing slightly better. Although estimated relationships were transferable in time, results were strongly dependent on the influence of spatial variation in fish density among streams. Density changes with width:depth ratio in a single stream were responsible for the significant (P < 0.10) negative slopes estimated for the higher quantiles (>80th). This suggests that stream-scale factors other than width:depth ratio play a more direct role in determining population density. Much of the variation in densities of cutthroat trout among streams was attributed to the occurrence of nonnative brook trout Salvelinus fontinalis (a possible competitor) or connectivity to migratory habitats. Regression quantiles can be useful for estimating the effects of limiting factors when ecological responses are highly variable, but our results indicate that spatiotemporal variability in the data should be explicitly considered. In this study, data from individual streams and stream-specific characteristics (e.g., the occurrence of nonnative species and habitat connectivity) strongly affected our interpretation of the relationship between width:depth ratio and fish density.

Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau

USGS Publications Warehouse

Nauman, Travis; Duniway, Michael C.; Villarreal, Miguel; Poitras, Travis

2017-01-01

A new disturbance automated reference toolset (DART) was developed to monitor human land surface impacts using soil-type and ecological context. DART identifies reference areas with similar soils, topography, and geology; and compares the disturbance condition to the reference area condition using a quantile-based approach based on a satellite vegetation index. DART was able to represent 26–55% of variation of relative differences in bare ground and 26–41% of variation in total foliar cover when comparing sites with nearby ecological reference areas using the Soil Adjusted Total Vegetation Index (SATVI). Assessment of ecological recovery at oil and gas pads on the Colorado Plateau with DART revealed that more than half of well-pads were below the 25th percentile of reference areas. Machine learning trend analysis of poorly recovering well-pads (quantile < 0.23) had out-of-bag error rates between 37 and 40% indicating moderate association with environmental and management variables hypothesized to influence recovery. Well-pads in grasslands (median quantile [MQ] = 13%), blackbrush (Coleogyne ramosissima) shrublands (MQ = 18%), arid canyon complexes (MQ = 18%), warmer areas with more summer-dominated precipitation, and state administered areas (MQ = 12%) had low recovery rates. Results showcase the usefulness of DART for assessing discrete surface land disturbances, and highlight the need for more targeted rehabilitation efforts at oil and gas well-pads in the arid southwest US.

Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau.

PubMed

Nauman, Travis W; Duniway, Michael C; Villarreal, Miguel L; Poitras, Travis B

2017-04-15

A new disturbance automated reference toolset (DART) was developed to monitor human land surface impacts using soil-type and ecological context. DART identifies reference areas with similar soils, topography, and geology; and compares the disturbance condition to the reference area condition using a quantile-based approach based on a satellite vegetation index. DART was able to represent 26-55% of variation of relative differences in bare ground and 26-41% of variation in total foliar cover when comparing sites with nearby ecological reference areas using the Soil Adjusted Total Vegetation Index (SATVI). Assessment of ecological recovery at oil and gas pads on the Colorado Plateau with DART revealed that more than half of well-pads were below the 25th percentile of reference areas. Machine learning trend analysis of poorly recovering well-pads (quantile<0.23) had out-of-bag error rates between 37 and 40% indicating moderate association with environmental and management variables hypothesized to influence recovery. Well-pads in grasslands (median quantile [MQ]=13%), blackbrush (Coleogyne ramosissima) shrublands (MQ=18%), arid canyon complexes (MQ=18%), warmer areas with more summer-dominated precipitation, and state administered areas (MQ=12%) had low recovery rates. Results showcase the usefulness of DART for assessing discrete surface land disturbances, and highlight the need for more targeted rehabilitation efforts at oil and gas well-pads in the arid southwest US. Published by Elsevier B.V.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities.

PubMed

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the "S&P 500 1987" bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Probabilistic forecasting for extreme NO2 pollution episodes.

PubMed

Aznarte, José L

2017-10-01

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO 2 . Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO 2 concentrations as well as meteorological measures, we build models that predict extreme NO 2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of different hydrological similarity measures to estimate flow quantiles

NASA Astrophysics Data System (ADS)

Rianna, M.; Ridolfi, E.; Napolitano, F.

2017-07-01

This paper aims to evaluate the influence of hydrological similarity measures on the definition of homogeneous regions. To this end, several attribute sets have been analyzed in the context of the Region of Influence (ROI) procedure. Several combinations of geomorphological, climatological, and geographical characteristics are also used to cluster potentially homogeneous regions. To verify the goodness of the resulting pooled sites, homogeneity tests arecarried out. Through a Monte Carlo simulation and a jack-knife procedure, flow quantiles areestimated for the regions effectively resulting as homogeneous. The analysis areperformed in both the so-called gauged and ungauged scenarios to analyze the effect of hydrological measures on flow quantiles estimation.

Quantile regression in the presence of monotone missingness with sensitivity analysis

PubMed Central

Liu, Minzhao; Daniels, Michael J.; Perri, Michael G.

2016-01-01

In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management. PMID:26041008

University of Iowa at TREC 2008 Legal and Relevance Feedback Tracks

DTIC Science & Technology

2008-11-01

Fellbaum, C, [ed.]. Wordnet: An Electronic Lexical Database. Cambridge : MIT Press, 1998. [3] Salton , G. (ed) (1971), The SMART Retrieval System...learning tools and techniques. 2nd Edition. San Francisco : Morgan Kaufmann, 2005. [5] Platt, J . Machines using Sequential Minimal Optimization. [ed.] B

Quantile Regression with Censored Data

ERIC Educational Resources Information Center

Lin, Guixian

2009-01-01

The Cox proportional hazards model and the accelerated failure time model are frequently used in survival data analysis. They are powerful, yet have limitation due to their model assumptions. Quantile regression offers a semiparametric approach to model data with possible heterogeneity. It is particularly powerful for censored responses, where the…

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Effect of feedback mode and task difficulty on quality of timing decisions in a zero-sum game.

PubMed

Tikuisis, Peter; Vartanian, Oshin; Mandel, David R

2014-09-01

The objective was to investigate the interaction between the mode of performance outcome feedback and task difficulty on timing decisions (i.e., when to act). Feedback is widely acknowledged to affect task performance. However, the extent to which feedback display mode and its impact on timing decisions is moderated by task difficulty remains largely unknown. Participants repeatedly engaged a zero-sum game involving silent duels with a computerized opponent and were given visual performance feedback after each engagement. They were sequentially tested on three different levels of task difficulty (low, intermediate, and high) in counterbalanced order. Half received relatively simple "inside view" binary outcome feedback, and the other half received complex "outside view" hit rate probability feedback. The key dependent variables were response time (i.e., time taken to make a decision) and survival outcome. When task difficulty was low to moderate, participants were more likely to learn and perform better from hit rate probability feedback than binary outcome feedback. However, better performance with hit rate feedback exacted a higher cognitive cost manifested by higher decision response time. The beneficial effect of hit rate probability feedback on timing decisions is partially moderated by task difficulty. Performance feedback mode should be judiciously chosen in relation to task difficulty for optimal performance in tasks involving timing decisions.

Measuring disparities across the distribution of mental health care expenditures.

PubMed

Le Cook, Benjamin; Manning, Willard; Alegria, Margarita

2013-03-01

Previous mental health care disparities studies predominantly compare mean mental health care use across racial/ethnic groups, leaving policymakers with little information on disparities among those with a higher level of expenditures. To identify racial/ethnic disparities among individuals at varying quantiles of mental health care expenditures. To assess whether disparities in the upper quantiles of expenditure differ by insurance status, income and education. Data were analyzed from a nationally representative sample of white, black and Latino adults 18 years and older (n=83,878). Our dependent variable was total mental health care expenditure. We measured disparities in any mental health care expenditures, disparities in mental health care expenditure at the 95th, 97.5 th, and 99 th expenditure quantiles of the full population using quantile regression, and at the 50 th, 75 th, and 95 th quantiles for positive users. In the full population, we tested interaction coefficients between race/ethnicity and income, insurance, and education levels to determine whether racial/ethnic disparities in the upper quantiles differed by income, insurance and education. Significant Black-white and Latino-white disparities were identified in any mental health care expenditures. In the full population, moving up the quantiles of mental health care expenditures, Black-White and Latino-White disparities were reduced but remained statistically significant. No statistically significant disparities were found in analyses of positive users only. The magnitude of black-white disparities was smaller among those enrolled in public insurance programs compared to the privately insured and uninsured in the 97.5 th and 99 th quantiles. Disparities persist in the upper quantiles among those in higher income categories and after excluding psychiatric inpatient and emergency department (ED) visits. Disparities exist in any mental health care and among those that use the most mental health care resources, but much of disparities seem to be driven by lack of access. The data do not allow us to disentangle whether disparities were related to white respondent's overuse or underuse as compared to minority groups. The cross-sectional data allow us to make only associational claims about the role of insurance, income, and education in disparities. With these limitations in mind, we identified a persistence of disparities in overall expenditures even among those in the highest income categories, after controlling for mental health status and observable sociodemographic characteristics. Interventions are needed to equalize resource allocation to racial/ethnic minority patients regardless of their income, with emphasis on outreach interventions to address the disparities in access that are responsible for the no/low expenditures for even Latinos at higher levels of illness severity. Increased policy efforts are needed to reduce the gap in health insurance for Latinos and improve outreach programs to enroll those in need into mental health care services. Future studies that conclusively disentangle overuse and appropriate use in these populations are warranted.

Analysis of regional natural flow for evaluation of flood risk according to RCP climate change scenarios

NASA Astrophysics Data System (ADS)

Lee, J. Y.; Chae, B. S.; Wi, S.; KIm, T. W.

2017-12-01

Various climate change scenarios expect the rainfall in South Korea to increase by 3-10% in the future. The future increased rainfall has significant effect on the frequency of flood in future as well. This study analyzed the probability of future flood to investigate the stability of existing and new installed hydraulic structures and the possibility of increasing flood damage in mid-sized watersheds in South Korea. To achieve this goal, we first clarified the relationship between flood quantiles acquired from the flood-frequency analysis (FFA) and design rainfall-runoff analysis (DRRA) in gauged watersheds. Then, after synthetically generating the regional natural flow data according to RCP climate change scenarios, we developed mathematical formulas to estimate future flood quantiles based on the regression between DRRA and FFA incorporated with regional natural flows in unguaged watersheds. Finally, we developed a flood risk map to investigate the change of flood risk in terms of the return period for the past, present, and future. The results identified that the future flood quantiles and risks would increase in accordance with the RCP climate change scenarios. Because the regional flood risk was identified to increase in future comparing with the present status, comprehensive flood control will be needed to cope with extreme floods in future.

Regional maximum rainfall analysis using L-moments at the Titicaca Lake drainage, Peru

NASA Astrophysics Data System (ADS)

Fernández-Palomino, Carlos Antonio; Lavado-Casimiro, Waldo Sven

2017-08-01

The present study investigates the application of the index flood L-moments-based regional frequency analysis procedure (RFA-LM) to the annual maximum 24-h rainfall (AM) of 33 rainfall gauge stations (RGs) to estimate rainfall quantiles at the Titicaca Lake drainage (TL). The study region was chosen because it is characterised by common floods that affect agricultural production and infrastructure. First, detailed quality analyses and verification of the RFA-LM assumptions were conducted. For this purpose, different tests for outlier verification, homogeneity, stationarity, and serial independence were employed. Then, the application of RFA-LM procedure allowed us to consider the TL as a single, hydrologically homogeneous region, in terms of its maximum rainfall frequency. That is, this region can be modelled by a generalised normal (GNO) distribution, chosen according to the Z test for goodness-of-fit, L-moments (LM) ratio diagram, and an additional evaluation of the precision of the regional growth curve. Due to the low density of RG in the TL, it was important to produce maps of the AM design quantiles estimated using RFA-LM. Therefore, the ordinary Kriging interpolation (OK) technique was used. These maps will be a useful tool for determining the different AM quantiles at any point of interest for hydrologists in the region.

Contrasting OLS and Quantile Regression Approaches to Student "Growth" Percentiles

ERIC Educational Resources Information Center

Castellano, Katherine Elizabeth; Ho, Andrew Dean

2013-01-01

Regression methods can locate student test scores in a conditional distribution, given past scores. This article contrasts and clarifies two approaches to describing these locations in terms of readily interpretable percentile ranks or "conditional status percentile ranks." The first is Betebenner's quantile regression approach that results in…

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels.

PubMed

Yu, Hwa-Lung; Wang, Chih-Hsin

2013-02-05

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

Pre-testing Orientation for the Disadvantaged.

ERIC Educational Resources Information Center

Mihalka, Joseph A.

A pre-testing orientation was incorporated into the Work Incentives Program, a pre-vocational program for disadvantaged youth. Test-taking skills were taught in seven and one half hours of instruction and a variety of methods were used to provide a sequential experience with distributed learning, positive reinforcement, and immediate feedback of…

Probability matching in risky choice: the interplay of feedback and strategy availability.

PubMed

Newell, Ben R; Koehler, Derek J; James, Greta; Rakow, Tim; van Ravenzwaaij, Don

2013-04-01

Probability matching in sequential decision making is a striking violation of rational choice that has been observed in hundreds of experiments. Recent studies have demonstrated that matching persists even in described tasks in which all the information required for identifying a superior alternative strategy-maximizing-is present before the first choice is made. These studies have also indicated that maximizing increases when (1) the asymmetry in the availability of matching and maximizing strategies is reduced and (2) normatively irrelevant outcome feedback is provided. In the two experiments reported here, we examined the joint influences of these factors, revealing that strategy availability and outcome feedback operate on different time courses. Both behavioral and modeling results showed that while availability of the maximizing strategy increases the choice of maximizing early during the task, feedback appears to act more slowly to erode misconceptions about the task and to reinforce optimal responding. The results illuminate the interplay between "top-down" identification of choice strategies and "bottom-up" discovery of those strategies via feedback.

Two time scale output feedback regulation for ill-conditioned systems

NASA Technical Reports Server (NTRS)

Calise, A. J.; Moerder, D. D.

1986-01-01

Issues pertaining to the well-posedness of a two time scale approach to the output feedback regulator design problem are examined. An approximate quadratic performance index which reflects a two time scale decomposition of the system dynamics is developed. It is shown that, under mild assumptions, minimization of this cost leads to feedback gains providing a second-order approximation of optimal full system performance. A simplified approach to two time scale feedback design is also developed, in which gains are separately calculated to stabilize the slow and fast subsystem models. By exploiting the notion of combined control and observation spillover suppression, conditions are derived assuring that these gains will stabilize the full-order system. A sequential numerical algorithm is described which obtains output feedback gains minimizing a broad class of performance indices, including the standard LQ case. It is shown that the algorithm converges to a local minimum under nonrestrictive assumptions. This procedure is adapted to and demonstrated for the two time scale design formulations.

Student Growth Percentiles Based on MIRT: Implications of Calibrated Projection. CRESST Report 842

ERIC Educational Resources Information Center

Monroe, Scott; Cai, Li; Choi, Kilchan

2014-01-01

This research concerns a new proposal for calculating student growth percentiles (SGP, Betebenner, 2009). In Betebenner (2009), quantile regression (QR) is used to estimate the SGPs. However, measurement error in the score estimates, which always exists in practice, leads to bias in the QR-­based estimates (Shang, 2012). One way to address this…

Electronic Design Automation: Integrating the Design and Manufacturing Functions

NASA Technical Reports Server (NTRS)

Bachnak, Rafic; Salkowski, Charles

1997-01-01

As the complexity of electronic systems grows, the traditional design practice, a sequential process, is replaced by concurrent design methodologies. A major advantage of concurrent design is that the feedback from software and manufacturing engineers can be easily incorporated into the design. The implementation of concurrent engineering methodologies is greatly facilitated by employing the latest Electronic Design Automation (EDA) tools. These tools offer integrated simulation of the electrical, mechanical, and manufacturing functions and support virtual prototyping, rapid prototyping, and hardware-software co-design. This report presents recommendations for enhancing the electronic design and manufacturing capabilities and procedures at JSC based on a concurrent design methodology that employs EDA tools.

Estimation of peak discharge quantiles for selected annual exceedance probabilities in Northeastern Illinois.

DOT National Transportation Integrated Search

2016-06-01

This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, : 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years,...

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Quantile regression reveals hidden bias and uncertainty in habitat models

Treesearch

Brian S. Cade; Barry R. Noon; Curtis H. Flather

2005-01-01

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias...

Goodness of Fit and Misspecification in Quantile Regressions

ERIC Educational Resources Information Center

Furno, Marilena

2011-01-01

The article considers a test of specification for quantile regressions. The test relies on the increase of the objective function and the worsening of the fit when unnecessary constraints are imposed. It compares the objective functions of restricted and unrestricted models and, in its different formulations, it verifies (a) forecast ability, (b)…

Early Home Activities and Oral Language Skills in Middle Childhood: A Quantile Analysis

ERIC Educational Resources Information Center

Law, James; Rush, Robert; King, Tom; Westrupp, Elizabeth; Reilly, Sheena

2018-01-01

Oral language development is a key outcome of elementary school, and it is important to identify factors that predict it most effectively. Commonly researchers use ordinary least squares regression with conclusions restricted to average performance conditional on relevant covariates. Quantile regression offers a more sophisticated alternative.…

Principles of Quantile Regression and an Application

ERIC Educational Resources Information Center

Chen, Fang; Chalhoub-Deville, Micheline

2014-01-01

Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…

Hospital charges associated with motorcycle crash factors: a quantile regression analysis.

PubMed

Olsen, Cody S; Thomas, Andrea M; Cook, Lawrence J

2014-08-01

Previous studies of motorcycle crash (MC) related hospital charges use trauma registries and hospital records, and do not adjust for the number of motorcyclists not requiring medical attention. This may lead to conservative estimates of helmet use effectiveness. MC records were probabilistically linked with emergency department and hospital records to obtain total hospital charges. Missing data were imputed. Multivariable quantile regression estimated reductions in hospital charges associated with helmet use and other crash factors. Motorcycle helmets were associated with reduced median hospital charges of $256 (42% reduction) and reduced 98th percentile of $32,390 (33% reduction). After adjusting for other factors, helmets were associated with reductions in charges in all upper percentiles studied. Quantile regression models described homogenous and heterogeneous associations between other crash factors and charges. Quantile regression comprehensively describes associations between crash factors and hospital charges. Helmet use among motorcyclists is associated with decreased hospital charges. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

PubMed Central

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs. PMID:27806093

Food away from home and body mass outcomes: taking heterogeneity into account enhances quality of results.

PubMed

Kim, Tae Hyun; Lee, Eui-Kyung; Han, Euna

2014-09-01

The aim of this study was to explore the heterogeneous association of consumption of food away from home (FAFH) with individual body mass outcomes including body mass index and waist circumference over the entire conditional distribution of each outcome. Information on 16,403 adults obtained from nationally representative data on nutrition and behavior in Korea was used. A quantile regression model captured the variability of the association of FAFH with body mass outcomes across the entire conditional distribution of each outcome measure. Heavy FAFH consumption was defined as obtaining ≥1400 kcal from FAFH on a single day. Heavy FAFH consumption, specifically at full-service restaurants, was significantly associated with higher body mass index (+0.46 kg/m2 at the 50th quantile, 0.55 at the 75th, 0.66 at the 90th, and 0.44 at the 95th) and waist circumference (+0.96 cm at the 25th quantile, 1.06 cm at the 50th, 1.35 cm at the 75th, and 0.96 cm at the 90th quantiles) with overall larger associations at higher quantiles. Findings of the study indicate that conventional regression methods may mask important heterogeneity in the association between heavy FAFH consumption and body mass outcomes. Further public health efforts are needed to improve the nutritional quality of affordable FAFH choices and nutrition education and to establish a healthy food consumption environment. Copyright © 2014 Elsevier Inc. All rights reserved.

Association of Perceived Stress with Stressful Life Events, Lifestyle and Sociodemographic Factors: A Large-Scale Community-Based Study Using Logistic Quantile Regression

PubMed Central

Feizi, Awat; Aliyari, Roqayeh; Roohafza, Hamidreza

2012-01-01

Objective. The present paper aimed at investigating the association between perceived stress and major life events stressors in Iranian general population. Methods. In a cross-sectional large-scale community-based study, 4583 people aged 19 and older, living in Isfahan, Iran, were investigated. Logistic quantile regression was used for modeling perceived stress, measured by GHQ questionnaire, as the bounded outcome (dependent), variable, and as a function of most important stressful life events, as the predictor variables, controlling for major lifestyle and sociodemographic factors. This model provides empirical evidence of the predictors' effects heterogeneity depending on individual location on the distribution of perceived stress. Results. The results showed that among four stressful life events, family conflicts and social problems were more correlated with level of perceived stress. Higher levels of education were negatively associated with perceived stress and its coefficients monotonically decrease beyond the 30th percentile. Also, higher levels of physical activity were associated with perception of low levels of stress. The pattern of gender's coefficient over the majority of quantiles implied that females are more affected by stressors. Also high perceived stress was associated with low or middle levels of income. Conclusions. The results of current research suggested that in a developing society with high prevalence of stress, interventions targeted toward promoting financial and social equalities, social skills training, and healthy lifestyle may have the potential benefits for large parts of the population, most notably female and lower educated people. PMID:23091560

Impact of climate change on Gironde Estuary

NASA Astrophysics Data System (ADS)

Laborie, Vanessya; Hissel, François; Sergent, Philippe

2014-05-01

Within the THESEUS European project, a simplified mathematical model for storm surge levels in the Bay of Biscay was adjusted on 10 events at Le Verdon using wind and pressure fields from CLM/SGA, so that the water levels at Le Verdon have the same statistic quantiles as observed tide records for the period [1960-2000]. The analysis of future storm surge levels shows a decrease in their quantiles at Le Verdon, whereas there is an increase of the quantiles of total water levels. This increase is smaller than the sea level rise and gets even smaller as one enters farther upstream in the estuary. A numerical model of the Gironde Estuary was then used to evaluate future water levels at 6 locations of the estuary from Le Verdon to Bordeaux and to assess the changes in the quantiles of water levels during the XXIst century using ONERC's pessimistic scenario for sea level rise (60 cm). The model was fed by several data sources : wind fields at Royan and Mérignac interpolated from the grid of the European Climatolologic Model CLM/SGA, a tide signal at Le Verdon, the discharges of Garonne (at La Réole), the Dordogne (at Pessac) and Isle (at Libourne). A series of flood maps for different return periods between 2 and 100 years and for four time periods ([1960-1999], [2010-2039], [2040-2069] and [2070-2099]) have been built for the region of Bordeaux. Quantiles of water levels in the floodplain have also been calculated. The impact of climate change on the evolution of flooded areas in the Gironde Estuary and on quantiles of water levels in the floodplain mainly depends on the sea level rise. Areas which are not currently flooded for low return periods will be inundated in 2100. The influence of river discharges and dike breaching should also be taken into account for more accurate results.

Comparing least-squares and quantile regression approaches to analyzing median hospital charges.

PubMed

Olsen, Cody S; Clark, Amy E; Thomas, Andrea M; Cook, Lawrence J

2012-07-01

Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased estimates even when lognormal and equal variance assumptions are violated. These methods may be particularly useful in describing and analyzing hospital charges from administrative data sets. © 2012 by the Society for Academic Emergency Medicine.

Gender difference in the association between food away-from-home consumption and body weight outcomes among Chinese adults.

PubMed

Du, Wen-Wen; Zhang, Bing; Wang, Hui-Jun; Wang, Zhi-Hong; Su, Chang; Zhang, Ji-Guo; Zhang, Ji; Jia, Xiao-Fang; Jiang, Hong-Ru

2016-11-01

The present study aimed to explore the associations between food away-from-home (FAFH) consumption and body weight outcomes among Chinese adults. FAFH was defined as food prepared at restaurants and the percentage of energy from FAFH was calculated. Measured BMI and waist circumference (WC) were used as body weight outcomes. Quantile regression models for BMI and WC were performed separately by gender. Information on demographic, socio-economic, diet and health parameters at individual, household and community levels was collected in twelve provinces of China. A cross-sectional sample of 7738 non-pregnant individuals aged 18-60 years from the China Health and Nutrition Survey 2011 was analysed. For males, quantile regression models showed that percentage of energy from FAFH was associated with an increase in BMI of 0·01, 0·01, 0·01, 0·02, 0·02 and 0·03 kg/m2 at the 5th, 25th, 50th, 75th, 90th and 95th quantile, and an increase in WC of 0·04, 0·06, 0·06, 0·04, 0·06, 0·05 and 0·07 cm at the 5th, 10th, 25th, 50th, 75th, 90th and 95th quantile. For females, percentage of energy from FAFH was associated with 0·01, 0·01, 0·01 and 0·02 kg/m2 increase in BMI at the 10th, 25th, 90th and 95th quantile, and with 0·05, 0·04, 0·03 and 0·03 cm increase in WC at the 5th, 10th, 25th and 75th quantile. Our findings suggest that FAFH consumption is relatively more important for BMI and WC among males rather than females in China. Public health initiatives are needed to encourage Chinese adults to make healthy food choices when eating out.

Trends of VOC exposures among a nationally representative sample: Analysis of the NHANES 1988 through 2004 data sets

NASA Astrophysics Data System (ADS)

Su, Feng-Chiao; Mukherjee, Bhramar; Batterman, Stuart

2011-09-01

Exposures to volatile organic compounds (VOCs) are ubiquitous due to emissions from personal, commercial and industrial products, but quantitative and representative information regarding long term exposure trends is lacking. This study characterizes trends from 1988 to 2004 for the 15 VOCs measured in blood in five cohorts of the National Health and Nutrition Examination Survey (NHANES), a large and representative sample of U.S. adults. Trends were evaluated at various percentiles using linear quantile regression (QR) models, which were adjusted for solvent-related occupations and cotinine levels. Most VOCs showed decreasing trends at all quantiles, e.g., median exposures declined by 2.5 (m,p-xylene) to 6.4 (tetrachloroethene) percent per year over the 15 year period. Trends varied by VOC and quantile, and were grouped into three patterns: similar decreases at all quantiles (including benzene, toluene); most rapid decreases at upper quantiles (ethylbenzene, m,p-xylene, o-xylene, styrene, chloroform, tetrachloroethene); and fastest declines at central quantiles (1,4-dichlorobenzene). These patterns reflect changes in exposure sources, e.g., upper-percentile exposures may result mostly from occupational exposure, while lower percentile exposures arise from general environmental sources. Both VOC emissions aggregated at the national level and VOC concentrations measured in ambient air also have declined substantially over the study period and are supportive of the exposure trends, although the NHANES data suggest the importance of indoor sources and personal activities on VOC exposures. While piecewise QR models suggest that exposures of several VOCs decreased little or any during the 1990's, followed by more rapid decreases from 1999 to 2004, questions are raised concerning the reliability of VOC data in several of the NHANES cohorts and its applicability as an exposure indicator, as demonstrated by the modest correlation between VOC levels in blood and personal air collected in the 1999/2000 cohort. Despite some limitations, the NHANES data provides a unique, long term and direct measurement of VOC exposures and trends.

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center

PubMed Central

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Principles Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. Methods 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Results Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). Conclusion We suggest considering psychiatric diagnosis, admission as an emergencay case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses. PMID:26517545

Relationship between Urbanization and Cancer Incidence in Iran Using Quantile Regression.

PubMed

Momenyan, Somayeh; Sadeghifar, Majid; Sarvi, Fatemeh; Khodadost, Mahmoud; Mosavi-Jarrahi, Alireza; Ghaffari, Mohammad Ebrahim; Sekhavati, Eghbal

2016-01-01

Quantile regression is an efficient method for predicting and estimating the relationship between explanatory variables and percentile points of the response distribution, particularly for extreme percentiles of the distribution. To study the relationship between urbanization and cancer morbidity, we here applied quantile regression. This cross-sectional study was conducted for 9 cancers in 345 cities in 2007 in Iran. Data were obtained from the Ministry of Health and Medical Education and the relationship between urbanization and cancer morbidity was investigated using quantile regression and least square regression. Fitting models were compared using AIC criteria. R (3.0.1) software and the Quantreg package were used for statistical analysis. With the quantile regression model all percentiles for breast, colorectal, prostate, lung and pancreas cancers demonstrated increasing incidence rate with urbanization. The maximum increase for breast cancer was in the 90th percentile (β=0.13, p-value<0.001), for colorectal cancer was in the 75th percentile (β=0.048, p-value<0.001), for prostate cancer the 95th percentile (β=0.55, p-value<0.001), for lung cancer was in 95th percentile (β=0.52, p-value=0.006), for pancreas cancer was in 10th percentile (β=0.011, p-value<0.001). For gastric, esophageal and skin cancers, with increasing urbanization, the incidence rate was decreased. The maximum decrease for gastric cancer was in the 90th percentile(β=0.003, p-value<0.001), for esophageal cancer the 95th (β=0.04, p-value=0.4) and for skin cancer also the 95th (β=0.145, p-value=0.071). The AIC showed that for upper percentiles, the fitting of quantile regression was better than least square regression. According to the results of this study, the significant impact of urbanization on cancer morbidity requirs more effort and planning by policymakers and administrators in order to reduce risk factors such as pollution in urban areas and ensure proper nutrition recommendations are made.

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.

PubMed

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.

Measuring Disparities across the Distribution of Mental Health Care Expenditures

PubMed Central

Cook, Benjamin Lê; Manning, Willard; Alegría, Margarita

2013-01-01

Background Previous mental health care disparities studies predominantly compare mean mental health care use across racial/ethnic groups, leaving policymakers with little information on disparities among those with a higher level of expenditures. Aims of the Study To identify racial/ethnic disparities among individuals at varying quantiles of mental health care expenditures. To assess whether disparities in the upper quantiles of expenditure differ by insurance status, income and education. Methods Data were analyzed from a nationally representative sample of white, black and Latino adults 18 years and older (n=83,878). Our dependent variable was total mental health care expenditure. We measured disparities in any mental health care expenditures, disparities in mental health care expenditure at the 95th, 97.5th, and 99th expenditure quantiles of the full population using quantile regression, and at the 50th, 75th, and 95th quantiles for positive users. In the full population, we tested interaction coefficients between race/ethnicity and income, insurance, and education levels to determine whether racial/ethnic disparities in the upper quantiles differed by income, insurance and education. Results Significant Black-white and Latino-white disparities were identified in any mental health care expenditures. In the full population, moving up the quantiles of mental health care expenditures, Black-White and Latino-White disparities were reduced but remained statistically significant. No statistically significant disparities were found in analyses of positive users only. The magnitude of black-white disparities was smaller among those enrolled in public insurance programs compared to the privately insured and uninsured in the 97.5th and 99th quantiles. Disparities persist in the upper quantiles among those in higher income categories and after excluding psychiatric inpatient and emergency department (ED) visits. Discussion Disparities exist in any mental health care and among those that use the most mental health care resources, but much of disparities seem to be driven by lack of access. The data do not allow us to disentangle whether disparities were related to white respondent’s overuse or underuse as compared to minority groups. The cross-sectional data allow us to make only associational claims about the role of insurance, income, and education in disparities. With these limitations in mind, we identified a persistence of disparities in overall expenditures even among those in the highest income categories, after controlling for mental health status and observable sociodemographic characteristics. Implications for Health Care Provision and Use Interventions are needed to equalize resource allocation to racial/ethnic minority patients regardless of their income, with emphasis on outreach interventions to address the disparities in access that are responsible for the no/low expenditures for even Latinos at higher levels of illness severity. Implications for Health Policies Increased policy efforts are needed to reduce the gap in health insurance for Latinos and improve outreach programs to enroll those in need into mental health care services. Implications for Further Research Future studies that conclusively disentangle overuse and appropriate use in these populations are warranted. PMID:23676411

Quantifying Population-Level Risks Using an Individual-Based Model: Sea Otters, Harlequin Ducks, and the Exxon Valdez Oil Spill

PubMed Central

Harwell, Mark A; Gentile, John H; Parker, Keith R

2012-01-01

Ecological risk assessments need to advance beyond evaluating risks to individuals that are largely based on toxicity studies conducted on a few species under laboratory conditions, to assessing population-level risks to the environment, including considerations of variability and uncertainty. Two individual-based models (IBMs), recently developed to assess current risks to sea otters and seaducks in Prince William Sound more than 2 decades after the Exxon Valdez oil spill (EVOS), are used to explore population-level risks. In each case, the models had previously shown that there were essentially no remaining risks to individuals from polycyclic aromatic hydrocarbons (PAHs) derived from the EVOS. New sensitivity analyses are reported here in which hypothetical environmental exposures to PAHs were heuristically increased until assimilated doses reached toxicity reference values (TRVs) derived at the no-observed-adverse-effects and lowest-observed-adverse-effects levels (NOAEL and LOAEL, respectively). For the sea otters, this was accomplished by artificially increasing the number of sea otter pits that would intersect remaining patches of subsurface oil residues by orders of magnitude over actual estimated rates. Similarly, in the seaduck assessment, the PAH concentrations in the constituents of diet, sediments, and seawater were increased in proportion to their relative contributions to the assimilated doses by orders of magnitude over measured environmental concentrations, to reach the NOAEL and LOAEL thresholds. The stochastic IBMs simulated millions of individuals. From these outputs, frequency distributions were derived of assimilated doses for populations of 500 000 sea otters or seaducks in each of 7 or 8 classes, respectively. Doses to several selected quantiles were analyzed, ranging from the 1-in-1000th most-exposed individuals (99.9% quantile) to the median-exposed individuals (50% quantile). The resulting families of quantile curves provide the basis for characterizing the environmental thresholds below which no population-level effects could be detected and above which population-level effects would be expected to become manifest. This approach provides risk managers an enhanced understanding of the risks to populations under various conditions and assumptions, whether under hypothetically increased exposure regimes, as demonstrated here, or in situations in which actual exposures are near toxic effects levels. This study shows that individual-based models are especially amenable and appropriate for conducting population-level risk assessments, and that they can readily be used to answer questions about the risks to individuals and populations across a variety of exposure conditions. Integr Environ Assess Manag 2012; 8: 503–522. © 2012 SETAC PMID:22275071

Quantifying population-level risks using an individual-based model: sea otters, Harlequin Ducks, and the Exxon Valdez oil spill.

PubMed

Harwell, Mark A; Gentile, John H; Parker, Keith R

2012-07-01

Ecological risk assessments need to advance beyond evaluating risks to individuals that are largely based on toxicity studies conducted on a few species under laboratory conditions, to assessing population-level risks to the environment, including considerations of variability and uncertainty. Two individual-based models (IBMs), recently developed to assess current risks to sea otters and seaducks in Prince William Sound more than 2 decades after the Exxon Valdez oil spill (EVOS), are used to explore population-level risks. In each case, the models had previously shown that there were essentially no remaining risks to individuals from polycyclic aromatic hydrocarbons (PAHs) derived from the EVOS. New sensitivity analyses are reported here in which hypothetical environmental exposures to PAHs were heuristically increased until assimilated doses reached toxicity reference values (TRVs) derived at the no-observed-adverse-effects and lowest-observed-adverse-effects levels (NOAEL and LOAEL, respectively). For the sea otters, this was accomplished by artificially increasing the number of sea otter pits that would intersect remaining patches of subsurface oil residues by orders of magnitude over actual estimated rates. Similarly, in the seaduck assessment, the PAH concentrations in the constituents of diet, sediments, and seawater were increased in proportion to their relative contributions to the assimilated doses by orders of magnitude over measured environmental concentrations, to reach the NOAEL and LOAEL thresholds. The stochastic IBMs simulated millions of individuals. From these outputs, frequency distributions were derived of assimilated doses for populations of 500,000 sea otters or seaducks in each of 7 or 8 classes, respectively. Doses to several selected quantiles were analyzed, ranging from the 1-in-1000th most-exposed individuals (99.9% quantile) to the median-exposed individuals (50% quantile). The resulting families of quantile curves provide the basis for characterizing the environmental thresholds below which no population-level effects could be detected and above which population-level effects would be expected to become manifest. This approach provides risk managers an enhanced understanding of the risks to populations under various conditions and assumptions, whether under hypothetically increased exposure regimes, as demonstrated here, or in situations in which actual exposures are near toxic effects levels. This study shows that individual-based models are especially amenable and appropriate for conducting population-level risk assessments, and that they can readily be used to answer questions about the risks to individuals and populations across a variety of exposure conditions. Copyright © 2012 SETAC.

Behavioural Sequential Analysis of Using an Instant Response Application to Enhance Peer Interactions in a Flipped Classroom

ERIC Educational Resources Information Center

Hsu, Ting-Chia

2018-01-01

To stimulate classroom interactions, this study employed two different smartphone application modes, providing an additional instant interaction channel in a flipped classroom teaching fundamental computer science concepts. One instant interaction mode provided the students (N = 36) with anonymous feedback in chronological time sequence, while the…

Self-Controlled Practice Enhances Motor Learning in Introverts and Extroverts

ERIC Educational Resources Information Center

Kaefer, Angélica; Chiviacowsky, Suzete; Meira, Cassio de Miranda, Jr.; Tani, Go

2014-01-01

Purpose: The purpose of the present study was to investigate the effects of self-controlled feedback on the learning of a sequential-timing motor task in introverts and extroverts. Method: Fifty-six university students were selected by the Eysenck Personality Questionnaire. They practiced a motor task consisting of pressing computer keyboard keys…

Exploring Students' Behavioural Patterns during Online Peer Assessment from the Affective, Cognitive, and Metacognitive Perspectives: A Progressive Sequential Analysis

ERIC Educational Resources Information Center

Cheng, Kun-Hung; Hou, Huei-Tse

2015-01-01

Previous research regarding peer assessment has investigated the relationships between peer feedback and learners' performance. However, few studies investigate in-depth learning processes during technology-assisted peer assessment activities, particularly from affective, cognitive, and metacognitive perspectives. This study conducts a series of…

Estimating the Extreme Behaviors of Students Performance Using Quantile Regression--Evidences from Taiwan

ERIC Educational Resources Information Center

Chen, Sheng-Tung; Kuo, Hsiao-I.; Chen, Chi-Chung

2012-01-01

The two-stage least squares approach together with quantile regression analysis is adopted here to estimate the educational production function. Such a methodology is able to capture the extreme behaviors of the two tails of students' performance and the estimation outcomes have important policy implications. Our empirical study is applied to the…

Determinants of Academic Attainment in the United States: A Quantile Regression Analysis of Test Scores

ERIC Educational Resources Information Center

Haile, Getinet Astatike; Nguyen, Anh Ngoc

2008-01-01

We investigate the determinants of high school students' academic attainment in mathematics, reading and science in the United States; focusing particularly on possible differential impacts of ethnicity and family background across the distribution of test scores. Using data from the NELS2000 and employing quantile regression, we find two…

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

PubMed

Tzavidis, Nikos; Salvati, Nicola; Schmid, Timo; Flouri, Eirini; Midouhas, Emily

2016-02-01

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M -quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Heterogeneous effects of oil shocks on exchange rates: evidence from a quantile regression approach.

PubMed

Su, Xianfang; Zhu, Huiming; You, Wanhai; Ren, Yinghua

2016-01-01

The determinants of exchange rates have attracted considerable attention among researchers over the past several decades. Most studies, however, ignore the possibility that the impact of oil shocks on exchange rates could vary across the exchange rate returns distribution. We employ a quantile regression approach to address this issue. Our results indicate that the effect of oil shocks on exchange rates is heterogeneous across quantiles. A large US depreciation or appreciation tends to heighten the effects of oil shocks on exchange rate returns. Positive oil demand shocks lead to appreciation pressures in oil-exporting countries and this result is robust across lower and upper return distributions. These results offer rich and useful information for investors and decision-makers.

Sequential Injection Analysis for Optimization of Molecular Biology Reactions

PubMed Central

Allen, Peter B.; Ellington, Andrew D.

2011-01-01

In order to automate the optimization of complex biochemical and molecular biology reactions, we developed a Sequential Injection Analysis (SIA) device and combined this with a Design of Experiment (DOE) algorithm. This combination of hardware and software automatically explores the parameter space of the reaction and provides continuous feedback for optimizing reaction conditions. As an example, we optimized the endonuclease digest of a fluorogenic substrate, and showed that the optimized reaction conditions also applied to the digest of the substrate outside of the device, and to the digest of a plasmid. The sequential technique quickly arrived at optimized reaction conditions with less reagent use than a batch process (such as a fluid handling robot exploring multiple reaction conditions in parallel) would have. The device and method should now be amenable to much more complex molecular biology reactions whose variable spaces are correspondingly larger. PMID:21338059

A Bibliography for the ABLUE.

DTIC Science & Technology

1982-06-01

scale based on two symmetric quantiles. Sankhya A 30, 335-336. [S] Gupta, S. S. and Gnanadesikan , M. (1966). Estimation of the parameters of the logistic...and Cheng (1971, 1972, 1974) Chan, Cheng, Mead and Panjer (1973) Cheng (1975) Eubank (1979, 1981a,b) Gupta and Gnanadesikan (1966) Hassanein (1969b

Examining the Reliability of Student Growth Percentiles Using Multidimensional IRT

ERIC Educational Resources Information Center

Monroe, Scott; Cai, Li

2015-01-01

Student growth percentiles (SGPs, Betebenner, 2009) are used to locate a student's current score in a conditional distribution based on the student's past scores. Currently, following Betebenner (2009), quantile regression (QR) is most often used operationally to estimate the SGPs. Alternatively, multidimensional item response theory (MIRT) may…

Spatially Modeling the Effects of Meteorological Drivers of PM2.5 in the Eastern United States via a Local Linear Penalized Quantile Regression Estimator.

PubMed

Russell, Brook T; Wang, Dewei; McMahan, Christopher S

2017-08-01

Fine particulate matter (PM 2.5 ) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, atherosclerosis, etc. In order to improve current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM 2.5 . It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM 2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely selects the set of important drivers at every spatial location and for each quantile of the conditional distribution of PM 2.5 levels. The performance of the proposed methodology is illustrated through simulation, and it is then used to determine the association between several meteorological drivers and PM 2.5 over the Eastern United States (US). This analysis suggests that the primary drivers throughout much of the Eastern US tend to differ based on season and geographic location, with similarities existing between "typical" and "high" PM 2.5 levels.

Design Life Level: Quantifying risk in a changing climate

NASA Astrophysics Data System (ADS)

Rootzén, Holger; Katz, Richard W.

2013-09-01

In the past, the concepts of return levels and return periods have been standard and important tools for engineering design. However, these concepts are based on the assumption of a stationary climate and do not apply to a changing climate, whether local or global. In this paper, we propose a refined concept, Design Life Level, which quantifies risk in a nonstationary climate and can serve as the basis for communication. In current practice, typical hydrologic risk management focuses on a standard (e.g., in terms of a high quantile corresponding to the specified probability of failure for a single year). Nevertheless, the basic information needed for engineering design should consist of (i) the design life period (e.g., the next 50 years, say 2015-2064); and (ii) the probability (e.g., 5% chance) of a hazardous event (typically, in the form of the hydrologic variable exceeding a high level) occurring during the design life period. Capturing both of these design characteristics, the Design Life Level is defined as an upper quantile (e.g., 5%) of the distribution of the maximum value of the hydrologic variable (e.g., water level) over the design life period. We relate this concept and variants of it to existing literature and illustrate how they, and some useful complementary plots, may be computed and used. One practically important consideration concerns quantifying the statistical uncertainty in estimating a high quantile under nonstationarity.

Moisture availability constraints on the leaf area to sapwood area ratio: analysis of measurements on Australian evergreen angiosperm trees

NASA Astrophysics Data System (ADS)

Togashi, Henrique; Prentice, Colin; Evans, Bradley; Forrester, David; Drake, Paul; Feikema, Paul; Brooksbank, Kim; Eamus, Derek; Taylor, Daniel

2014-05-01

The leaf area to sapwood area ratio (LA:SA) is a key plant trait that links photosynthesis to transpiration. Pipe model theory states that the sapwood cross-sectional area of a stem or branch at any point should scale isometrically with the area of leaves distal to that point. Optimization theory further suggests that LA:SA should decrease towards drier climates. Although acclimation of LA:SA to climate has been reported within species, much less is known about the scaling of this trait with climate among species. We compiled LA:SA measurements from 184 species of Australian evergreen angiosperm trees. The pipe model was broadly confirmed, based on measurements on branches and trunks of trees from one to 27 years old. We found considerable scatter in LA:SA among species. However quantile regression showed strong (0.2

Morphological and moisture availability controls of the leaf area-to-sapwood area ratio: analysis of measurements on Australian trees.

PubMed

Togashi, Henrique Furstenau; Prentice, Iain Colin; Evans, Bradley John; Forrester, David Ian; Drake, Paul; Feikema, Paul; Brooksbank, Kim; Eamus, Derek; Taylor, Daniel

2015-03-01

The leaf area-to-sapwood area ratio (LA:SA) is a key plant trait that links photosynthesis to transpiration. The pipe model theory states that the sapwood cross-sectional area of a stem or branch at any point should scale isometrically with the area of leaves distal to that point. Optimization theory further suggests that LA:SA should decrease toward drier climates. Although acclimation of LA:SA to climate has been reported within species, much less is known about the scaling of this trait with climate among species. We compiled LA:SA measurements from 184 species of Australian evergreen angiosperm trees. The pipe model was broadly confirmed, based on measurements on branches and trunks of trees from one to 27 years old. Despite considerable scatter in LA:SA among species, quantile regression showed strong (0.2 < R1 < 0.65) positive relationships between two climatic moisture indices and the lowermost (5%) and uppermost (5-15%) quantiles of log LA:SA, suggesting that moisture availability constrains the envelope of minimum and maximum values of LA:SA typical for any given climate. Interspecific differences in plant hydraulic conductivity are probably responsible for the large scatter of values in the mid-quantile range and may be an important determinant of tree morphology.

Morphological and moisture availability controls of the leaf area-to-sapwood area ratio: analysis of measurements on Australian trees

PubMed Central

Togashi, Henrique Furstenau; Prentice, Iain Colin; Evans, Bradley John; Forrester, David Ian; Drake, Paul; Feikema, Paul; Brooksbank, Kim; Eamus, Derek; Taylor, Daniel

2015-01-01

The leaf area-to-sapwood area ratio (LA:SA) is a key plant trait that links photosynthesis to transpiration. The pipe model theory states that the sapwood cross-sectional area of a stem or branch at any point should scale isometrically with the area of leaves distal to that point. Optimization theory further suggests that LA:SA should decrease toward drier climates. Although acclimation of LA:SA to climate has been reported within species, much less is known about the scaling of this trait with climate among species. We compiled LA:SA measurements from 184 species of Australian evergreen angiosperm trees. The pipe model was broadly confirmed, based on measurements on branches and trunks of trees from one to 27 years old. Despite considerable scatter in LA:SA among species, quantile regression showed strong (0.2 < R1 < 0.65) positive relationships between two climatic moisture indices and the lowermost (5%) and uppermost (5–15%) quantiles of log LA:SA, suggesting that moisture availability constrains the envelope of minimum and maximum values of LA:SA typical for any given climate. Interspecific differences in plant hydraulic conductivity are probably responsible for the large scatter of values in the mid-quantile range and may be an important determinant of tree morphology. PMID:25859331

Growth curves of preschool children in the northeast of iran: a population based study using quantile regression approach.

PubMed

Payande, Abolfazl; Tabesh, Hamed; Shakeri, Mohammad Taghi; Saki, Azadeh; Safarian, Mohammad

2013-01-14

Growth charts are widely used to assess children's growth status and can provide a trajectory of growth during early important months of life. The objectives of this study are going to construct growth charts and normal values of weight-for-age for children aged 0 to 5 years using a powerful and applicable methodology. The results compare with the World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green. A total of 70737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from those attending community clinics for routine health checks as a part of a national survey. Anthropometric measurements were done by trained health staff using WHO methodology. The nonparametric quantile regression method obtained by local constant kernel estimation of conditional quantiles curves using for estimation of curves and normal values. The weight-for-age growth curves for boys and girls aged from 0 to 5 years were derived utilizing a population of children living in the northeast of Iran. The results were similar to the ones obtained by the semi-parametric LMS method in the same data. Among all age groups from 0 to 5 years, the median values of children's weight living in the northeast of Iran were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups. The differences between growth patterns of children living in the northeast of Iran versus international ones necessitate using local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in Iranian children. Quantile regression (QR) as a flexible method which doesn't require restricted assumptions, proposed for estimation reference curves and normal values.

Growth Curves of Preschool Children in the Northeast of Iran: A Population Based Study Using Quantile Regression Approach

PubMed Central

Payande, Abolfazl; Tabesh, Hamed; Shakeri, Mohammad Taghi; Saki, Azadeh; Safarian, Mohammad

2013-01-01

Introduction: Growth charts are widely used to assess children’s growth status and can provide a trajectory of growth during early important months of life. The objectives of this study are going to construct growth charts and normal values of weight-for-age for children aged 0 to 5 years using a powerful and applicable methodology. The results compare with the World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green. Methods: A total of 70737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from those attending community clinics for routine health checks as a part of a national survey. Anthropometric measurements were done by trained health staff using WHO methodology. The nonparametric quantile regression method obtained by local constant kernel estimation of conditional quantiles curves using for estimation of curves and normal values. Results: The weight-for-age growth curves for boys and girls aged from 0 to 5 years were derived utilizing a population of children living in the northeast of Iran. The results were similar to the ones obtained by the semi-parametric LMS method in the same data. Among all age groups from 0 to 5 years, the median values of children’s weight living in the northeast of Iran were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups. Conclusion: The differences between growth patterns of children living in the northeast of Iran versus international ones necessitate using local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in Iranian children. Quantile regression (QR) as a flexible method which doesn’t require restricted assumptions, proposed for estimation reference curves and normal values. PMID:23618470

The 2011 heat wave in Greater Houston: Effects of land use on temperature.

PubMed

Zhou, Weihe; Ji, Shuang; Chen, Tsun-Hsuan; Hou, Yi; Zhang, Kai

2014-11-01

Effects of land use on temperatures during severe heat waves have been rarely studied. This paper examines land use-temperature associations during the 2011 heat wave in Greater Houston. We obtained high resolution of satellite-derived land use data from the US National Land Cover Database, and temperature observations at 138 weather stations from Weather Underground, Inc (WU) during the August of 2011, which was the hottest month in Houston since 1889. Land use regression and quantile regression methods were applied to the monthly averages of daily maximum/mean/minimum temperatures and 114 land use-related predictors. Although selected variables vary with temperature metric, distance to the coastline consistently appears among all models. Other variables are generally related to high developed intensity, open water or wetlands. In addition, our quantile regression analysis shows that distance to the coastline and high developed intensity areas have larger impacts on daily average temperatures at higher quantiles, and open water area has greater impacts on daily minimum temperatures at lower quantiles. By utilizing both land use regression and quantile regression on a recent heat wave in one of the largest US metropolitan areas, this paper provides a new perspective on the impacts of land use on temperatures. Our models can provide estimates of heat exposures for epidemiological studies, and our findings can be combined with demographic variables, air conditioning and relevant diseases information to identify 'hot spots' of population vulnerability for public health interventions to reduce heat-related health effects during heat waves. Copyright © 2014 Elsevier Inc. All rights reserved.

Multi-element stochastic spectral projection for high quantile estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ko, Jordan, E-mail: jordan.ko@mac.com; Garnier, Josselin

2013-06-15

We investigate quantile estimation by multi-element generalized Polynomial Chaos (gPC) metamodel where the exact numerical model is approximated by complementary metamodels in overlapping domains that mimic the model’s exact response. The gPC metamodel is constructed by the non-intrusive stochastic spectral projection approach and function evaluation on the gPC metamodel can be considered as essentially free. Thus, large number of Monte Carlo samples from the metamodel can be used to estimate α-quantile, for moderate values of α. As the gPC metamodel is an expansion about the means of the inputs, its accuracy may worsen away from these mean values where themore » extreme events may occur. By increasing the approximation accuracy of the metamodel, we may eventually improve accuracy of quantile estimation but it is very expensive. A multi-element approach is therefore proposed by combining a global metamodel in the standard normal space with supplementary local metamodels constructed in bounded domains about the design points corresponding to the extreme events. To improve the accuracy and to minimize the sampling cost, sparse-tensor and anisotropic-tensor quadratures are tested in addition to the full-tensor Gauss quadrature in the construction of local metamodels; different bounds of the gPC expansion are also examined. The global and local metamodels are combined in the multi-element gPC (MEgPC) approach and it is shown that MEgPC can be more accurate than Monte Carlo or importance sampling methods for high quantile estimations for input dimensions roughly below N=8, a limit that is very much case- and α-dependent.« less

Effective Control of Computationally Simulated Wing Rock in Subsonic Flow

NASA Technical Reports Server (NTRS)

Kandil, Osama A.; Menzies, Margaret A.

1997-01-01

The unsteady compressible, full Navier-Stokes (NS) equations and the Euler equations of rigid-body dynamics are sequentially solved to simulate the delta wing rock phenomenon. The NS equations are solved time accurately, using the implicit, upwind, Roe flux-difference splitting, finite-volume scheme. The rigid-body dynamics equations are solved using a four-stage Runge-Kutta scheme. Once the wing reaches the limit-cycle response, an active control model using a mass injection system is applied from the wing surface to suppress the limit-cycle oscillation. The active control model is based on state feedback and the control law is established using pole placement techniques. The control law is based on the feedback of two states: the roll-angle and roll velocity. The primary model of the computational applications consists of a 80 deg swept, sharp edged, delta wing at 30 deg angle of attack in a freestream of Mach number 0.1 and Reynolds number of 0.4 x 10(exp 6). With a limit-cycle roll amplitude of 41.1 deg, the control model is applied, and the results show that within one and one half cycles of oscillation, the wing roll amplitude and velocity are brought to zero.

Using nonlinear quantile regression to estimate the self-thinning boundary curve

Treesearch

Quang V. Cao; Thomas J. Dean

2015-01-01

The relationship between tree size (quadratic mean diameter) and tree density (number of trees per unit area) has been a topic of research and discussion for many decades. Starting with Reineke in 1933, the maximum size-density relationship, on a log-log scale, has been assumed to be linear. Several techniques, including linear quantile regression, have been employed...

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: Evidence from Quantile Regression Analyses

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2011-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15 year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: A Quantile Regression Approach

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2013-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15-year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

A Quantile Regression Approach to Understanding the Relations among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

ERIC Educational Resources Information Center

Tighe, Elizabeth L.; Schatschneider, Christopher

2016-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological…

Trait Mindfulness as a Limiting Factor for Residual Depressive Symptoms: An Explorative Study Using Quantile Regression

PubMed Central

Radford, Sholto; Eames, Catrin; Brennan, Kate; Lambert, Gwladys; Crane, Catherine; Williams, J. Mark G.; Duggan, Danielle S.; Barnhofer, Thorsten

2014-01-01

Mindfulness has been suggested to be an important protective factor for emotional health. However, this effect might vary with regard to context. This study applied a novel statistical approach, quantile regression, in order to investigate the relation between trait mindfulness and residual depressive symptoms in individuals with a history of recurrent depression, while taking into account symptom severity and number of episodes as contextual factors. Rather than fitting to a single indicator of central tendency, quantile regression allows exploration of relations across the entire range of the response variable. Analysis of self-report data from 274 participants with a history of three or more previous episodes of depression showed that relatively higher levels of mindfulness were associated with relatively lower levels of residual depressive symptoms. This relationship was most pronounced near the upper end of the response distribution and moderated by the number of previous episodes of depression at the higher quantiles. The findings suggest that with lower levels of mindfulness, residual symptoms are less constrained and more likely to be influenced by other factors. Further, the limiting effect of mindfulness on residual symptoms is most salient in those with higher numbers of episodes. PMID:24988072

Sustainability of teacher implementation of school-based mental health programs.

PubMed

Han, Susan S; Weiss, Bahr

2005-12-01

Evidence-based prevention and intervention programs are increasingly being implemented in schools and it therefore is becoming increasingly important to understand the complexities of program implementation under real-world conditions. Much research has focused on the contextual factors that influence program implementation but less work has attempted to provide an integrated understanding of mechanisms (e.g., teacher-training processes) that affect teachers' program implementation. In this paper, we review literature on factors related to teachers' implementation of school-based prevention and intervention programs, then from this review abstract what we believe are four basic ingredients that characterize potentially sustainable teacher-implemented classroom programs. Finally, we present a sequential model, based on these ingredients, of the naturalistic processes underlying sustainability of teachers' program implementation and describe how this sustainability can be enhanced through provision of teacher training and performance feedback from a classroom consultant.

Robust and efficient estimation with weighted composite quantile regression

NASA Astrophysics Data System (ADS)

Jiang, Xuejun; Li, Jingzhi; Xia, Tian; Yan, Wanfeng

2016-09-01

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

Feedback from visual cortical area 7 to areas 17 and 18 in cats: How neural web is woven during feedback.

PubMed

Yang, X; Ding, H; Lu, J

2016-01-15

To investigate the feedback effect from area 7 to areas 17 and 18, intrinsic signal optical imaging combined with pharmacological, morphological methods and functional magnetic resonance imaging (fMRI) was employed. A spatial frequency-dependent decrease in response amplitude of orientation maps was observed in areas 17 and 18 when area 7 was inactivated by a local injection of GABA, or by a lesion induced by liquid nitrogen freezing. The pattern of orientation maps of areas 17 and 18 after the inactivation of area 7, if they were not totally blurred, paralleled the normal one. In morphological experiments, after one point at the shallow layers within the center of the cat's orientation column of area 17 was injected electrophoretically with HRP (horseradish peroxidase), three sequential patches in layers 1, 2 and 3 of area 7 were observed. Employing fMRI it was found that area 7 feedbacks mainly to areas 17 and 18 on ipsilateral hemisphere. Therefore, our conclusions are: (1) feedback from area 7 to areas 17 and 18 is spatial frequency modulated; (2) feedback from area 7 to areas 17 and 18 occurs mainly ipsilaterally; (3) histological feedback pattern from area 7 to area 17 is weblike. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

A sequential analysis of classroom discourse in Italian primary schools: the many faces of the IRF pattern.

PubMed

Molinari, Luisa; Mameli, Consuelo; Gnisci, Augusto

2013-09-01

A sequential analysis of classroom discourse is needed to investigate the conditions under which the triadic initiation-response-feedback (IRF) pattern may host different teaching orientations. The purpose of the study is twofold: first, to describe the characteristics of classroom discourse and, second, to identify and explore the different interactive sequences that can be captured with a sequential statistical analysis. Twelve whole-class activities were video recorded in three Italian primary schools. We observed classroom interaction as it occurs naturally on an everyday basis. In total, we collected 587 min of video recordings. Subsequently, 828 triadic IRF patterns were extracted from this material and analysed with the programme Generalized Sequential Query (GSEQ). The results indicate that classroom discourse may unfold in different ways. In particular, we identified and described four types of sequences. Dialogic sequences were triggered by authentic questions, and continued through further relaunches. Monologic sequences were directed to fulfil the teachers' pre-determined didactic purposes. Co-constructive sequences fostered deduction, reasoning, and thinking. Scaffolding sequences helped and sustained children with difficulties. The application of sequential analyses allowed us to show that interactive sequences may account for a variety of meanings, thus making a significant contribution to the literature and research practice in classroom discourse. © 2012 The British Psychological Society.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

No causal impact of serum vascular endothelial growth factor level on temporal changes in body mass index in Japanese male workers: a five-year longitudinal study.

PubMed

Imatoh, Takuya; Kamimura, Seiichiro; Miyazaki, Motonobu

2017-03-01

It has been reported that adipocytes secrete vascular endothelial growth factor. Therefore, we conducted a 5-year longitudinal epidemiological study to further elucidate the association between vascular endothelial growth factor levels and temporal changes in body mass index. Our study subjects were Japanese male workers, who had regular health check-ups. Vascular endothelial growth factor levels were measured at baseline. To examine the association between vascular endothelial growth factor levels and overweight, we calculated the odds ratio using a multivariate logistic regression model. Moreover, linear mixed effect models were used to assess the association between vascular endothelial growth factor level and temporal changes in body mass index during the 5-year follow-up period. Vascular endothelial growth factor levels were marginally higher in subjects with a body mass index greater than 25 kg/m 2 compared with in those with a body mass index less than 25 kg/m 2 (505.4 vs. 465.5 pg/mL, P = 0.1) and were weakly correlated with leptin levels (β: 0.05, P = 0.07). In multivariate logistic regression, subjects in the highest vascular endothelial growth factor quantile were significantly associated with an increased risk for overweight compared with those in the lowest quantile (odds ratio 1.65, 95 % confidential interval: 1.10-2.50). Moreover P for trend was significant (P for trend = 0.003). However, the linear mixed effect model revealed that vascular endothelial growth factor levels were not associated with changes in body mass index over a 5-year period (quantile 2, β: 0.06, P = 0.46; quantile 3, β: -0.06, P = 0.45; quantile 4, β: -0.10, P = 0.22; quantile 1 as reference). Our results suggested that high vascular endothelial growth factor levels were significantly associated with overweight in Japanese males but high vascular endothelial growth factor levels did not necessarily cause obesity.

Trends of VOC exposures among a nationally representative sample: Analysis of the NHANES 1988 through 2004 data sets

PubMed Central

Su, Feng-Chiao; Mukherjee, Bhramar; Batterman, Stuart

2015-01-01

Exposures to volatile organic compounds (VOCs) are ubiquitous due to emissions from personal, commercial and industrial products, but quantitative and representative information regarding long term exposure trends is lacking. This study characterizes trends from1988 to 2004 for the 15 VOCs measured in blood in five cohorts of the National Health and Nutrition Examination Survey (NHANES), a large and representative sample of U.S. adults. Trends were evaluated at various percentiles using linear quantile regression (QR) models, which were adjusted for solvent-related occupations and cotinine levels. Most VOCs showed decreasing trends at all quantiles, e.g., median exposures declined by 2.5 (m, p-xylene) to 6.4 (tetrachloroethene) percent per year over the 15 year period. Trends varied by VOC and quantile, and were grouped into three patterns: similar decreases at all quantiles (including benzene, toluene); most rapid decreases at upper quantiles (ethylbenzene, m, p-xylene, o-xylene, styrene, chloroform, tetrachloroethene); and fastest declines at central quantiles (1,4-dichlorobenzene). These patterns reflect changes in exposure sources, e.g., upper-percentile exposures may result mostly from occupational exposure, while lower percentile exposures arise from general environmental sources. Both VOC emissions aggregated at the national level and VOC concentrations measured in ambient air also have declined substantially over the study period and are supportive of the exposure trends, although the NHANES data suggest the importance of indoor sources and personal activities on VOC exposures. While piecewise QR models suggest that exposures of several VOCs decreased little or any during the 1990’s, followed by more rapid decreases from 1999 to 2004, questions are raised concerning the reliability of VOC data in several of the NHANES cohorts and its applicability as an exposure indicator, as demonstrated by the modest correlation between VOC levels in blood and personal air collected in the 1999/2000 cohort. Despite some limitations, the NHANES data provides a unique, long term and direct measurement of VOC exposures and trends. PMID:25705111

The persistence of a visual dominance effect in a telemanipulator task: A comparison between visual and electrotactile feedback

NASA Technical Reports Server (NTRS)

Gaillard, J. P.

1981-01-01

The possibility to use an electrotactile stimulation in teleoperation and to observe the interpretation of such information as a feedback to the operator was investigated. It is proposed that visual feedback is more informative than an electrotactile one; and that complex electrotactile feedback slows down both the motor decision and motor response processes, is processed as an all or nothing signal, and bypasses the receptive structure and accesses directly in a working memory where information is sequentially processed and where memory is limited in treatment capacity. The electrotactile stimulation is used as an alerting signal. It is suggested that the visual dominance effect is the result of the advantage of both a transfer function and a sensory memory register where information is pretreated and memorized for a short time. It is found that dividing attention has an effect on the acquisition of the information but not on the subsequent decision processes.

Regional L-Moment-Based Flood Frequency Analysis in the Upper Vistula River Basin, Poland

NASA Astrophysics Data System (ADS)

Rutkowska, A.; Żelazny, M.; Kohnová, S.; Łyp, M.; Banasik, K.

2017-02-01

The Upper Vistula River basin was divided into pooling groups with similar dimensionless frequency distributions of annual maximum river discharge. The cluster analysis and the Hosking and Wallis (HW) L-moment-based method were used to divide the set of 52 mid-sized catchments into disjoint clusters with similar morphometric, land use, and rainfall variables, and to test the homogeneity within clusters. Finally, three and four pooling groups were obtained alternatively. Two methods for identification of the regional distribution function were used, the HW method and the method of Kjeldsen and Prosdocimi based on a bivariate extension of the HW measure. Subsequently, the flood quantile estimates were calculated using the index flood method. The ordinary least squares (OLS) and the generalised least squares (GLS) regression techniques were used to relate the index flood to catchment characteristics. Predictive performance of the regression scheme for the southern part of the Upper Vistula River basin was improved by using GLS instead of OLS. The results of the study can be recommended for the estimation of flood quantiles at ungauged sites, in flood risk mapping applications, and in engineering hydrology to help design flood protection structures.

Rotorcraft control system design for uncertain vehicle dynamics using quantitative feedback theory

NASA Technical Reports Server (NTRS)

Hess, R. A.

1994-01-01

Quantitative Feedback Theory describes a frequency-domain technique for the design of multi-input, multi-output control systems which must meet time or frequency domain performance criteria when specified uncertainty exists in the linear description of the vehicle dynamics. This theory is applied to the design of the longitudinal flight control system for a linear model of the BO-105C rotorcraft. Uncertainty in the vehicle model is due to the variation in the vehicle dynamics over a range of airspeeds from 0-100 kts. For purposes of exposition, the vehicle description contains no rotor or actuator dynamics. The design example indicates the manner in which significant uncertainty exists in the vehicle model. The advantage of using a sequential loop closure technique to reduce the cost of feedback is demonstrated by example.

Explaining Variation in Instructional Time: An Application of Quantile Regression

ERIC Educational Resources Information Center

Corey, Douglas Lyman; Phelps, Geoffrey; Ball, Deborah Loewenberg; Demonte, Jenny; Harrison, Delena

2012-01-01

This research is conducted in the context of a large-scale study of three nationally disseminated comprehensive school reform projects (CSRs) and examines how school- and classroom-level factors contribute to variation in instructional time in English language arts and mathematics. When using mean-based OLS regression techniques such as…

Public health impacts of ecosystem change in the Brazilian Amazon

PubMed Central

Bauch, Simone C.; Birkenbach, Anna M.; Pattanayak, Subhrendu K.; Sills, Erin O.

2015-01-01

The claim that nature delivers health benefits rests on a thin empirical evidence base. Even less evidence exists on how specific conservation policies affect multiple health outcomes. We address these gaps in knowledge by combining municipal-level panel data on diseases, public health services, climatic factors, demographics, conservation policies, and other drivers of land-use change in the Brazilian Amazon. To fully exploit this dataset, we estimate random-effects and quantile regression models of disease incidence. We find that malaria, acute respiratory infection (ARI), and diarrhea incidence are significantly and negatively correlated with the area under strict environmental protection. Results vary by disease for other types of protected areas (PAs), roads, and mining. The relationships between diseases and land-use change drivers also vary by quantile of the disease distribution. Conservation scenarios based on estimated regression results suggest that malaria, ARI, and diarrhea incidence would be reduced by expanding strict PAs, and malaria could be further reduced by restricting roads and mining. Although these relationships are complex, we conclude that interventions to preserve natural capital can deliver cobenefits by also increasing human (health) capital. PMID:26082548

Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies.

PubMed

Li, Zhixun; Zhang, Yingtao; Gong, Huiling; Li, Weimin; Tang, Xianglong

2016-12-01

Coronary artery disease has become the most dangerous diseases to human life. And coronary artery segmentation is the basis of computer aided diagnosis and analysis. Existing segmentation methods are difficult to handle the complex vascular texture due to the projective nature in conventional coronary angiography. Due to large amount of data and complex vascular shapes, any manual annotation has become increasingly unrealistic. A fully automatic segmentation method is necessary in clinic practice. In this work, we study a method based on reliable boundaries via multi-domains remapping and robust discrepancy correction via distance balance and quantile regression for automatic coronary artery segmentation of angiography images. The proposed method can not only segment overlapping vascular structures robustly, but also achieve good performance in low contrast regions. The effectiveness of our approach is demonstrated on a variety of coronary blood vessels compared with the existing methods. The overall segmentation performances si, fnvf, fvpf and tpvf were 95.135%, 3.733%, 6.113%, 96.268%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

NASA Astrophysics Data System (ADS)

Lopez, Patricia; Verkade, Jan; Weerts, Albrecht; Solomatine, Dimitri

2014-05-01

Hydrological forecasting is subject to many sources of uncertainty, including those originating in initial state, boundary conditions, model structure and model parameters. Although uncertainty can be reduced, it can never be fully eliminated. Statistical post-processing techniques constitute an often used approach to estimate the hydrological predictive uncertainty, where a model of forecast error is built using a historical record of past forecasts and observations. The present study focuses on the use of the Quantile Regression (QR) technique as a hydrological post-processor. It estimates the predictive distribution of water levels using deterministic water level forecasts as predictors. This work aims to thoroughly verify uncertainty estimates using the implementation of QR that was applied in an operational setting in the UK National Flood Forecasting System, and to inter-compare forecast quality and skill in various, differing configurations of QR. These configurations are (i) 'classical' QR, (ii) QR constrained by a requirement that quantiles do not cross, (iii) QR derived on time series that have been transformed into the Normal domain (Normal Quantile Transformation - NQT), and (iv) a piecewise linear derivation of QR models. The QR configurations are applied to fourteen hydrological stations on the Upper Severn River with different catchments characteristics. Results of each QR configuration are conditionally verified for progressively higher flood levels, in terms of commonly used verification metrics and skill scores. These include Brier's probability score (BS), the continuous ranked probability score (CRPS) and corresponding skill scores as well as the Relative Operating Characteristic score (ROCS). Reliability diagrams are also presented and analysed. The results indicate that none of the four Quantile Regression configurations clearly outperforms the others.

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

NASA Astrophysics Data System (ADS)

Santri, Dewi; Wigena, Aji Hamim; Djuraidah, Anik

2016-02-01

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th.

Evidence accumulation in decision making: unifying the "take the best" and the "rational" models.

PubMed

Lee, Michael D; Cummins, Tarrant D R

2004-04-01

An evidence accumulation model of forced-choice decision making is proposed to unify the fast and frugal take the best (TTB) model and the alternative rational (RAT) model with which it is usually contrasted. The basic idea is to treat the TTB model as a sequential-sampling process that terminates as soon as any evidence in favor of a decision is found and the rational approach as a sequential-sampling process that terminates only when all available information has been assessed. The unified TTB and RAT models were tested in an experiment in which participants learned to make correct judgments for a set of real-world stimuli on the basis of feedback, and were then asked to make additional judgments without feedback for cases in which the TTB and the rational models made different predictions. The results show that, in both experiments, there was strong intraparticipant consistency in the use of either the TTB or the rational model but large interparticipant differences in which model was used. The unified model is shown to be able to capture the differences in decision making across participants in an interpretable way and is preferred by the minimum description length model selection criterion.

Probing sensorimotor integration during musical performance.

PubMed

Furuya, Shinichi; Furukawa, Yuta; Uehara, Kazumasa; Oku, Takanori

2018-03-10

An integration of afferent sensory information from the visual, auditory, and proprioceptive systems into execution and update of motor programs plays crucial roles in control and acquisition of skillful sequential movements in musical performance. However, conventional behavioral and neurophysiological techniques that have been applied to study simplistic motor behaviors limit elucidating online sensorimotor integration processes underlying skillful musical performance. Here, we propose two novel techniques that were developed to investigate the roles of auditory and proprioceptive feedback in piano performance. First, a closed-loop noninvasive brain stimulation system that consists of transcranial magnetic stimulation, a motion sensor, and a microcomputer enabled to assess time-varying cortical processes subserving auditory-motor integration during piano playing. Second, a force-field system capable of manipulating the weight of a piano key allowed for characterizing movement adaptation based on the feedback obtained, which can shed light on the formation of an internal representation of the piano. Results of neurophysiological and psychophysics experiments provided evidence validating these systems as effective means for disentangling computational and neural processes of sensorimotor integration in musical performance. © 2018 New York Academy of Sciences.

Laser-Based Monitoring of CH4, CO2, NH3, and H2S in Animal Farming—System Characterization and Initial Demonstration

PubMed Central

Jaworski, Piotr; Nikodem, Michał

2018-01-01

In this paper, we present a system for sequential detection of multiple gases using laser-based wavelength modulation spectroscopy (WMS) method combined with a Herriot-type multi-pass cell. Concentration of hydrogen sulfide (H2S), methane (CH4), carbon dioxide (CO2), and ammonia (NH3) are retrieved using three distributed feedback laser diodes operating at 1574.5 nm (H2S and CO2), 1651 nm (CH4), and 1531 nm (NH3). Careful adjustment of system parameters allows for H2S sensing at single parts-per-million by volume (ppmv) level with strongly reduced interference from adjacent CO2 transitions even at atmospheric pressure. System characterization in laboratory conditions is presented and the results from initial tests in real-world application are demonstrated. PMID:29425175

Behavioral preference in sequential decision-making and its association with anxiety.

PubMed

Zhang, Dandan; Gu, Ruolei

2018-06-01

In daily life, people often make consecutive decisions before the ultimate goal is reached (i.e., sequential decision-making). However, this kind of decision-making has been largely overlooked in the literature. The current study investigated whether behavioral preference would change during sequential decisions, and the neural processes underlying the potential changes. For this purpose, we revised the classic balloon analogue risk task and recorded the electroencephalograph (EEG) signals associated with each step of decision-making. Independent component analysis performed on EEG data revealed that four EEG components elicited by periodic feedback in the current step predicted participants' decisions (gamble vs. no gamble) in the next step. In order of time sequence, these components were: bilateral occipital alpha rhythm, bilateral frontal theta rhythm, middle frontal theta rhythm, and bilateral sensorimotor mu rhythm. According to the information flows between these EEG oscillations, we proposed a brain model that describes the temporal dynamics of sequential decision-making. Finally, we found that the tendency to gamble (as well as the power intensity of bilateral frontal theta rhythms) was sensitive to the individual level of trait anxiety in certain steps, which may help understand the role of emotion in decision-making. © 2018 Wiley Periodicals, Inc.

Secure Learning and Learning for Security: Research in the Intersection

DTIC Science & Technology

2010-05-13

researchers to consider how Machine Learning and Statistics might be leveraged for constructing intelli - gent attacks. In a similar vein, security...Quantiles S am pl e Q ua nt ile s...8217 Residuals in Flow 144 Theoretical Quantiles S am pl e Q ua nt ile s 0 1 2 3 4 5 6 7 5. 0e + 07 1. 0e + 08 1. 5e + 08 Comparing Actual and Synthetic

Analysis of the labor productivity of enterprises via quantile regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2017-07-01

In this study, we have analyzed the factors that affect the performance of Turkey's Top 500 Industrial Enterprises using quantile regression. The variable about labor productivity of enterprises is considered as dependent variable, the variableabout assets is considered as independent variable. The distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, linear regression could not catch important aspects of the relationships between the dependent variable and its predictors due to modeling only the conditional mean. Hence, the quantile regression, which allows modelingany quantilesof the dependent distribution, including the median,appears to be useful. It examines whether relationships between dependent and independent variables are different for low, medium, and high percentiles. As a result of analyzing data, the effect of total assets is relatively constant over the entire distribution, except the upper tail. It hasa moderately stronger effect in the upper tail.

An Investigation of Factors Influencing Nurses' Clinical Decision-Making Skills.

PubMed

Wu, Min; Yang, Jinqiu; Liu, Lingying; Ye, Benlan

2016-08-01

This study aims to investigate the influencing factors on nurses' clinical decision-making (CDM) skills. A cross-sectional nonexperimental research design was conducted in the medical, surgical, and emergency departments of two university hospitals, between May and June 2014. We used a quantile regression method to identify the influencing factors across different quantiles of the CDM skills distribution and compared the results with the corresponding ordinary least squares (OLS) estimates. Our findings revealed that nurses were best at the skills of managing oneself. Educational level, experience, and the total structural empowerment had significant positive impacts on nurses' CDM skills, while the nurse-patient relationship, patient care and interaction, formal empowerment, and information empowerment were negatively correlated with nurses' CDM skills. These variables explained no more than 30% of the variance in nurses' CDM skills and mainly explained the lower quantiles of nurses' CDM skills distribution. © The Author(s) 2016.

Effects of export concentration on CO2 emissions in developed countries: an empirical analysis.

PubMed

Apergis, Nicholas; Can, Muhlis; Gozgor, Giray; Lau, Chi Keung Marco

2018-03-08

This paper provides the evidence on the short- and the long-run effects of the export product concentration on the level of CO 2 emissions in 19 developed (high-income) economies, spanning the period 1962-2010. To this end, the paper makes use of the nonlinear panel unit root and cointegration tests with multiple endogenous structural breaks. It also considers the mean group estimations, the autoregressive distributed lag model, and the panel quantile regression estimations. The findings illustrate that the environmental Kuznets curve (EKC) hypothesis is valid in the panel dataset of 19 developed economies. In addition, it documents that a higher level of the product concentration of exports leads to lower CO 2 emissions. The results from the panel quantile regressions also indicate that the effect of the export product concentration upon the per capita CO 2 emissions is relatively high at the higher quantiles.

Heterogeneity in Smokers' Responses to Tobacco Control Policies.

PubMed

Nesson, Erik

2017-02-01

This paper uses unconditional quantile regression to estimate whether smokers' responses to tobacco control policies change across the distribution of smoking levels. I measure smoking behavior with the number of cigarettes smoked per day and also with serum cotinine levels, a continuous biomarker of nicotine exposure, using individual-level repeated cross-section data from the National Health and Nutrition Examination Surveys. I find that the cigarette taxes lead to reductions in both the number of cigarettes smoked per day and in smokers' cotinine levels. These reductions are most pronounced in the middle quantiles of both distributions in terms of marginal effects, but most pronounced in the lower quantiles in terms of tax elasticities. I do not find that higher cigarette taxes lead to statistically significant changes in the amount of nicotine smokers ingest from each cigarette. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Simulating Quantile Models with Applications to Economics and Management

NASA Astrophysics Data System (ADS)

Machado, José A. F.

2010-05-01

The massive increase in the speed of computers over the past forty years changed the way that social scientists, applied economists and statisticians approach their trades and also the very nature of the problems that they could feasibly tackle. The new methods that use intensively computer power go by the names of "computer-intensive" or "simulation". My lecture will start with bird's eye view of the uses of simulation in Economics and Statistics. Then I will turn out to my own research on uses of computer- intensive methods. From a methodological point of view the question I address is how to infer marginal distributions having estimated a conditional quantile process, (Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression," Journal of Applied Econometrics 20, 2005). Illustrations will be provided of the use of the method to perform counterfactual analysis in several different areas of knowledge.

Kinematic evidence for feedback-driven star formation in NGC 1893

NASA Astrophysics Data System (ADS)

Lim, Beomdu; Sung, Hwankyung; Bessell, Michael S.; Lee, Sangwoo; Lee, Jae Joon; Oh, Heeyoung; Hwang, Narae; Park, Byeong-Gon; Hur, Hyeonoh; Hong, Kyeongsoo; Park, Sunkyung

2018-06-01

OB associations are the prevailing star-forming sites in the Galaxy. Up to now, the process of how OB associations were formed remained a mystery. A possible process is self-regulating star formation driven by feedback from massive stars. However, although a number of observational studies uncovered various signposts of feedback-driven star formation, the effectiveness of such feedback has been questioned. Stellar and gas kinematics is a promising tool to capture the relative motion of newborn stars and gas away from ionizing sources. We present high-resolution spectroscopy of stars and gas in the young open cluster NGC 1893. Our findings show that newborn stars and the tadpole nebula Sim 130 are moving away from the central cluster containing two O-type stars, and that the time-scale of sequential star formation is about 1 Myr within a 9 pc distance. The newborn stars formed by feedback from massive stars account for at least 18 per cent of the total stellar population in the cluster, suggesting that this process can play an important role in the formation of OB associations. These results support the self-regulating star formation model.

Learning the Art of Electronics

NASA Astrophysics Data System (ADS)

Hayes, Thomas C.; Horowitz, Paul

2016-03-01

1. DC circuits; 2. RC circuits; 3. Diode circuits; 4. Transistors I; 5. Transistors II; 6. Operational amplifiers I; 7. Operational amplifiers II: nice positive feedback; 8. Operational amplifiers III; 9. Operational amplifiers IV: nasty positive feedback; 10. Operational amplifiers V: PID motor control loop; 11. Voltage regulators; 12. MOSFET switches; 13. Group audio project; 14. Logic gates; 15. Logic compilers, sequential circuits, flip-flops; 16. Counters; 17. Memory: state machines; 18. Analog to digital: phase-locked loop; 19. Microcontrollers and microprocessors I: processor/controller; 20. I/O, first assembly language; 21. Bit operations; 22. Interrupt: ADC and DAC; 23. Moving pointers, serial buses; 24. Dallas Standalone Micro, SiLabs SPI RAM; 25. Toys in the attic; Appendices; Index.

Robust parameter design for automatically controlled systems and nanostructure synthesis

NASA Astrophysics Data System (ADS)

Dasgupta, Tirthankar

2007-12-01

This research focuses on developing comprehensive frameworks for developing robust parameter design methodology for dynamic systems with automatic control and for synthesis of nanostructures. In many automatically controlled dynamic processes, the optimal feedback control law depends on the parameter design solution and vice versa and therefore an integrated approach is necessary. A parameter design methodology in the presence of feedback control is developed for processes of long duration under the assumption that experimental noise factors are uncorrelated over time. Systems that follow a pure-gain dynamic model are considered and the best proportional-integral and minimum mean squared error control strategies are developed by using robust parameter design. The proposed method is illustrated using a simulated example and a case study in a urea packing plant. This idea is also extended to cases with on-line noise factors. The possibility of integrating feedforward control with a minimum mean squared error feedback control scheme is explored. To meet the needs of large scale synthesis of nanostructures, it is critical to systematically find experimental conditions under which the desired nanostructures are synthesized reproducibly, at large quantity and with controlled morphology. The first part of the research in this area focuses on modeling and optimization of existing experimental data. Through a rigorous statistical analysis of experimental data, models linking the probabilities of obtaining specific morphologies to the process variables are developed. A new iterative algorithm for fitting a Multinomial GLM is proposed and used. The optimum process conditions, which maximize the above probabilities and make the synthesis process less sensitive to variations of process variables around set values, are derived from the fitted models using Monte-Carlo simulations. The second part of the research deals with development of an experimental design methodology, tailor-made to address the unique phenomena associated with nanostructure synthesis. A sequential space filling design called Sequential Minimum Energy Design (SMED) for exploring best process conditions for synthesis of nanowires. The SMED is a novel approach to generate sequential designs that are model independent, can quickly "carve out" regions with no observable nanostructure morphology, and allow for the exploration of complex response surfaces.

A robust rotorcraft flight control system design methodology utilizing quantitative feedback theory

NASA Technical Reports Server (NTRS)

Gorder, Peter James

1993-01-01

Rotorcraft flight control systems present design challenges which often exceed those associated with fixed-wing aircraft. First, large variations in the response characteristics of the rotorcraft result from the wide range of airspeeds of typical operation (hover to over 100 kts). Second, the assumption of vehicle rigidity often employed in the design of fixed-wing flight control systems is rarely justified in rotorcraft where rotor degrees of freedom can have a significant impact on the system performance and stability. This research was intended to develop a methodology for the design of robust rotorcraft flight control systems. Quantitative Feedback Theory (QFT) was chosen as the basis for the investigation. Quantitative Feedback Theory is a technique which accounts for variability in the dynamic response of the controlled element in the design robust control systems. It was developed to address a Multiple-Input Single-Output (MISO) design problem, and utilizes two degrees of freedom to satisfy the design criteria. Two techniques were examined for extending the QFT MISO technique to the design of a Multiple-Input-Multiple-Output (MIMO) flight control system (FCS) for a UH-60 Black Hawk Helicopter. In the first, a set of MISO systems, mathematically equivalent to the MIMO system, was determined. QFT was applied to each member of the set simultaneously. In the second, the same set of equivalent MISO systems were analyzed sequentially, with closed loop response information from each loop utilized in subsequent MISO designs. The results of each technique were compared, and the advantages of the second, termed Sequential Loop Closure, were clearly evident.

Variability in reaction time performance of younger and older adults.

PubMed

Hultsch, David F; MacDonald, Stuart W S; Dixon, Roger A

2002-03-01

Age differences in three basic types of variability were examined: variability between persons (diversity), variability within persons across tasks (dispersion), and variability within persons across time (inconsistency). Measures of variability were based on latency performance from four measures of reaction time (RT) performed by a total of 99 younger adults (ages 17--36 years) and 763 older adults (ages 54--94 years). Results indicated that all three types of variability were greater in older compared with younger participants even when group differences in speed were statistically controlled. Quantile-quantile plots showed age and task differences in the shape of the inconsistency distributions. Measures of within-person variability (dispersion and inconsistency) were positively correlated. Individual differences in RT inconsistency correlated negatively with level of performance on measures of perceptual speed, working memory, episodic memory, and crystallized abilities. Partial set correlation analyses indicated that inconsistency predicted cognitive performance independent of level of performance. The results indicate that variability of performance is an important indicator of cognitive functioning and aging.

Statistics of concentrations due to single air pollution sources to be applied in numerical modelling of pollutant dispersion

NASA Astrophysics Data System (ADS)

Tumanov, Sergiu

A test of goodness of fit based on rank statistics was applied to prove the applicability of the Eggenberger-Polya discrete probability law to hourly SO 2-concentrations measured in the vicinity of single sources. With this end in view, the pollutant concentration was considered an integral quantity which may be accepted if one properly chooses the unit of measurement (in this case μg m -3) and if account is taken of the limited accuracy of measurements. The results of the test being satisfactory, even in the range of upper quantiles, the Eggenberger-Polya law was used in association with numerical modelling to estimate statistical parameters, e.g. quantiles, cumulative probabilities of threshold concentrations to be exceeded, and so on, in the grid points of a network covering the area of interest. This only needs accurate estimations of means and variances of the concentration series which can readily be obtained through routine air pollution dispersion modelling.

Incremental Treatment Costs Attributable to Overweight and Obesity in Patients with Diabetes: Quantile Regression Approach.

PubMed

Lee, Seung-Mi; Choi, In-Sun; Han, Euna; Suh, David; Shin, Eun-Kyung; Je, Seyunghe; Lee, Sung Su; Suh, Dong-Churl

2018-01-01

This study aimed to estimate treatment costs attributable to overweight and obesity in patients with diabetes who were less than 65 years of age in the United States. This study used data from the Medical Expenditure Panel Survey from 2001 to 2013. Patients with diabetes were identified by using the International Classification of Diseases, Ninth Revision, Clinical Modification code (250), clinical classification codes (049 and 050), or self-reported physician diagnoses. Total treatment costs attributable to overweight and obesity were calculated as the differences in the adjusted costs compared with individuals with diabetes and normal weight. Adjusted costs were estimated by using generalized linear models or unconditional quantile regression models. The mean annual treatment costs attributable to obesity were $1,852 higher than those attributable to normal weight, while costs attributable to overweight were $133 higher. The unconditional quantile regression results indicated that the impact of obesity on total treatment costs gradually became more significant as treatment costs approached the upper quantile. Among patients with diabetes who were less than 65 years of age, patients with diabetes and obesity have significantly higher treatment costs than patients with diabetes and normal weight. The economic burden of diabetes to society will continue to increase unless more proactive preventive measures are taken to effectively treat patients with overweight or obesity. © 2017 The Obesity Society.

Customized Fetal Growth Charts for Parents' Characteristics, Race, and Parity by Quantile Regression Analysis: A Cross-sectional Multicenter Italian Study.

PubMed

Ghi, Tullio; Cariello, Luisa; Rizzo, Ludovica; Ferrazzi, Enrico; Periti, Enrico; Prefumo, Federico; Stampalija, Tamara; Viora, Elsa; Verrotti, Carla; Rizzo, Giuseppe

2016-01-01

The purpose of this study was to construct fetal biometric charts between 16 and 40 weeks' gestation that were customized for parental characteristics, race, and parity, using quantile regression analysis. In a multicenter cross-sectional study, 8070 sonographic examinations from low-risk pregnancies between 16 and 40 weeks' gestation were analyzed. The fetal measurements obtained were biparietal diameter, head circumference, abdominal circumference, and femur diaphysis length. Quantile regression was used to examine the impact of parental height and weight, parity, and race across biometric percentiles for the fetal measurements considered. Paternal and maternal height were significant covariates for all of the measurements considered (P < .05). Maternal weight significantly influenced head circumference, abdominal circumference, and femur diaphysis length. Parity was significantly associated with biparietal diameter and head circumference. Central African race was associated with head circumference and femur diaphysis length, whereas North African race was only associated with femur diaphysis length. In this study we constructed customized biometric growth charts using quantile regression in a large cohort of low-risk pregnancies. These charts offer the advantage of defining individualized normal ranges of fetal biometric parameters at each specific percentile corrected for parental height and weight, parity, and race. This study supports the importance of including these variables in routine sonographic screening for fetal growth abnormalities.

Height premium for job performance.

PubMed

Kim, Tae Hyun; Han, Euna

2017-08-01

This study assessed the relationship of height with wages, using the 1998 and 2012 Korean Labor and Income Panel Study data. The key independent variable was height measured in centimeters, which was included as a series of dummy indicators of height per 5cm span (<155cm, 155-160cm, 160-165cm, and ≥165cm for women; <165cm, 165-170cm, 170-175cm, 175-180cm, and ≥180cm for men). We controlled for household- and individual-level random effects. We used a random-effect quantile regression model for monthly wages to assess the heterogeneity in the height-wage relationship, across the conditional distribution of monthly wages. We found a non-linear relationship of height with monthly wages. For men, the magnitude of the height wage premium was overall larger at the upper quantile of the conditional distribution of log monthly wages than at the median to low quantile, particularly in professional and semi-professional occupations. The height-wage premium was also larger at the 90th quantile for self-employed women and salaried men. Our findings add a global dimension to the existing evidence on height-wage premium, demonstrating non-linearity in the association between height and wages and heterogeneous changes in the dispersion and direction of the association between height and wages, by wage level. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation.

PubMed

Smeers, Inge; Decorte, Ronny; Van de Voorde, Wim; Bekaert, Bram

2018-05-01

DNA methylation is a promising biomarker for forensic age prediction. A challenge that has emerged in recent studies is the fact that prediction errors become larger with increasing age due to interindividual differences in epigenetic ageing rates. This phenomenon of non-constant variance or heteroscedasticity violates an assumption of the often used method of ordinary least squares (OLS) regression. The aim of this study was to evaluate alternative statistical methods that do take heteroscedasticity into account in order to provide more accurate, age-dependent prediction intervals. A weighted least squares (WLS) regression is proposed as well as a quantile regression model. Their performances were compared against an OLS regression model based on the same dataset. Both models provided age-dependent prediction intervals which account for the increasing variance with age, but WLS regression performed better in terms of success rate in the current dataset. However, quantile regression might be a preferred method when dealing with a variance that is not only non-constant, but also not normally distributed. Ultimately the choice of which model to use should depend on the observed characteristics of the data. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies.

PubMed

Letué, Frédérique; Martinez, Marie-José; Samson, Adeline; Vilain, Anne; Vilain, Coriandre

2018-03-15

Repeated duration data are frequently used in behavioral studies. Classical linear or log-linear mixed models are often inadequate to analyze such data, because they usually consist of nonnegative and skew-distributed variables. Therefore, we recommend use of a statistical methodology specific to duration data. We propose a methodology based on Cox mixed models and written under the R language. This semiparametric model is indeed flexible enough to fit duration data. To compare log-linear and Cox mixed models in terms of goodness-of-fit on real data sets, we also provide a procedure based on simulations and quantile-quantile plots. We present two examples from a data set of speech and gesture interactions, which illustrate the limitations of linear and log-linear mixed models, as compared to Cox models. The linear models are not validated on our data, whereas Cox models are. Moreover, in the second example, the Cox model exhibits a significant effect that the linear model does not. We provide methods to select the best-fitting models for repeated duration data and to compare statistical methodologies. In this study, we show that Cox models are best suited to the analysis of our data set.

Percentile-Based ETCCDI Temperature Extremes Indices for CMIP5 Model Output: New Results through Semiparametric Quantile Regression Approach

NASA Astrophysics Data System (ADS)

Li, L.; Yang, C.

2017-12-01

Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP8.5) emission scenarios run a multimodel ensemble of 19 members. We analyze changes in the six indices on global and regional scales over the 21st century relative to either the base period 1961-1990 or the reference period 1981-2000, and compare the results with those based on the CLIMDEX datasets.

Overview of progesterone profiles in dairy cows.

PubMed

Blavy, P; Derks, M; Martin, O; Höglund, J K; Friggens, N C

2016-09-01

The aim of this study was to gain a better understanding of the variability in shape and features of all progesterone profiles during estrus cycles in cows and to create templates for cycle shapes and features as a base for further research. Milk progesterone data from 1418 estrus cycles, coming from 1009 lactations, was obtained from the Danish Cattle Research Centre in Foulum, Denmark. Milk samples were analyzed daily using a Ridgeway ELISA-kit. Estrus cycles with less than 10 data points or shorter than 4 days were discarded, after which 1006 cycles remained in the analysis. A median kernel of three data points was used to smooth the progesterone time series. The time between start of progesterone rise and end of progesterone decline was identified by fitting a simple model consisting of base length and a quadratic curve to progesterone data, and this luteal-like phase (LLP) was used for further analysis. The data set of 1006 LLP's was divided into five quantiles based on length. Within quantiles, a cluster analysis was performed on the basis of shape distance. Height, upward and downward slope, and progesterone level on Day 5 were compared between quantiles. Also, the ratio of typical versus atypical shapes was described, using a reference curve on the basis of data in Q1-Q4. The main results of this article were that (1) most of the progesterone profiles showed a typical profile, including the ones that exceeded the optimum cycle length of 24 days; (2) cycles in Q2 and Q3 had steeper slopes and higher peak progesterone levels than cycles in Q1 and Q4 but, when normalized, had a similar shape. Results were used to define differences between quantiles that can be used as templates. Compared to Q1, LLP's in Q2 had a shape that is 1.068 times steeper and 1.048 times higher. Luteal-like phases in Q3 were 1.053 times steeper and 1.018 times higher. Luteal-like phases in Q4 were 0.977 times steeper and 0.973 times higher than LLP's in Q1. This article adds to our knowledge about the variability of progesterone profiles and their shape differences. The profile clustering procedure described in this article can be used as a means to classify progesterone profiles without recourse to an a priori set of rules, which arbitrarily segment the natural variability in these profiles. Using data-derived profile shapes may allow a more accurate assessment of the effects of, e.g., nutritional management or breeding system on progesterone profiles. Copyright © 2016 Elsevier Inc. All rights reserved.

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

PubMed Central

2012-01-01

Background The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. Results We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. Conclusion T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially. PMID:22276688

Quantile-Specific Penetrance of Genes Affecting Lipoproteins, Adiposity and Height

PubMed Central

Williams, Paul T.

2012-01-01

Quantile-dependent penetrance is proposed to occur when the phenotypic expression of a SNP depends upon the population percentile of the phenotype. To illustrate the phenomenon, quantiles of height, body mass index (BMI), and plasma lipids and lipoproteins were compared to genetic risk scores (GRS) derived from single nucleotide polymorphisms (SNP)s having established genome-wide significance: 180 SNPs for height, 32 for BMI, 37 for low-density lipoprotein (LDL)-cholesterol, 47 for high-density lipoprotein (HDL)-cholesterol, 52 for total cholesterol, and 31 for triglycerides in 1930 subjects. Both phenotypes and GRSs were adjusted for sex, age, study, and smoking status. Quantile regression showed that the slope of the genotype-phenotype relationships increased with the percentile of BMI (P = 0.002), LDL-cholesterol (P = 3×10−8), HDL-cholesterol (P = 5×10−6), total cholesterol (P = 2.5×10−6), and triglyceride distribution (P = 7.5×10−6), but not height (P = 0.09). Compared to a GRS's phenotypic effect at the 10th population percentile, its effect at the 90th percentile was 4.2-fold greater for BMI, 4.9-fold greater for LDL-cholesterol, 1.9-fold greater for HDL-cholesterol, 3.1-fold greater for total cholesterol, and 3.3-fold greater for triglycerides. Moreover, the effect of the rs1558902 (FTO) risk allele was 6.7-fold greater at the 90th than the 10th percentile of the BMI distribution, and that of the rs3764261 (CETP) risk allele was 2.4-fold greater at the 90th than the 10th percentile of the HDL-cholesterol distribution. Conceptually, it maybe useful to distinguish environmental effects on the phenotype that in turn alters a gene's phenotypic expression (quantile-dependent penetrance) from environmental effects affecting the gene's phenotypic expression directly (gene-environment interaction). PMID:22235250

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots.

PubMed

Washington, Simon; Haque, Md Mazharul; Oh, Jutaek; Lee, Dongmin

2014-05-01

Hot spot identification (HSID) aims to identify potential sites-roadway segments, intersections, crosswalks, interchanges, ramps, etc.-with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effect of threatening life experiences and adverse family relations in ulcerative colitis: analysis using structural equation modeling and comparison with Crohn's disease.

PubMed

Slonim-Nevo, Vered; Sarid, Orly; Friger, Michael; Schwartz, Doron; Sergienko, Ruslan; Pereg, Avihu; Vardi, Hillel; Singer, Terri; Chernin, Elena; Greenberg, Dan; Odes, Shmuel

2017-05-01

We published that threatening life experiences and adverse family relations impact Crohn's disease (CD) adversely. In this study, we examine the influence of these stressors in ulcerative colitis (UC). Patients completed demography, economic status (ES), the Patient-Simple Clinical Colitis Activity Index (P-SCCAI), the Short Inflammatory Bowel Disease Questionnaire (SIBDQ), the Short-Form Health Survey (SF-36), the Brief Symptom Inventory (BSI), the Family Assessment Device (FAD), and the List of Threatening Life Experiences (LTE). Analysis included multiple linear and quantile regressions and structural equation modeling, comparing CD. UC patients (N=148, age 47.55±16.04 years, 50.6% women) had scores [median (interquartile range)] as follows: SCAAI, 2 (0.3-4.8); FAD, 1.8 (1.3-2.2); LTE, 1.0 (0-2.0); SF-36 Physical Health, 49.4 (36.8-55.1); SF-36 Mental Health, 45 (33.6-54.5); Brief Symptom Inventory-Global Severity Index (GSI), 0.5 (0.2-1.0). SIBDQ was 49.76±14.91. There were significant positive associations for LTE and SCAAI (25, 50, 75% quantiles), FAD and SF-36 Mental Health, FAD and LTE with GSI (50, 75, 90% quantiles), and ES with SF-36 and SIBDQ. The negative associations were as follows: LTE with SF-36 Physical/Mental Health, SIBDQ with FAD and LTE, ES with GSI (all quantiles), and P-SCCAI (75, 90% quantiles). In structural equation modeling analysis, LTE impacted ES negatively and ES impacted GSI negatively; LTE impacted GSI positively and GSI impacted P-SCCAI positively. In a split model, ES had a greater effect on GSI in UC than CD, whereas other path magnitudes were similar. Threatening life experiences, adverse family relations, and poor ES make UC patients less healthy both physically and mentally. The impact of ES is worse in UC than CD.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

2017-02-01

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

Data-driven modeling of surface temperature anomaly and solar activity trends

USGS Publications Warehouse

Friedel, Michael J.

2012-01-01

A novel two-step modeling scheme is used to reconstruct and analyze surface temperature and solar activity data at global, hemispheric, and regional scales. First, the self-organizing map (SOM) technique is used to extend annual modern climate data from the century to millennial scale. The SOM component planes are used to identify and quantify strength of nonlinear relations among modern surface temperature anomalies (<150 years), tropical and extratropical teleconnections, and Palmer Drought Severity Indices (0–2000 years). Cross-validation of global sea and land surface temperature anomalies verifies that the SOM is an unbiased estimator with less uncertainty than the magnitude of anomalies. Second, the quantile modeling of SOM reconstructions reveal trends and periods in surface temperature anomaly and solar activity whose timing agrees with published studies. Temporal features in surface temperature anomalies, such as the Medieval Warm Period, Little Ice Age, and Modern Warming Period, appear at all spatial scales but whose magnitudes increase when moving from ocean to land, from global to regional scales, and from southern to northern regions. Some caveats that apply when interpreting these data are the high-frequency filtering of climate signals based on quantile model selection and increased uncertainty when paleoclimatic data are limited. Even so, all models find the rate and magnitude of Modern Warming Period anomalies to be greater than those during the Medieval Warm Period. Lastly, quantile trends among reconstructed equatorial Pacific temperature profiles support the recent assertion of two primary El Niño Southern Oscillation types. These results demonstrate the efficacy of this alternative modeling approach for reconstructing and interpreting scale-dependent climate variables.

Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video.

PubMed

Gunay, Osman; Toreyin, Behçet Ugur; Kose, Kivanc; Cetin, A Enis

2012-05-01

In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.

The quality and value of seasonal precipitation forecasts for an early warning of large-scale droughts and floods in West Africa

NASA Astrophysics Data System (ADS)

Bliefernicht, Jan; Seidel, Jochen; Salack, Seyni; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

2017-04-01

Seasonal precipitation forecasts are a crucial source of information for an early warning of hydro-meteorological extremes in West Africa. However, the current seasonal forecasting system used by the West African weather services in the framework of the West African Climate Outlook forum (PRESAO) is limited to probabilistic precipitation forecasts of 1-month lead time. To improve this provision, we use an ensemble-based quantile-quantile transformation for bias correction of precipitation forecasts provided by a global seasonal ensemble prediction system, the Climate Forecast System Version 2 (CFS2). The statistical technique eliminates systematic differences between global forecasts and observations with the potential to preserve the signal from the model. The technique has also the advantage that it can be easily implemented at national weather services with low capacities. The statistical technique is used to generate probabilistic forecasts of monthly and seasonal precipitation amount and other precipitation indices useful for an early warning of large-scale drought and floods in West Africa. The evaluation of the statistical technique is done using CFS hindcasts (1982 to 2009) in a cross-validation mode to determine the performance of the precipitation forecasts for several lead times focusing on drought and flood events depicted over the Volta and Niger basins. In addition, operational forecasts provided by PRESAO are analyzed from 1998 to 2015. The precipitation forecasts are compared to low-skill reference forecasts generated from gridded observations (i.e. GPCC, CHIRPS) and a novel in-situ gauge database from national observation networks (see Poster EGU2017-10271). The forecasts are evaluated using state-of-the-art verification techniques to determine specific quality attributes of probabilistic forecasts such as reliability, accuracy and skill. In addition, cost-loss approaches are used to determine the value of probabilistic forecasts for multiple users in warning situations. The outcomes of the hindcasts experiment for the Volta basin illustrate that the statistical technique can clearly improve the CFS precipitation forecasts with the potential to provide skillful and valuable early precipitation warnings for large-scale drought and flood situations several months in ahead. In this presentation we give a detailed overview about the ensemble-based quantile-quantile-transformation, its validation and verification and the possibilities of this technique to complement PRESAO. We also highlight the performance of this technique for extremes such as the Sahel drought in the 80ties and in comparison to the various reference data sets (e.g. CFS2, PRESAO, observational data sets) used in this study.

Flood frequency analysis - the challenge of using historical data

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn

2015-04-01

Estimates of high flood quantiles are needed for many applications, .e.g. dam safety assessments are based on the 1000 years flood, whereas the dimensioning of important infrastructure requires estimates of the 200 year flood. The flood quantiles are estimated by fitting a parametric distribution to a dataset of high flows comprising either annual maximum values or peaks over a selected threshold. Since the record length of data is limited compared to the desired flood quantile, the estimated flood magnitudes are based on a high degree of extrapolation. E.g. the longest time series available in Norway are around 120 years, and as a result any estimation of a 1000 years flood will require extrapolation. One solution is to extend the temporal dimension of a data series by including information about historical floods before the stream flow was systematically gaugeded. Such information could be flood marks or written documentation about flood events. The aim of this study was to evaluate the added value of using historical flood data for at-site flood frequency estimation. The historical floods were included in two ways by assuming: (1) the size of (all) floods above a high threshold within a time interval is known; and (2) the number of floods above a high threshold for a time interval is known. We used a Bayesian model formulation, with MCMC used for model estimation. This estimation procedure allowed us to estimate the predictive uncertainty of flood quantiles (i.e. both sampling and parameter uncertainty is accounted for). We tested the methods using 123 years of systematic data from Bulken in western Norway. In 2014 the largest flood in the systematic record was observed. From written documentation and flood marks we had information from three severe floods in the 18th century and they were likely to exceed the 2014 flood. We evaluated the added value in two ways. First we used the 123 year long streamflow time series and investigated the effect of having several shorter series' which could be supplemented with a limited number of known large flood events. Then we used the three historical floods from the 18th century combined with the whole and subsets of the 123 years of systematic observations. In the latter case several challenges were identified: i) The possibility to transfer water levels to river streamflows due to man made changes in the river profile, (ii) The stationarity of the data might be questioned since the three largest historical floods occurred during the "little ice age" with different climatic conditions compared to today.

ALMA Reveals Sequential High-mass Star Formation in the G9.62+0.19 Complex

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Tie; Kim, Kee-Tae; Lacy, John

Stellar feedback from high-mass stars (e.g., H ii regions) can strongly influence the surrounding interstellar medium and regulate star formation. Our new ALMA observations reveal sequential high-mass star formation taking place within one subvirial filamentary clump (the G9.62 clump) in the G9.62+0.19 complex. The 12 dense cores (MM1–MM12) detected by ALMA are at very different evolutionary stages, from the starless core phase to the UC H ii region phase. Three dense cores (MM6, MM7/G, MM8/F) are associated with outflows. The mass–velocity diagrams of the outflows associated with MM7/G and MM8/F can be well-fit by broken power laws. The mass–velocity diagrammore » of the SiO outflow associated with MM8/F breaks much earlier than other outflow tracers (e.g., CO, SO, CS, HCN), suggesting that SiO traces newly shocked gas, while the other molecular lines (e.g., CO, SO, CS, HCN) mainly trace the ambient gas continuously entrained by outflow jets. Five cores (MM1, MM3, MM5, MM9, MM10) are massive starless core candidates whose masses are estimated to be larger than 25 M {sub ☉}, assuming a dust temperature of ≤20 K. The shocks from the expanding H ii regions (“B” and “C”) to the west may have a great impact on the G9.62 clump by compressing it into a filament and inducing core collapse successively, leading to sequential star formation. Our findings suggest that stellar feedback from H ii regions may enhance the star formation efficiency and suppress low-mass star formation in adjacent pre-existing massive clumps.« less

A minimal mathematical model combining several regulatory cycles from the budding yeast cell cycle.

PubMed

Sriram, K; Bernot, G; Képès, F

2007-11-01

A novel topology of regulatory networks abstracted from the budding yeast cell cycle is studied by constructing a simple nonlinear model. A ternary positive feedback loop with only positive regulations is constructed with elements that activates the subsequent element in a clockwise fashion. A ternary negative feedback loop with only negative regulations is constructed with the elements that inhibit the subsequent element in an anticlockwise fashion. Positive feedback loop exhibits bistability, whereas the negative feedback loop exhibits limit cycle oscillations. The novelty of the topology is that the corresponding elements in these two homogeneous feedback loops are linked by the binary positive feedback loops with only positive regulations. This results in the emergence of mixed feedback loops in the network that displays complex behaviour like the coexistence of multiple steady states, relaxation oscillations and chaos. Importantly, the arrangement of the feedback loops brings in the notion of checkpoint in the model. The model also exhibits domino-like behaviour, where the limit cycle oscillations take place in a stepwise fashion. As the aforementioned topology is abstracted from the budding yeast cell cycle, the events that govern the cell cycle are considered for the present study. In budding yeast, the sequential activation of the transcription factors, cyclins and their inhibitors form mixed feedback loops. The transcription factors that involve in the positive regulation in a clockwise orientation generates ternary positive feedback loop, while the cyclins and their inhibitors that involve in the negative regulation in an anticlockwise orientation generates ternary negative feedback loop. The mutual regulation between the corresponding elements in the transcription factors and the cyclins and their inhibitors generates binary positive feedback loops. The bifurcation diagram constructed for the whole system can be related to the different events of the cell cycle in terms of dynamical system theory. The checkpoint mechanism that plays an important role in different phases of the cell cycle are accounted for by silencing appropriate feedback loops in the model.

Statistical bias correction method applied on CMIP5 datasets over the Indian region during the summer monsoon season for climate change applications

NASA Astrophysics Data System (ADS)

Prasanna, V.

2018-01-01

This study makes use of temperature and precipitation from CMIP5 climate model output for climate change application studies over the Indian region during the summer monsoon season (JJAS). Bias correction of temperature and precipitation from CMIP5 GCM simulation results with respect to observation is discussed in detail. The non-linear statistical bias correction is a suitable bias correction method for climate change data because it is simple and does not add up artificial uncertainties to the impact assessment of climate change scenarios for climate change application studies (agricultural production changes) in the future. The simple statistical bias correction uses observational constraints on the GCM baseline, and the projected results are scaled with respect to the changing magnitude in future scenarios, varying from one model to the other. Two types of bias correction techniques are shown here: (1) a simple bias correction using a percentile-based quantile-mapping algorithm and (2) a simple but improved bias correction method, a cumulative distribution function (CDF; Weibull distribution function)-based quantile-mapping algorithm. This study shows that the percentile-based quantile mapping method gives results similar to the CDF (Weibull)-based quantile mapping method, and both the methods are comparable. The bias correction is applied on temperature and precipitation variables for present climate and future projected data to make use of it in a simple statistical model to understand the future changes in crop production over the Indian region during the summer monsoon season. In total, 12 CMIP5 models are used for Historical (1901-2005), RCP4.5 (2005-2100), and RCP8.5 (2005-2100) scenarios. The climate index from each CMIP5 model and the observed agricultural yield index over the Indian region are used in a regression model to project the changes in the agricultural yield over India from RCP4.5 and RCP8.5 scenarios. The results revealed a better convergence of model projections in the bias corrected data compared to the uncorrected data. The study can be extended to localized regional domains aimed at understanding the changes in the agricultural productivity in the future with an agro-economy or a simple statistical model. The statistical model indicated that the total food grain yield is going to increase over the Indian region in the future, the increase in the total food grain yield is approximately 50 kg/ ha for the RCP4.5 scenario from 2001 until the end of 2100, and the increase in the total food grain yield is approximately 90 kg/ha for the RCP8.5 scenario from 2001 until the end of 2100. There are many studies using bias correction techniques, but this study applies the bias correction technique to future climate scenario data from CMIP5 models and applied it to crop statistics to find future crop yield changes over the Indian region.

Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London.

PubMed

Tonne, Cathryn; Milà, Carles; Fecht, Daniela; Alvarez, Mar; Gulliver, John; Smith, James; Beevers, Sean; Ross Anderson, H; Kelly, Frank

2018-06-01

Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise. Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources. We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter <2.5 μm and nitrogen dioxide (NO 2 ), road-traffic noise at residence (TRANEX) and identified those within 50 dB noise contours of railways and Heathrow airport. We analyzed relationships between household income, area-level income deprivation and ethnicity with air and noise pollution using quantile and logistic regression. We observed inverse patterns in inequalities in air pollution when estimated at residence versus personal exposure with respect to household income (categorical, 8 groups). Compared to the lowest income group (<£10,000), the highest group (>£75,000) had lower residential NO 2 (-1.3 (95% CI -2.1, -0.6) μg/m 3 in the 95th exposure quantile) but higher personal NO 2 exposure (1.9 (95% CI 1.6, 2.3) μg/m 3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO 2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO 2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO 2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age, household income, and area-level income deprivation. Inequalities in road traffic noise were generally small. In logistic regression models, the odds of living within a 50 dB contour of aircraft noise were highest in individuals with the highest household income, white ethnicity, and with the lowest area-level income deprivation. Odds of living within a 50 dB contour of rail noise were 19% (95% CI 3, 37) higher for black compared to white individuals. Socioeconomic inequalities in air pollution exposure were different for modeled residential versus personal exposure, which has important implications for environmental justice and confounding in epidemiology studies. Exposure misclassification was dependent on several factors related to health, a potential source of bias in epidemiological studies. Quantile regression revealed that socioeconomic and ethnic inequalities in air pollution are often not uniform across the exposure distribution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Bayesian Non-Stationary Flood Frequency Estimation at Ungauged Basins Using Climate Information and a Scaling Model

NASA Astrophysics Data System (ADS)

Lima, C. H.; Lall, U.

2010-12-01

Flood frequency statistical analysis most often relies on stationary assumptions, where distribution moments (e.g. mean, standard deviation) and associated flood quantiles do not change over time. In this sense, one expects that flood magnitudes and their frequency of occurrence will remain constant as observed in the historical information. However, evidence of inter-annual and decadal climate variability and anthropogenic change as well as an apparent increase in the number and magnitude of flood events across the globe have made the stationary assumption questionable. Here, we show how to estimate flood quantiles (e.g. 100-year flood) at ungauged basins without needing to consider stationarity. A statistical model based on the well known flow-area scaling law is proposed to estimate flood flows at ungauged basins. The slope and intercept scaling law coefficients are assumed time varying and a hierarchical Bayesian model is used to include climate information and reduce parameter uncertainties. Cross-validated results from 34 streamflow gauges located in a nested Basin in Brazil show that the proposed model is able to estimate flood quantiles at ungauged basins with remarkable skills compared with data based estimates using the full record. The model as developed in this work is also able to simulate sequences of flood flows considering global climate changes provided an appropriate climate index developed from the General Circulation Model is used as a predictor. The time varying flood frequency estimates can be used for pricing insurance models, and in a forecast mode for preparations for flooding, and finally, for timing infrastructure investments and location. Non-stationary 95% interval estimation for the 100-year Flood (shaded gray region) and 95% interval for the 100-year flood estimated from data (horizontal dashed and solid lines). The average distribution of the 100-year flood is shown in green in the right side.

Analysis and trends of precipitation lapse rate and extreme indices over north Sikkim eastern Himalayas under CMIP5ESM-2M RCPs experiments

NASA Astrophysics Data System (ADS)

Singh, Vishal; Goyal, Manish Kumar

2016-01-01

This paper draws attention to highlight the spatial and temporal variability in precipitation lapse rate (PLR) and precipitation extreme indices (PEIs) through the mesoscale characterization of Teesta river catchment, which corresponds to north Sikkim eastern Himalayas. A PLR rate is an important variable for the snowmelt runoff models. In a mountainous region, the PLR could be varied from lower elevation parts to high elevation parts. In this study, a PLR was computed by accounting elevation differences, which varies from around 1500 m to 7000 m. A precipitation variability and extremity were analysed using multiple mathematical functions viz. quantile regression, spatial mean, spatial standard deviation, Mann-Kendall test and Sen's estimation. For this reason, a daily precipitation, in the historical (years 1980-2005) as measured/observed gridded points and projected experiments for the 21st century (years 2006-2100) simulated by CMIP5 ESM-2 M model (Coupled Model Intercomparison Project Phase 5 Earth System Model 2) employing three different radiative forcing scenarios (Representative Concentration Pathways), utilized for the research work. The outcomes of this study suggest that a PLR is significantly varied from lower elevation to high elevation parts. The PEI based analysis showed that the extreme high intensity events have been increased significantly, especially after 2040s. The PEI based observations also showed that the numbers of wet days are increased for all the RCPs. The quantile regression plots showed significant increments in the upper and lower quantiles of the various extreme indices. The Mann-Kendall test and Sen's estimation tests clearly indicated significant changing patterns in the frequency and intensity of the precipitation indices across all the sub-basins and RCP scenario in an intra-decadal time series domain. The RCP8.5 showed extremity of the projected outcomes.

Prediction and Real-Time Compensation of Qubit Decoherence Via Machine Learning (Open Access, Publisher’s Version)

DTIC Science & Technology

2017-01-16

ARTICLE Received 24 Sep 2016 | Accepted 29 Nov 2016 | Published 16 Jan 2017 Prediction and real- time compensation of qubit decoherence via machine...information to suppress stochastic, semiclassical decoherence, even when access to measurements is limited. First, we implement a time -division...quantum information experiments. Second, we employ predictive feedback during sequential but time delayed measurements to reduce the Dick effect as

Escalating risk and the moderating effect of resistance to peer influence on the P200 and feedback-related negativity.

PubMed

Kiat, John; Straley, Elizabeth; Cheadle, Jacob E

2016-03-01

Young people frequently socialize together in contexts that encourage risky decision making, pointing to a need for research into how susceptibility to peer influence is related to individual differences in the neural processing of decisions during sequentially escalating risk. We applied a novel analytic approach to analyze EEG activity from college-going students while they completed the Balloon Analogue Risk Task (BART), a well-established risk-taking propensity assessment. By modeling outcome-processing-related changes in the P200 and feedback-related negativity (FRN) sequentially within each BART trial as a function of pump order as an index of increasing risk, our results suggest that analyzing the BART in a progressive fashion may provide valuable new insights into the temporal neurophysiological dynamics of risk taking. Our results showed that a P200, localized to the left caudate nucleus, and an FRN, localized to the left dACC, were positively correlated with the level of risk taking and reward. Furthermore, consistent with our hypotheses, the rate of change in the FRN was higher among college students with greater self-reported resistance to peer influence. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A method to preserve trends in quantile mapping bias correction of climate modeled temperature

NASA Astrophysics Data System (ADS)

Grillakis, Manolis G.; Koutroulis, Aristeidis G.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

2017-09-01

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Quantile Regression Models for Current Status Data

PubMed Central

Ou, Fang-Shu; Zeng, Donglin; Cai, Jianwen

2016-01-01

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging. PMID:27994307

Using the Quantile Mapping to improve a weather generator

NASA Astrophysics Data System (ADS)

Chen, Y.; Themessl, M.; Gobiet, A.

2012-04-01

We developed a weather generator (WG) by using statistical and stochastic methods, among them are quantile mapping (QM), Monte-Carlo, auto-regression, empirical orthogonal function (EOF). One of the important steps in the WG is using QM, through which all the variables, no matter what distribution they originally are, are transformed into normal distributed variables. Therefore, the WG can work on normally distributed variables, which greatly facilitates the treatment of random numbers in the WG. Monte-Carlo and auto-regression are used to generate the realization; EOFs are employed for preserving spatial relationships and the relationships between different meteorological variables. We have established a complete model named WGQM (weather generator and quantile mapping), which can be applied flexibly to generate daily or hourly time series. For example, with 30-year daily (hourly) data and 100-year monthly (daily) data as input, the 100-year daily (hourly) data would be relatively reasonably produced. Some evaluation experiments with WGQM have been carried out in the area of Austria and the evaluation results will be presented.

Mobility and orientation aid for blind persons using artificial vision

NASA Astrophysics Data System (ADS)

Costa, Gustavo; Gusberti, Adrián; Graffigna, Juan Pablo; Guzzo, Martín; Nasisi, Oscar

2007-11-01

Blind or vision-impaired persons are limited in their normal life activities. Mobility and orientation of blind persons is an ever-present research subject because no total solution has yet been reached for these activities that pose certain risks for the affected persons. The current work presents the design and development of a device conceived on capturing environment information through stereoscopic vision. The images captured by a couple of video cameras are transferred and processed by configurable and sequential FPGA and DSP devices that issue action signals to a tactile feedback system. Optimal processing algorithms are implemented to perform this feedback in real time. The components selected permit portability; that is, to readily get used to wearing the device.

Sequential biases on subjective judgments: Evidence from face attractiveness and ringtone agreeableness judgment.

PubMed

Huang, Jianrui; He, Xianyou; Ma, Xiaojin; Ren, Yian; Zhao, Tingting; Zeng, Xin; Li, Han; Chen, Yiheng

2018-01-01

When people make decisions about sequentially presented items in psychophysical experiments, their decisions are always biased by their preceding decisions and the preceding items, either by assimilation (shift towards the decision or item) or contrast (shift away from the decision or item). Such sequential biases also occur in naturalistic and real-world judgments such as facial attractiveness judgments. In this article, we aimed to cast light on the causes of these sequential biases. We first found significant assimilative and contrastive effects in a visual face attractiveness judgment task and an auditory ringtone agreeableness judgment task, indicating that sequential effects are not limited to the visual modality. We then found that the provision of trial-by-trial feedback of the preceding stimulus value eliminated the contrastive effect, but only weakened the assimilative effect. When participants orally reported their judgments rather than indicated them via a keyboard button press, we found a significant diminished assimilative effect, suggesting that motor response repetition strengthened the assimilation bias. Finally, we found that when visual and auditory stimuli were alternated, there was no longer a contrastive effect from the immediately previous trial, but there was an assimilative effect both from the previous trial (cross-modal) and the 2-back trial (same stimulus modality). These findings suggested that the contrastive effect results from perceptual processing, while the assimilative effect results from anchoring of the previous judgment and is strengthened by response repetition and numerical priming.

Air pollution and daily mortality in Erfurt, east Germany, 1980-1989.

PubMed Central

Spix, C; Heinrich, J; Dockery, D; Schwartz, J; Völksch, G; Schwinkowski, K; Cöllen, C; Wichmann, H E

1993-01-01

In Erfurt, Germany, unfavorable geography and emissions from coal burning lead to very high ambient pollution (up to about 4000 micrograms/m3 SO2 in 1980-89). To assess possible health effects of these exposures, total daily mortality was obtained for this same period. A multivariate model was fitted, including corrections for long-term fluctuations, influenza epidemics, and meterology, before analyzing the effect of pollution. The best fit for pollution was obtained for log (SO2 daily mean) with a lag of 2 days. Daily mortality increased by 10% for an increase in SO2 from 23 to 929 micrograms/m3 (5% quantile to 95% quantile). A harvesting effect (fewer people die on a given day if more deaths occurred in the last 15 days) may modify this by +/- 2%. The effect for particulates (SP, 1988-89 only) was stronger than the effect of SO2. Log SP (daily mean) increasing from 15 micrograms/m3 to 331 micrograms/m3 (5% quantile to 95% quantile) was associated with a 22% increase in mortality. Depending on harvesting, the observable effect may lie between 14% and 27%. There is no indication of a threshold or synergism. The effects of air pollution are smaller than the effects of influenza epidemics and are of the same size as meterologic effects. The results for the lower end of the dose range are in agreement with linear models fitted in studies of moderate air pollution and episode studies. Images Figure 1. Figure 2. PMID:8137781

Air pollution and daily mortality in Erfurt, east Germany, 1980-1989.

PubMed

Spix, C; Heinrich, J; Dockery, D; Schwartz, J; Völksch, G; Schwinkowski, K; Cöllen, C; Wichmann, H E

1993-11-01

In Erfurt, Germany, unfavorable geography and emissions from coal burning lead to very high ambient pollution (up to about 4000 micrograms/m3 SO2 in 1980-89). To assess possible health effects of these exposures, total daily mortality was obtained for this same period. A multivariate model was fitted, including corrections for long-term fluctuations, influenza epidemics, and meterology, before analyzing the effect of pollution. The best fit for pollution was obtained for log (SO2 daily mean) with a lag of 2 days. Daily mortality increased by 10% for an increase in SO2 from 23 to 929 micrograms/m3 (5% quantile to 95% quantile). A harvesting effect (fewer people die on a given day if more deaths occurred in the last 15 days) may modify this by +/- 2%. The effect for particulates (SP, 1988-89 only) was stronger than the effect of SO2. Log SP (daily mean) increasing from 15 micrograms/m3 to 331 micrograms/m3 (5% quantile to 95% quantile) was associated with a 22% increase in mortality. Depending on harvesting, the observable effect may lie between 14% and 27%. There is no indication of a threshold or synergism. The effects of air pollution are smaller than the effects of influenza epidemics and are of the same size as meterologic effects. The results for the lower end of the dose range are in agreement with linear models fitted in studies of moderate air pollution and episode studies.

Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model

NASA Astrophysics Data System (ADS)

Li, Fawen; Wei, Wan; Zhao, Yong; Qiao, Jiale

2017-01-01

There is a close relationship between groundwater level in a shallow aquifer and the surface ecological environment; hence, it is important to accurately simulate and predict the groundwater level in eco-environmental construction projects. The multiple linear regression (MLR) model is one of the most useful methods to predict groundwater level (depth); however, the predicted values by this model only reflect the mean distribution of the observations and cannot effectively fit the extreme distribution data (outliers). The study reported here builds a prediction model of groundwater-depth dynamics in a shallow aquifer using the quantile regression (QR) method on the basis of the observed data of groundwater depth and related factors. The proposed approach was applied to five sites in Tianjin city, north China, and the groundwater depth was calculated in different quantiles, from which the optimal quantile was screened out according to the box plot method and compared to the values predicted by the MLR model. The results showed that the related factors in the five sites did not follow the standard normal distribution and that there were outliers in the precipitation and last-month (initial state) groundwater-depth factors because the basic assumptions of the MLR model could not be achieved, thereby causing errors. Nevertheless, these conditions had no effect on the QR model, as it could more effectively describe the distribution of original data and had a higher precision in fitting the outliers.

Automatic Feature Selection and Weighting for the Formation of Homogeneous Groups for Regional Intensity-Duration-Frequency (IDF) Curve Estimation

NASA Astrophysics Data System (ADS)

Yang, Z.; Burn, D. H.

2017-12-01

Extreme rainfall events can have devastating impacts on society. To quantify the associated risk, the IDF curve has been used to provide the essential rainfall-related information for urban planning. However, the recent changes in the rainfall climatology caused by climate change and urbanization have made the estimates provided by the traditional regional IDF approach increasingly inaccurate. This inaccuracy is mainly caused by two problems: 1) The ineffective choice of similarity indicators for the formation of a homogeneous group at different regions; and 2) An inadequate number of stations in the pooling group that does not adequately reflect the optimal balance between group size and group homogeneity or achieve the lowest uncertainty in the rainfall quantiles estimates. For the first issue, to consider the temporal difference among different meteorological and topographic indicators, a three-layer design is proposed based on three stages in the extreme rainfall formation: cloud formation, rainfall generation and change of rainfall intensity above urban surface. During the process, the impacts from climate change and urbanization are considered through the inclusion of potential relevant features at each layer. Then to consider spatial difference of similarity indicators for the homogeneous group formation at various regions, an automatic feature selection and weighting algorithm, specifically the hybrid searching algorithm of Tabu search, Lagrange Multiplier and Fuzzy C-means Clustering, is used to select the optimal combination of features for the potential optimal homogenous groups formation at a specific region. For the second issue, to compare the uncertainty of rainfall quantile estimates among potential groups, the two sample Kolmogorov-Smirnov test-based sample ranking process is used. During the process, linear programming is used to rank these groups based on the confidence intervals of the quantile estimates. The proposed methodology fills the gap of including the urbanization impacts during the pooling group formation, and challenges the traditional assumption that the same set of similarity indicators can be equally effective in generating the optimal homogeneous group for regions with different geographic and meteorological characteristics.

Project Lifespan-based Nonstationary Hydrologic Design Methods for Changing Environment

NASA Astrophysics Data System (ADS)

Xiong, L.

2017-12-01

Under changing environment, we must associate design floods with the design life period of projects to ensure the hydrologic design is really relevant to the operation of the hydrologic projects, because the design value for a given exceedance probability over the project life period would be significantly different from that over other time periods of the same length due to the nonstationarity of probability distributions. Several hydrologic design methods that take the design life period of projects into account have been proposed in recent years, i.e. the expected number of exceedances (ENE), design life level (DLL), equivalent reliability (ER), and average design life level (ADLL). Among the four methods to be compared, both the ENE and ER methods are return period-based methods, while DLL and ADLL are risk/reliability- based methods which estimate design values for given probability values of risk or reliability. However, the four methods can be unified together under a general framework through a relationship transforming the so-called representative reliability (RRE) into the return period, i.e. m=1/1(1-RRE), in which we compute the return period m using the representative reliability RRE.The results of nonstationary design quantiles and associated confidence intervals calculated by ENE, ER and ADLL were very similar, since ENE or ER was a special case or had a similar expression form with respect to ADLL. In particular, the design quantiles calculated by ENE and ADLL were the same when return period was equal to the length of the design life. In addition, DLL can yield similar design values if the relationship between DLL and ER/ADLL return periods is considered. Furthermore, ENE, ER and ADLL had good adaptability to either an increasing or decreasing situation, yielding not too large or too small design quantiles. This is important for applications of nonstationary hydrologic design methods in actual practice because of the concern of choosing the emerging nonstationary methods versus the traditional stationary methods. There is still a long way to go for the conceptual transition from stationarity to nonstationarity in hydrologic design.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

Systematic and Iterative Development of a Smartphone App to Promote Sun-Protection Among Holidaymakers: Design of a Prototype and Results of Usability and Acceptability Testing

PubMed Central

Sniehotta, Falko F; Birch-Machin, Mark A; Olivier, Patrick; Araújo-Soares, Vera

2017-01-01

Background Sunburn and intermittent exposure to ultraviolet rays are risk factors for melanoma. Sunburn is a common experience during holidays, making tourism settings of particular interest for skin cancer prevention. Holidaymakers are a volatile populations found at different locations, which may make them difficult to reach. Given the widespread use of smartphones, evidence suggests that this might be a novel, convenient, scalable, and feasible way of reaching the target population. Objective The main objective of this study was to describe and appraise the process of systematically developing a smartphone intervention (mISkin app) to promote sun-protection during holidays. Methods The iterative development process of the mISkin app was conducted over four sequential stages: (1) identify evidence on the most effective behavior change techniques (BCTs) used (active ingredients) as well as theoretical predictors and theories, (2) evidence-based intervention design, (3) co-design with users of the mISkin app prototype, and (4) refinement of the app. Each stage provided key findings that were subsequently used to inform the design of the mISkin app. Results The sequential approach to development integrates different strands of evidence to inform the design of an evidence-based intervention. A systematic review on previously tested interventions to promote sun-protection provided cues and constraints for the design of this intervention. The development and design of the mISkin app also incorporated other sources of information, such as other literature reviews and experts’ consultations. The developed prototype of the mISkin app was evaluated by engaging potential holidaymakers in the refinement and further development of the mISkin app through usability (ease-of-use) and acceptability testing of the intervention prototype. All 17 participants were satisfied with the mISkin prototype and expressed willingness to use it. Feedback on the app was integrated in the optimization process of the mISkin app. Conclusions The mISkin app was designed to promote sun-protection among holidaymakers and was based on current evidence, experts’ knowledge and experience, and user involvement. Based on user feedback, the app has been refined and a fully functional version is ready for formal testing in a feasibility pilot study. PMID:28606892

On deciphering the book of nature: human communication in psychotherapy.

PubMed

Goodheart, W B

1992-10-01

The tools of contemporary applied mathematics reveal important hidden regularities amidst the ongoing interactive feedback phenomena occurring in interactional or dynamical systems in nature where everything affects everything else. Badalamenti and Langs investigate each therapy session as a continuous sequential emergence of interrelated communicative events (or communicative states) which meet the criteria of a dynamical system. Applying mathematical modeling the authors demonstrate how otherwise hidden regularities occurring between patients and therapists become accessible to us that are unavailable to our unaided powers of observation, intuition, and thought. This is a systems or population investigation of clinical interaction that begins in a qualitative or domain mode, but which opens immediately toward statistical and formal modes of discussion. It can lead to statements of properties and laws that meet the criteria of scientific dialogue and validity. It provides the clinician with guidelines for making interpretations and for assessing their immediate subsequent effect. It is distinguished from the essentialist approach at the foundation of traditional clinical thought which provides no access to such feedback phenomena and their properties. Communicative Psychoanalysts have adopted the systems perspective and are evolving a clinical language and treatment based upon its principles and discoveries.

Web-based unfolding cases: a strategy to enhance and evaluate clinical reasoning skills.

PubMed

Johnson, Gail; Flagler, Susan

2013-10-01

Clinical reasoning involves the use of both analytical and nonanalytical intuitive cognitive processes. Fostering student development of clinical reasoning skills and evaluating student performance in this cognitive arena can challenge educators. The use of Web-based unfolding cases is proposed as a strategy to address these challenges. Unfolding cases mimic real-life clinical situations by presenting only partial clinical information in sequential segments. Students receive immediate feedback after submitting a response to a given segment. The student's comparison of the desired and submitted responses provides information to enhance the development of clinical reasoning skills. Each student's set of case responses are saved for the instructor in an individual-student electronic file, providing a record of the student's knowledge and thinking processes for faculty evaluation. For the example case given, the approaches used to evaluate individual components of clinical reasoning are provided. Possible future uses of Web-based unfolding cases are described. Copyright 2013, SLACK Incorporated.

Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.

PubMed

Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M

2014-01-01

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

F-16 Training System Media Report

DTIC Science & Technology

1981-03-01

practice items. 4.1.3 Use/Procedure This strategy requires the learner to apply a set of sequential steps designed to accomplish a specific task which needs...information. 6. Feedback: Provides the student with the correct answers for the practice items. 4.1.5 Use/Rule This strategy requires the learner to...provide the background and rationale for selecting and/or modifying instructional media to best meet the needs of the F-16 training program. The

Approximations for Quantitative Feedback Theory Designs

NASA Technical Reports Server (NTRS)

Henderson, D. K.; Hess, R. A.

1997-01-01

The computational requirements for obtaining the results summarized in the preceding section were very modest and were easily accomplished using computer-aided control system design software. Of special significance is the ability of the PDT to indicate a loop closure sequence for MIMO QFT designs that employ sequential loop closure. Although discussed as part of a 2 x 2 design, the PDT is obviously applicable to designs with a greater number of inputs and system responses.

Optimal Sequential Rules for Computer-Based Instruction.

ERIC Educational Resources Information Center

Vos, Hans J.

1998-01-01

Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

The effect of a sequential structure of practice for the training of perceptual-cognitive skills in tennis

PubMed Central

2017-01-01

Objective Anticipation of opponent actions, through the use of advanced (i.e., pre-event) kinematic information, can be trained using video-based temporal occlusion. Typically, this involves isolated opponent skills/shots presented as trials in a random order. However, two different areas of research concerning representative task design and contextual (non-kinematic) information, suggest this structure of practice restricts expert performance. The aim of this study was to examine the effect of a sequential structure of practice during video-based training of anticipatory behavior in tennis, as well as the transfer of these skills to the performance environment. Methods In a pre-practice-retention-transfer design, participants viewed life-sized video of tennis rallies across practice in either a sequential order (sequential group), in which participants were exposed to opponent skills/shots in the order they occur in the sport, or a non-sequential (non-sequential group) random order. Results In the video-based retention test, the sequential group was significantly more accurate in their anticipatory judgments when the retention condition replicated the sequential structure compared to the non-sequential group. In the non-sequential retention condition, the non-sequential group was more accurate than the sequential group. In the field-based transfer test, overall decision time was significantly faster in the sequential group compared to the non-sequential group. Conclusion Findings highlight the benefits of a sequential structure of practice for the transfer of anticipatory behavior in tennis. We discuss the role of contextual information, and the importance of representative task design, for the testing and training of perceptual-cognitive skills in sport. PMID:28355263

Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2015-01-01

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory. PMID:25910189

Differences in nutrient and energy contents of commonly consumed dishes prepared in restaurants v. at home in Hunan Province, China.

PubMed

Jia, Xiaofang; Liu, Jiawu; Chen, Bo; Jin, Donghui; Fu, Zhongxi; Liu, Huilin; Du, Shufa; Popkin, Barry M; Mendez, Michelle A

2018-05-01

Eating away from home is associated with poor diet quality, in part due to less healthy food choices and larger portions. However, few studies account for the potential additional contribution of differences in food composition between restaurant- and home-prepared dishes. The present study aimed to investigate differences in nutrients of dishes prepared in restaurants v. at home. Eight commonly consumed dishes were collected in twenty of each of the following types of locations: small and large restaurants, and urban and rural households. In addition, two fast-food items were collected from ten KFC, McDonald's and food stalls. Five samples per dish were randomly pooled from every location. Nutrients were analysed and energy was calculated in composite samples. Differences in nutrients of dishes by preparation location were determined. Hunan Province, China. Na, K, protein, total fat, fatty acids, carbohydrate and energy in dishes. On average, both the absolute and relative fat contents, SFA and Na:K ratio were higher in dishes prepared in restaurants than households (P < 0·05). Protein was 15 % higher in animal food-based dishes prepared in households than restaurants (P<0·05). Quantile regression models found that, at the 90th quantile, restaurant preparation was consistently negatively associated with protein and positively associated with the percentage of energy from fat in all dishes. Moreover, restaurant preparation also positively influenced the SFA content in dishes, except at the highest quantiles. These findings suggest that compared with home preparation, dishes prepared in restaurants in China may differ in concentrations of total fat, SFA, protein and Na:K ratio, which may further contribute, beyond food choices, to less healthy nutrient intakes linked to eating away from home.

Differences in nutrient and energy content of commonly-consumed dishes prepared in restaurants vs. at home in Hunan province, China

PubMed Central

Jia, Xiaofang; Liu, Jiawu; Chen, Bo; Jin, Donghui; Fu, Zhongxi; Liu, Huilin; Du, Shufa; Popkin, Barry M.; Mendez, Michelle A.

2017-01-01

Objective Eating away from home is associated with poor diet quality, in part due to less healthy food choices and larger portions. However, few studies take into account the potential additional contribution of differences in food composition between restaurant- and home-prepared dishes. This study aimed to investigate differences in nutrients of dishes prepared in restaurants vs. at home. Design Eight commonly consumed dishes were collected in 20 of each of the following types of locations: small and large restaurants, and urban and rural households. In addition, two fast-food items were collected from 10 KFC’s, McDonald’s, and food stalls. Five samples per dish were randomly pooled from every location. Nutrients were analyzed and energy was calculated in composite samples. Differences in nutrients of dishes by preparation location were determined. Setting Urban and rural. Subjects Sodium, potassium, protein, total fat, fatty acids, carbohydrate, and energy in dishes. Results On average, both the absolute and relative fat content, saturated fatty acid (SFA) and sodium/potassium ratio were higher in dishes prepared in restaurants than households (P<0.05). Protein was 15% higher in animal food-based dishes prepared in households than restaurants (P <0.05). Quantile regression models found that, at the 90th quantile, restaurant preparation was consistently negatively associated with protein and positively associated with the percentage energy from fat in all dishes. Moreover, restaurant preparation also positively influenced the SFA content in dishes, except at the highest quantiles. Conclusions These findings suggest that compared to home preparation, dishes prepared in restaurants in China may differ in concentrations of total fat, SFA, protein, and sodium/potassium ratio, which may further contribute, beyond food choices, to less healthy nutrient intake linked to eating away from home. PMID:29306339

Subjective wellbeing, suicide and socioeconomic factors: an ecological analysis in Hong Kong.

PubMed

Hsu, C-Y; Chang, S-S; Yip, P S F

2018-04-10

There has recently been an increased interest in mental health indicators for the monitoring of population wellbeing, which is among the targets of Sustainable Development Goals adopted by the United Nations. Levels of subjective wellbeing and suicide rates have been proposed as indicators of population mental health, but prior research is limited. Data on individual happiness and life satisfaction were sourced from a population-based survey in Hong Kong (2011). Suicide data were extracted from Coroner's Court files (2005-2013). Area characteristic variables included local poverty rate and four factors derived from a factor analysis of 21 variables extracted from the 2011 census. The associations between mean happiness and life satisfaction scores and suicide rates were assessed using Pearson correlation coefficient at two area levels: 18 districts and 30 quantiles of large street blocks (LSBs; n = 1620). LSB is a small area unit with a higher level of within-unit homogeneity compared with districts. Partial correlations were used to control for area characteristics. Happiness and life satisfaction demonstrated weak inverse associations with suicide rate at the district level (r = -0.32 and -0.36, respectively) but very strong associations at the LSB quantile level (r = -0.83 and -0.84, respectively). There were generally very weak or weak negative correlations across sex/age groups at the district level but generally moderate to strong correlations at the LSB quantile level. The associations were markedly attenuated or became null after controlling for area characteristics. Subjective wellbeing is strongly associated with suicide at a small area level; socioeconomic factors can largely explain this association. Socioeconomic factors could play an important role in determining the wellbeing of the population, and this could inform policies aimed at enhancing population wellbeing.

Environmental influence on mussel (Mytilus edulis) growth - A quantile regression approach

NASA Astrophysics Data System (ADS)

Bergström, Per; Lindegarth, Mats

2016-03-01

The need for methods for sustainable management and use of coastal ecosystems has increased in the last century. A key aspect for obtaining ecologically and economically sustainable aquaculture in threatened coastal areas is the requirement of geographic information of growth and potential production capacity. Growth varies over time and space and depends on a complex pattern of interactions between the bivalve and a diverse range of environmental factors (e.g. temperature, salinity, food availability). Understanding these processes and modelling the environmental control of bivalve growth has been central in aquaculture. In contrast to the most conventional modelling techniques, quantile regression can handle cases where not all factors are measured and provide the possibility to estimate the effect at different levels of the response distribution and give therefore a more complete picture of the relationship between environmental factors and biological response. Observation of the relationships between environmental factors and growth of the bivalve Mytilus edulis revealed relationships that varied both among level of growth rate and within the range of environmental variables along the Swedish west coast. The strongest patterns were found for water oxygen concentration level which had a negative effect on growth for all oxygen levels and growth levels. However, these patterns coincided with differences in growth among periods and very little of the remaining variability within periods could be explained indicating that interactive processes masked the importance of the individual variables. By using quantile regression and local regression (LOESS) this study was able to provide valuable information on environmental factors influencing the growth of M. edulis and important insight for the development of ecosystem based management tools of aquaculture activities, its use in mitigation efforts and successful management of human use of coastal areas.

Reciprocal relationships and potential feedbacks between biodiversity and disturbance.

PubMed

Hughes, A Randall; Byrnes, Jarrett E; Kimbro, David L; Stachowicz, John J

2007-09-01

Two major foci of ecological research involve reciprocal views of the relationship between biodiversity and disturbance: disturbance determines community diversity or diversity determines realized disturbance severity. Here, we present an initial attempt to synthesize these two approaches in order to understand whether feedbacks occur, and what their effects on patterns of diversity might be. Our review of published experiments shows that (i) disturbance severity can be both a cause and a consequence of local diversity in a wide range of ecosystems and (ii) shapes of the unidirectional relationships between diversity and disturbance can be quite variable. To explore how feedbacks between diversity and disturbance might operate to alter expected patterns of diversity in nature, we develop and then evaluate a conceptual model that decomposes the relationships into component parts, considering sequentially the effect of diversity on disturbance severity, and the effect of realized disturbance on diversity loss, subsequent recruitment, and competitive exclusion. Our model suggests that feedbacks can increase mean values of richness, decrease variability, and alter the patterns of correlation between diversity and disturbance in nature. We close by offering ideas for future research to help fill gaps in our understanding of reciprocal relationships among ecological variables like diversity and disturbance.

A high accuracy sequential solver for simulation and active control of a longitudinal combustion instability

NASA Technical Reports Server (NTRS)

Shyy, W.; Thakur, S.; Udaykumar, H. S.

1993-01-01

A high accuracy convection scheme using a sequential solution technique has been developed and applied to simulate the longitudinal combustion instability and its active control. The scheme has been devised in the spirit of the Total Variation Diminishing (TVD) concept with special source term treatment. Due to the substantial heat release effect, a clear delineation of the key elements employed by the scheme, i.e., the adjustable damping factor and the source term treatment has been made. By comparing with the first-order upwind scheme previously utilized, the present results exhibit less damping and are free from spurious oscillations, offering improved quantitative accuracy while confirming the spectral analysis reported earlier. A simple feedback type of active control has been found to be capable of enhancing or attenuating the magnitude of the combustion instability.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Bayesian estimation of extreme flood quantiles using a rainfall-runoff model and a stochastic daily rainfall generator

NASA Astrophysics Data System (ADS)

Costa, Veber; Fernandes, Wilson

2017-11-01

Extreme flood estimation has been a key research topic in hydrological sciences. Reliable estimates of such events are necessary as structures for flood conveyance are continuously evolving in size and complexity and, as a result, their failure-associated hazards become more and more pronounced. Due to this fact, several estimation techniques intended to improve flood frequency analysis and reducing uncertainty in extreme quantile estimation have been addressed in the literature in the last decades. In this paper, we develop a Bayesian framework for the indirect estimation of extreme flood quantiles from rainfall-runoff models. In the proposed approach, an ensemble of long daily rainfall series is simulated with a stochastic generator, which models extreme rainfall amounts with an upper-bounded distribution function, namely, the 4-parameter lognormal model. The rationale behind the generation model is that physical limits for rainfall amounts, and consequently for floods, exist and, by imposing an appropriate upper bound for the probabilistic model, more plausible estimates can be obtained for those rainfall quantiles with very low exceedance probabilities. Daily rainfall time series are converted into streamflows by routing each realization of the synthetic ensemble through a conceptual hydrologic model, the Rio Grande rainfall-runoff model. Calibration of parameters is performed through a nonlinear regression model, by means of the specification of a statistical model for the residuals that is able to accommodate autocorrelation, heteroscedasticity and nonnormality. By combining the outlined steps in a Bayesian structure of analysis, one is able to properly summarize the resulting uncertainty and estimating more accurate credible intervals for a set of flood quantiles of interest. The method for extreme flood indirect estimation was applied to the American river catchment, at the Folsom dam, in the state of California, USA. Results show that most floods, including exceptionally large non-systematic events, were reasonably estimated with the proposed approach. In addition, by accounting for uncertainties in each modeling step, one is able to obtain a better understanding of the influential factors in large flood formation dynamics.

Event-related potentials in response to cheating and cooperation in a social dilemma game.

PubMed

Bell, Raoul; Sasse, Julia; Möller, Malte; Czernochowski, Daniela; Mayr, Susanne; Buchner, Axel

2016-02-01

A sequential prisoner's dilemma game was combined with psychophysiological measures to examine the cognitive underpinnings of reciprocal exchange. Participants played four rounds of the game with partners who either cooperated or cheated. In a control condition, the partners' faces were shown, but no interaction took place. The partners' behaviors were consistent in the first three rounds of the game, but in the last round some of the partners unexpectedly changed strategies. In the first round of the game, the feedback about a partner's decision elicited a feedback P300, which was more pronounced for cooperation and cheating in comparison to the control condition, but did not vary as a function of feedback valence. In the last round, both the feedback negativity and the feedback P300 were sensitive to expectancy violations. There was no consistent evidence for a negativity bias, that is, enhanced allocation of attention to feedback about another person's cheating in comparison to feedback about another person's cooperation. Instead, participants focused on both positive and negative information, and flexibly adjusted their processing biases to the diagnosticity of the information. This conclusion was corroborated by the ERP correlates of memory retrieval. Successful retrieval of a partner's reputation was associated with an anterior positivity between 400 and 600 ms after face onset. This anterior positivity was more pronounced for both cooperator and cheater faces in comparison to control faces. The results suggest that it is not the negativity of social information, but rather its motivational and behavioral relevance that determines its processing. © 2015 Society for Psychophysiological Research.

Identifying the Safety Factors over Traffic Signs in State Roads using a Panel Quantile Regression Approach.

PubMed

Šarić, Željko; Xu, Xuecai; Duan, Li; Babić, Darko

2018-06-20

This study intended to investigate the interactions between accident rate and traffic signs in state roads located in Croatia, and accommodate the heterogeneity attributed to unobserved factors. The data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the heterogeneity, a panel quantile regression model was proposed, in which quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, while the panel data model accommodates the heterogeneity attributed to unobserved factors. Results revealed that (1) low visibility of material damage (MD) and death or injured (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3)average speed limit and the number of invalid traffic signs per km exhibited a high accident rate. To our knowledge, it's the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by involving the visibility, the present study demonstrates that the low visibility causes a relatively higher risk of MD and DI; It is noteworthy that average speed limit corresponds with accident rate positively; The number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; The number of invalid traffic signs per km are significant for accident rate, thus regular maintenance should be kept for a safer roadway environment.

Data quantile-quantile plots: quantifying the time evolution of space climatology

NASA Astrophysics Data System (ADS)

Tindale, Elizabeth; Chapman, Sandra

2017-04-01

The solar wind is inherently variable across a wide range of spatio-temporal scales; embedded in the flow are the signatures of distinct non-linear physical processes from evolving turbulence to the dynamical solar corona. In-situ satellite observations of solar wind magnetic field and velocity are at minute and below time resolution and now extend over several solar cycles. Each solar cycle is unique, and the space climatology challenge is to quantify how solar wind variability changes within, and across, each distinct solar cycle, and how this in turn drives space weather at earth. We will demonstrate a novel statistical method, that of data-data quantile-quantile (DQQ) plots, which quantifies how the underlying statistical distribution of a given observable is changing in time. Importantly this method does not require any assumptions concerning the underlying functional form of the distribution and can identify multi-component behaviour that is changing in time. This can be used to determine when a sub-range of a given observable is undergoing a change in statistical distribution, or where the moments of the distribution only are changing and the functional form of the underlying distribution is not changing in time. The method is quite general; for this application we use data from the WIND satellite to compare the solar wind across the minima and maxima of solar cycles 23 and 24 [1], and how these changes are manifest in parameters that quantify coupling to the earth's magnetosphere. [1] Tindale, E., and S.C. Chapman (2016), Geophys. Res. Lett., 43(11), doi: 10.1002/2016GL068920.

Wildfire Selectivity for Land Cover Type: Does Size Matter?

PubMed Central

Barros, Ana M. G.; Pereira, José M. C.

2014-01-01

Previous research has shown that fires burn certain land cover types disproportionally to their abundance. We used quantile regression to study land cover proneness to fire as a function of fire size, under the hypothesis that they are inversely related, for all land cover types. Using five years of fire perimeters, we estimated conditional quantile functions for lower (avoidance) and upper (preference) quantiles of fire selectivity for five land cover types - annual crops, evergreen oak woodlands, eucalypt forests, pine forests and shrublands. The slope of significant regression quantiles describes the rate of change in fire selectivity (avoidance or preference) as a function of fire size. We used Monte-Carlo methods to randomly permutate fires in order to obtain a distribution of fire selectivity due to chance. This distribution was used to test the null hypotheses that 1) mean fire selectivity does not differ from that obtained by randomly relocating observed fire perimeters; 2) that land cover proneness to fire does not vary with fire size. Our results show that land cover proneness to fire is higher for shrublands and pine forests than for annual crops and evergreen oak woodlands. As fire size increases, selectivity decreases for all land cover types tested. Moreover, the rate of change in selectivity with fire size is higher for preference than for avoidance. Comparison between observed and randomized data led us to reject both null hypotheses tested ( = 0.05) and to conclude it is very unlikely the observed values of fire selectivity and change in selectivity with fire size are due to chance. PMID:24454747

More heads choose better than one: Group decision making can eliminate probability matching.

PubMed

Schulze, Christin; Newell, Ben R

2016-06-01

Probability matching is a robust and common failure to adhere to normative predictions in sequential decision making. We show that this choice anomaly is nearly eradicated by gathering individual decision makers into small groups and asking the groups to decide. The group choice advantage emerged both when participants generated responses for an entire sequence of choices without outcome feedback (Exp. 1a) and when participants made trial-by-trial predictions with outcome feedback after each decision (Exp. 1b). We show that the dramatic improvement observed in group settings stands in stark contrast to a complete lack of effective solitary deliberation. These findings suggest a crucial role of group discussion in alleviating the impact of hasty intuitive responses in tasks better suited to careful deliberation.

Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice.

PubMed

Trivedi, Madhukar H; Daly, Ella J

2007-05-01

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the "next best" treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses.

Measurement-Based Care for Refractory Depression: A Clinical Decision Support Model for Clinical Research and Practice

PubMed Central

Trivedi, Madhukar H.; Daly, Ella J.

2009-01-01

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the “next best” treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses. PMID:17320312

Removing technical variability in RNA-seq data using conditional quantile normalization.

PubMed

Hansen, Kasper D; Irizarry, Rafael A; Wu, Zhijin

2012-04-01

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.

Robust neural network with applications to credit portfolio data analysis.

PubMed

Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

2010-01-01

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

Valence and magnitude ambiguity in feedback processing.

PubMed

Gu, Ruolei; Feng, Xue; Broster, Lucas S; Yuan, Lu; Xu, Pengfei; Luo, Yue-Jia

2017-05-01

Outcome feedback which indicates behavioral consequences are crucial for reinforcement learning and environmental adaptation. Nevertheless, outcome information in daily life is often totally or partially ambiguous. Studying how people interpret this kind of information would provide important knowledge about the human evaluative system. This study concentrates on the neural processing of partially ambiguous feedback, that is, either its valence or magnitude is unknown to participants. To address this topic, we sequentially presented valence and magnitude information; electroencephalography (EEG) response to each kind of presentation was recorded and analyzed. The event-related potential components feedback-related negativity (FRN) and P3 were used as indices of neural activity. Consistent with previous literature, the FRN elicited by ambiguous valence was not significantly different from that elicited by negative valence. On the other hand, the FRN elicited by ambiguous magnitude was larger than both the large and small magnitude, indicating the motivation to seek unambiguous magnitude information. The P3 elicited by ambiguous valence and ambiguous magnitude was not significantly different from that elicited by negative valence and small magnitude, respectively, indicating the emotional significance of feedback ambiguity. Finally, the aforementioned effects also manifested in the stage of information integration. These findings indicate both similarities and discrepancies between the processing of valence ambiguity and that of magnitude ambiguity, which may help understand the mechanisms of ambiguous information processing.

Single sensor for multiple analytes in different optical channel: Applying for multi-ion response modulation

NASA Astrophysics Data System (ADS)

Liang, Chunshuang; Jiang, Shimei

2017-08-01

A Schiff-base, (2,4-di-tert-butyl-6-((2-hydroxyphenyl-imino)-methyl)phenol) (L), has been improved to function as a simultaneous multi-ion probe in different optical channel. The probe changes from colorless to orangish upon being deprotonated by F-, while the presence of Al3+ significantly enhances the fluorescence of the probe due to the inhibition of Cdbnd N isomerization, cation-induced inhibition of excited-state intramolecular proton transfer (ESIPT), and chelation enhanced fluorescence (CHEF). Dual-channel "off-on" switching behavior resulted from the sequential input of F- and Al3+, reflecting the balance of independent reactions of Al3+ and F- with L and with one another. This sensing phenomenon realizes transformation between multiple states and beautifully mimics a "Write-Read-Erase-Read" logic circuit with two feedback loops.

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

PubMed

Astola, Laura; Molenaar, Jaap

2014-07-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

Prenatal Lead Exposure and Fetal Growth: Smaller Infants Have Heightened Susceptibility

PubMed Central

Rodosthenous, Rodosthenis S.; Burris, Heather H.; Svensson, Katherine; Amarasiriwardena, Chitra J.; Cantoral, Alejandra; Schnaas, Lourdes; Mercado-García, Adriana; Coull, Brent A.; Wright, Robert O.; Téllez-Rojo, Martha M.; Baccarelli, Andrea A.

2016-01-01

Background As population lead levels decrease, the toxic effects of lead may be distributed to more sensitive populations, such as infants with poor fetal growth. Objectives To determine the association of prenatal lead exposure and fetal growth; and to evaluate whether infants with poor fetal growth are more susceptible to lead toxicity than those with normal fetal growth. Methods We examined the association of second trimester maternal blood lead levels (BLL) with birthweight-for-gestational age (BWGA) z-score in 944 mother-infant participants of the PROGRESS cohort. We determined the association between maternal BLL and BWGA z-score by using both linear and quantile regression. We estimated odds ratios for small-for-gestational age (SGA) infants between maternal BLL quartiles using logistic regression. Maternal age, body mass index, socioeconomic status, parity, household smoking exposure, hemoglobin levels, and infant sex were included as confounders. Results While linear regression showed a negative association between maternal BLL and BWGA z-score (β=−0.06 z-score units per log2 BLL increase; 95% CI: −0.13, 0.003; P=0.06), quantile regression revealed larger magnitudes of this association in the <30th percentiles of BWGA z-score (β range [−0.08, −0.13] z-score units per log2 BLL increase; all P values <0.05). Mothers in the highest BLL quartile had an odds ratio of 1.62 (95% CI: 0.99–2.65) for having a SGA infant compared to the lowest BLL quartile. Conclusions While both linear and quantile regression showed a negative association between prenatal lead exposure and birthweight, quantile regression revealed that smaller infants may represent a more susceptible subpopulation. PMID:27923585

Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

NASA Astrophysics Data System (ADS)

Verkade, J. S.; Brown, J. D.; Reggiani, P.; Weerts, A. H.

2013-09-01

The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean, spread and forecast probabilities, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and to investigate whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of bivariate normality and logistic regression. Both the raw and post-processed ensembles are run through a hydrologic model of the river Rhine to create streamflow ensembles. The results are compared using multiple verification metrics and skill scores: relative mean error, Brier skill score and its decompositions, mean continuous ranked probability skill score and its decomposition, and the ROC score. Verification of the streamflow ensembles is performed at multiple spatial scales: relatively small headwater basins, large tributaries and the Rhine outlet at Lobith. The streamflow ensembles are verified against simulated streamflow, in order to isolate the effects of biases in the forcing ensembles and any improvements therein. The results indicate that the forcing ensembles contain significant biases, and that these cascade to the streamflow ensembles. Some of the bias in the forcing ensembles is unconditional in nature; this was resolved by a simple quantile-to-quantile transform. Improvements in conditional bias and skill of the forcing ensembles vary with forecast lead time, amount, and spatial scale, but are generally moderate. The translation to streamflow forecast skill is further muted, and several explanations are considered, including limitations in the modelling of the space-time covariability of the forcing ensembles and the presence of storages.

Can quantile mapping improve precipitation extremes from regional climate models?

NASA Astrophysics Data System (ADS)

Tani, Satyanarayana; Gobiet, Andreas

2015-04-01

The ability of quantile mapping to accurately bias correct regard to precipitation extremes is investigated in this study. We developed new methods by extending standard quantile mapping (QMα) to improve the quality of bias corrected extreme precipitation events as simulated by regional climate model (RCM) output. The new QM version (QMβ) was developed by combining parametric and nonparametric bias correction methods. The new nonparametric method is tested with and without a controlling shape parameter (Qmβ1 and Qmβ0, respectively). Bias corrections are applied on hindcast simulations for a small ensemble of RCMs at six different locations over Europe. We examined the quality of the extremes through split sample and cross validation approaches of these three bias correction methods. This split-sample approach mimics the application to future climate scenarios. A cross validation framework with particular focus on new extremes was developed. Error characteristics, q-q plots and Mean Absolute Error (MAEx) skill scores are used for evaluation. We demonstrate the unstable behaviour of correction function at higher quantiles with QMα, whereas the correction functions with for QMβ0 and QMβ1 are smoother, with QMβ1 providing the most reasonable correction values. The result from q-q plots demonstrates that, all bias correction methods are capable of producing new extremes but QMβ1 reproduces new extremes with low biases in all seasons compared to QMα, QMβ0. Our results clearly demonstrate the inherent limitations of empirical bias correction methods employed for extremes, particularly new extremes, and our findings reveals that the new bias correction method (Qmß1) produces more reliable climate scenarios for new extremes. These findings present a methodology that can better capture future extreme precipitation events, which is necessary to improve regional climate change impact studies.

Diagnostic Imaging Services in Magnet and Non-Magnet Hospitals: Trends in Utilization and Costs.

PubMed

Jayawardhana, Jayani; Welton, John M

2015-12-01

The purpose of this study was to better understand trends in utilization and costs of diagnostic imaging services at Magnet hospitals (MHs) and non-Magnet hospitals (NMHs). A data set was created by merging hospital-level data from the American Hospital Association's annual survey and Medicare cost reports, individual-level inpatient data from the Healthcare Cost and Utilization Project, and Magnet recognition status data from the American Nurses Credentialing Center. A descriptive analysis was conducted to evaluate the trends in utilization and costs of CT, MRI, and ultrasound procedures among MHs and NMHs in urban locations between 2000 and 2006 from the following ten states: Arizona, California, Colorado, Florida, Iowa, Maryland, North Carolina, New Jersey, New York, and Washington. When matched by bed size, severity of illness (case mix index), and clinical technological sophistication (Saidin index) quantiles, MHs in higher quantiles indicated higher rates of utilization of imaging services for MRI, CT, and ultrasound in comparison with NMHs in the same quantiles. However, average costs of MRI, CT, and ultrasounds were lower at MHs in comparison with NMHs in the same quantiles. Overall, MHs that are larger in size (number of beds), serve more severely ill patients (case mix index), and are more technologically sophisticated (Saidin index) show higher utilization of diagnostic imaging services, although costs per procedure at MHs are lower in comparison with similar NMHs, indicating possible cost efficiency at MHs. Further research is necessary to understand the relationship between the utilization of diagnostic imaging services among MHs and its impact on patient outcomes. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Effect of psychosocial stressors on patients with Crohn's disease: threatening life experiences and family relations.

PubMed

Slonim-Nevo, Vered; Sarid, Orly; Friger, Michael; Schwartz, Doron; Chernin, Elena; Shahar, Ilana; Sergienko, Ruslan; Vardi, Hillel; Rosenthal, Alexander; Mushkalo, Alexander; Dizengof, Vitaly; Ben-Yakov, Gil; Abu-Freha, Naim; Munteanu, Daniella; Gaspar, Nava; Eidelman, Leslie; Segal, Arik; Fich, Alexander; Greenberg, Dan; Odes, Shmuel

2016-09-01

Threatening life experiences and adverse family relations are major psychosocial stressors affecting mental and physical health in chronic illnesses, but their influence in Crohn's disease (CD) is unclear. We assessed whether these stressors would predict the psychological and medical condition of CD patients. Consecutive adult CD patients completed a series of instruments including demography, Patient Harvey-Bradshaw Index (P-HBI), Short Inflammatory Bowel Disease Questionnaire (SIBDQ), short-form survey instrument (SF-36), brief symptom inventory (BSI), family assessment device (FAD), and list of threatening life experiences (LTE). Associations of FAD and LTE with P-HBI, SIBDQ, SF-36, and BSI were examined by multiple linear and quantile regression analyses. The cohort included 391 patients, mean age 38.38±13.95 years, 59.6% women, with intermediate economic status. The median scores were as follows: P-HBI 4 (2-8), FAD 1.67 (1.3-2.1), LTE 1 (0-3), SF-36 physical health 43.75 (33.7-51.0), SF-36 mental health 42.99 (34.1-51.9), and BSI-Global Severity Index 0.81 (0.4-1.4). The SIBDQ was 47.27±13.9. LTE was associated with increased P-HBI in all quantiles and FAD in the 50% quantile. FAD and LTE were associated with reduced SIBDQ (P<0.001). Higher LTE was associated with lower SF-36 physical and mental health (P<0.001); FAD was associated with reduced mental health (P<0.001). FAD and LTE were associated positively with GSI in all quantiles; age was associated negatively. CD patients with more threatening life experiences and adverse family relations were less healthy both physically and mentally. Physicians offering patients sociopsychological therapy should relate to threatening life experiences and family relations.

Use of Quantile Regression to Determine the Impact on Total Health Care Costs of Surgical Site Infections Following Common Ambulatory Procedures.

PubMed

Olsen, Margaret A; Tian, Fang; Wallace, Anna E; Nickel, Katelin B; Warren, David K; Fraser, Victoria J; Selvam, Nandini; Hamilton, Barton H

2017-02-01

To determine the impact of surgical site infections (SSIs) on health care costs following common ambulatory surgical procedures throughout the cost distribution. Data on costs of SSIs following ambulatory surgery are sparse, particularly variation beyond just mean costs. We performed a retrospective cohort study of persons undergoing cholecystectomy, breast-conserving surgery, anterior cruciate ligament reconstruction, and hernia repair from December 31, 2004 to December 31, 2010 using commercial insurer claims data. SSIs within 90 days post-procedure were identified; infections during a hospitalization or requiring surgery were considered serious. We used quantile regression, controlling for patient, operative, and postoperative factors to examine the impact of SSIs on 180-day health care costs throughout the cost distribution. The incidence of serious and nonserious SSIs was 0.8% and 0.2%, respectively, after 21,062 anterior cruciate ligament reconstruction, 0.5% and 0.3% after 57,750 cholecystectomy, 0.6% and 0.5% after 60,681 hernia, and 0.8% and 0.8% after 42,489 breast-conserving surgery procedures. Serious SSIs were associated with significantly higher costs than nonserious SSIs for all 4 procedures throughout the cost distribution. The attributable cost of serious SSIs increased for both cholecystectomy and hernia repair as the quantile of total costs increased ($38,410 for cholecystectomy with serious SSI vs no SSI at the 70th percentile of costs, up to $89,371 at the 90th percentile). SSIs, particularly serious infections resulting in hospitalization or surgical treatment, were associated with significantly increased health care costs after 4 common surgical procedures. Quantile regression illustrated the differential effect of serious SSIs on health care costs at the upper end of the cost distribution.

Prenatal lead exposure and fetal growth: Smaller infants have heightened susceptibility.

PubMed

Rodosthenous, Rodosthenis S; Burris, Heather H; Svensson, Katherine; Amarasiriwardena, Chitra J; Cantoral, Alejandra; Schnaas, Lourdes; Mercado-García, Adriana; Coull, Brent A; Wright, Robert O; Téllez-Rojo, Martha M; Baccarelli, Andrea A

2017-02-01

As population lead levels decrease, the toxic effects of lead may be distributed to more sensitive populations, such as infants with poor fetal growth. To determine the association of prenatal lead exposure and fetal growth; and to evaluate whether infants with poor fetal growth are more susceptible to lead toxicity than those with normal fetal growth. We examined the association of second trimester maternal blood lead levels (BLL) with birthweight-for-gestational age (BWGA) z-score in 944 mother-infant participants of the PROGRESS cohort. We determined the association between maternal BLL and BWGA z-score by using both linear and quantile regression. We estimated odds ratios for small-for-gestational age (SGA) infants between maternal BLL quartiles using logistic regression. Maternal age, body mass index, socioeconomic status, parity, household smoking exposure, hemoglobin levels, and infant sex were included as confounders. While linear regression showed a negative association between maternal BLL and BWGA z-score (β=-0.06 z-score units per log 2 BLL increase; 95% CI: -0.13, 0.003; P=0.06), quantile regression revealed larger magnitudes of this association in the <30th percentiles of BWGA z-score (β range [-0.08, -0.13] z-score units per log 2 BLL increase; all P values<0.05). Mothers in the highest BLL quartile had an odds ratio of 1.62 (95% CI: 0.99-2.65) for having a SGA infant compared to the lowest BLL quartile. While both linear and quantile regression showed a negative association between prenatal lead exposure and birthweight, quantile regression revealed that smaller infants may represent a more susceptible subpopulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use of Quantile Regression to Determine the Impact on Total Health Care Costs of Surgical Site Infections Following Common Ambulatory Procedures

PubMed Central

Olsen, Margaret A.; Tian, Fang; Wallace, Anna E.; Nickel, Katelin B.; Warren, David K.; Fraser, Victoria J.; Selvam, Nandini; Hamilton, Barton H.

2017-01-01

Objective To determine the impact of surgical site infections (SSIs) on healthcare costs following common ambulatory surgical procedures throughout the cost distribution. Background Data on costs of SSIs following ambulatory surgery are sparse, particularly variation beyond just mean costs. Methods We performed a retrospective cohort study of persons undergoing cholecystectomy, breast-conserving surgery (BCS), anterior cruciate ligament reconstruction (ACL), and hernia repair from 12/31/2004–12/31/2010 using commercial insurer claims data. SSIs within 90 days post-procedure were identified; infections during a hospitalization or requiring surgery were considered serious. We used quantile regression, controlling for patient, operative, and postoperative factors to examine the impact of SSIs on 180-day healthcare costs throughout the cost distribution. Results The incidence of serious and non-serious SSIs were 0.8% and 0.2% after 21,062 ACL, 0.5% and 0.3% after 57,750 cholecystectomy, 0.6% and 0.5% after 60,681 hernia, and 0.8% and 0.8% after 42,489 BCS procedures. Serious SSIs were associated with significantly higher costs than non-serious SSIs for all 4 procedures throughout the cost distribution. The attributable cost of serious SSIs increased for both cholecystectomy and hernia repair as the quantile of total costs increased ($38,410 for cholecystectomy with serious SSI vs. no SSI at the 70th percentile of costs, up to $89,371 at the 90th percentile). Conclusions SSIs, particularly serious infections resulting in hospitalization or surgical treatment, were associated with significantly increased healthcare costs after 4 common surgical procedures. Quantile regression illustrated the differential effect of serious SSIs on healthcare costs at the upper end of the cost distribution. PMID:28059961

Estimating normative limits of Heidelberg Retina Tomograph optic disc rim area with quantile regression.

PubMed

Artes, Paul H; Crabb, David P

2010-01-01

To investigate why the specificity of the Moorfields Regression Analysis (MRA) of the Heidelberg Retina Tomograph (HRT) varies with disc size, and to derive accurate normative limits for neuroretinal rim area to address this problem. Two datasets from healthy subjects (Manchester, UK, n = 88; Halifax, Nova Scotia, Canada, n = 75) were used to investigate the physiological relationship between the optic disc and neuroretinal rim area. Normative limits for rim area were derived by quantile regression (QR) and compared with those of the MRA (derived by linear regression). Logistic regression analyses were performed to quantify the association between disc size and positive classifications with the MRA, as well as with the QR-derived normative limits. In both datasets, the specificity of the MRA depended on optic disc size. The odds of observing a borderline or outside-normal-limits classification increased by approximately 10% for each 0.1 mm(2) increase in disc area (P < 0.1). The lower specificity of the MRA with large optic discs could be explained by the failure of linear regression to model the extremes of the rim area distribution (observations far from the mean). In comparison, the normative limits predicted by QR were larger for smaller discs (less specific, more sensitive), and smaller for larger discs, such that false-positive rates became independent of optic disc size. Normative limits derived by quantile regression appear to remove the size-dependence of specificity with the MRA. Because quantile regression does not rely on the restrictive assumptions of standard linear regression, it may be a more appropriate method for establishing normative limits in other clinical applications where the underlying distributions are nonnormal or have nonconstant variance.

Methods for estimating selected low-flow statistics and development of annual flow-duration statistics for Ohio

USGS Publications Warehouse

Koltun, G.F.; Kula, Stephanie P.

2013-01-01

This report presents the results of a study to develop methods for estimating selected low-flow statistics and for determining annual flow-duration statistics for Ohio streams. Regression techniques were used to develop equations for estimating 10-year recurrence-interval (10-percent annual-nonexceedance probability) low-flow yields, in cubic feet per second per square mile, with averaging periods of 1, 7, 30, and 90-day(s), and for estimating the yield corresponding to the long-term 80-percent duration flow. These equations, which estimate low-flow yields as a function of a streamflow-variability index, are based on previously published low-flow statistics for 79 long-term continuous-record streamgages with at least 10 years of data collected through water year 1997. When applied to the calibration dataset, average absolute percent errors for the regression equations ranged from 15.8 to 42.0 percent. The regression results have been incorporated into the U.S. Geological Survey (USGS) StreamStats application for Ohio (http://water.usgs.gov/osw/streamstats/ohio.html) in the form of a yield grid to facilitate estimation of the corresponding streamflow statistics in cubic feet per second. Logistic-regression equations also were developed and incorporated into the USGS StreamStats application for Ohio for selected low-flow statistics to help identify occurrences of zero-valued statistics. Quantiles of daily and 7-day mean streamflows were determined for annual and annual-seasonal (September–November) periods for each complete climatic year of streamflow-gaging station record for 110 selected streamflow-gaging stations with 20 or more years of record. The quantiles determined for each climatic year were the 99-, 98-, 95-, 90-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 25-, 20-, 10-, 5-, 2-, and 1-percent exceedance streamflows. Selected exceedance percentiles of the annual-exceedance percentiles were subsequently computed and tabulated to help facilitate consideration of the annual risk of exceedance or nonexceedance of annual and annual-seasonal-period flow-duration values. The quantiles are based on streamflow data collected through climatic year 2008.

Evaluating models of healthcare delivery using the Model of Care Evaluation Tool (MCET).

PubMed

Hudspeth, Randall S; Vogt, Marjorie; Wysocki, Ken; Pittman, Oralea; Smith, Susan; Cooke, Cindy; Dello Stritto, Rita; Hoyt, Karen Sue; Merritt, T Jeanne

2016-08-01

Our aim was to provide the outcome of a structured Model of Care (MoC) Evaluation Tool (MCET), developed by an FAANP Best-practices Workgroup, that can be used to guide the evaluation of existing MoCs being considered for use in clinical practice. Multiple MoCs are available, but deciding which model of health care delivery to use can be confusing. This five-component tool provides a structured assessment approach to model selection and has universal application. A literature review using CINAHL, PubMed, Ovid, and EBSCO was conducted. The MCET evaluation process includes five sequential components with a feedback loop from component 5 back to component 3 for reevaluation of any refinements. The components are as follows: (1) Background, (2) Selection of an MoC, (3) Implementation, (4) Evaluation, and (5) Sustainability and Future Refinement. This practical resource considers an evidence-based approach to use in determining the best model to implement based on need, stakeholder considerations, and feasibility. ©2015 American Association of Nurse Practitioners.

Interventions to Modify Health Care Provider Adherence to Asthma Guidelines: A Systematic Review

PubMed Central

Okelo, Sande O.; Butz, Arlene M.; Sharma, Ritu; Diette, Gregory B.; Pitts, Samantha I.; King, Tracy M.; Linn, Shauna T.; Reuben, Manisha; Chelladurai, Yohalakshmi

2013-01-01

BACKGROUND AND OBJECTIVE: Health care provider adherence to asthma guidelines is poor. The objective of this study was to assess the effect of interventions to improve health care providers’ adherence to asthma guidelines on health care process and clinical outcomes. METHODS: Data sources included Medline, Embase, Cochrane CENTRAL Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, PsycINFO, and Research and Development Resource Base in Continuing Medical Education up to July 2012. Paired investigators independently assessed study eligibility. Investigators abstracted data sequentially and independently graded the evidence. RESULTS: Sixty-eight eligible studies were classified by intervention: decision support, organizational change, feedback and audit, clinical pharmacy support, education only, quality improvement/pay-for-performance, multicomponent, and information only. Half were randomized trials (n = 35). There was moderate evidence for increased prescriptions of controller medications for decision support, feedback and audit, and clinical pharmacy support and low-grade evidence for organizational change and multicomponent interventions. Moderate evidence supports the use of decision support and clinical pharmacy interventions to increase provision of patient self-education/asthma action plans. Moderate evidence supports use of decision support tools to reduce emergency department visits, and low-grade evidence suggests there is no benefit for this outcome with organizational change, education only, and quality improvement/pay-for-performance. CONCLUSIONS: Decision support tools, feedback and audit, and clinical pharmacy support were most likely to improve provider adherence to asthma guidelines, as measured through health care process outcomes. There is a need to evaluate health care provider-targeted interventions with standardized outcomes. PMID:23979092

Prototype Willingness Model Drinking Cognitions Mediate Personalized Normative Feedback Efficacy.

PubMed

Lewis, Melissa A; Litt, Dana M; Tomkins, Mary; Neighbors, Clayton

2017-05-01

Personalized normative feedback (PNF) interventions have been shown to be efficacious at reducing college student drinking. Because descriptive norms have been shown to mediate PNF efficacy, the current study focused on examining additional prototype willingness model social reaction cognitions, namely, prototypes and willingness, as mediators of intervention efficacy. We expected the PNF interventions to be associated with increased prototype favorability of students who do not drink, which would in turn be associated with decreased willingness to drink and subsequently, less drinking. The current study included 622 college students (53.2% women; 62% Caucasian) who reported one or more heavy drinking episodes in the past month and completed baseline and three-month follow-up assessments. As posited by the framework of the prototype willingness model, sequential mediation analyses were conducted to evaluate increases in abstainer prototype favorability on willingness on drinking, and subsequently willingness to drink on drinking behavior. Mediation results revealed significant indirect effects of PNF on three-month drinking through three-month prototypes and willingness, indicating that the social reaction pathway of the prototype willingness model was supported. Findings have important implications for PNF interventions aiming to reduce high-risk drinking among college students. Study findings suggest that we should consider looking at additional socially-based mediators of PNF efficacy in addition to perceived descriptive norms.

Prototype Willingness Model Drinking Cognitions Mediate Personalized Normative Feedback Efficacy

PubMed Central

Litt, Dana M.; Tomkins, Mary; Neighbors, Clayton

2017-01-01

Personalized normative feedback (PNF) interventions have been shown to be efficacious at reducing college student drinking. Because descriptive norms have been shown to mediate PNF efficacy, the current study focused on examining additional prototype willingness model social reaction cognitions, namely, prototypes and willingness, as mediators of intervention efficacy. We expected the PNF interventions to be associated with increased prototype favorability of students who do not drink, which would in turn be associated with decreased willingness to drink and subsequently, less drinking. The current study included 622 college students (53.2% women; 62% Caucasian) who reported one or more heavy drinking episodes in the past month and completed baseline and three-month follow-up assessments. As posited by the framework of the prototype willingness model, sequential mediation analyses were conducted to evaluate increases in abstainer prototype favorability on willingness on drinking, and subsequently willingness to drink on drinking behavior. Mediation results revealed significant indirect effects of PNF on three-month drinking through three-month prototypes and willingness, indicating that the social reaction pathway of the prototype willingness model was supported. Findings have important implications for PNF interventions aiming to reduce high-risk drinking among college students. Study findings suggest that we should consider looking at additional socially-based mediators of PNF efficacy in addition to perceived descriptive norms. PMID:27995431

Developing a Self-Report-Based Sequential Analysis Method for Educational Technology Systems: A Process-Based Usability Evaluation

ERIC Educational Resources Information Center

Lin, Yi-Chun; Hsieh, Ya-Hui; Hou, Huei-Tse

2015-01-01

The development of a usability evaluation method for educational systems or applications, called the self-report-based sequential analysis, is described herein. The method aims to extend the current practice by proposing self-report-based sequential analysis as a new usability method, which integrates the advantages of self-report in survey…

Robust Feedback Control of Flow Induced Structural Radiation of Sound

NASA Technical Reports Server (NTRS)

Heatwole, Craig M.; Bernhard, Robert J.; Franchek, Matthew A.

1997-01-01

A significant component of the interior noise of aircraft and automobiles is a result of turbulent boundary layer excitation of the vehicular structure. In this work, active robust feedback control of the noise due to this non-predictable excitation is investigated. Both an analytical model and experimental investigations are used to determine the characteristics of the flow induced structural sound radiation problem. The problem is shown to be broadband in nature with large system uncertainties associated with the various operating conditions. Furthermore the delay associated with sound propagation is shown to restrict the use of microphone feedback. The state of the art control methodologies, IL synthesis and adaptive feedback control, are evaluated and shown to have limited success for solving this problem. A robust frequency domain controller design methodology is developed for the problem of sound radiated from turbulent flow driven plates. The control design methodology uses frequency domain sequential loop shaping techniques. System uncertainty, sound pressure level reduction performance, and actuator constraints are included in the design process. Using this design method, phase lag was added using non-minimum phase zeros such that the beneficial plant dynamics could be used. This general control approach has application to lightly damped vibration and sound radiation problems where there are high bandwidth control objectives requiring a low controller DC gain and controller order.

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care.

PubMed

Kowalski, Amanda

2016-01-02

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to induce variation in an individual's own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from -0.76 to -1.49, which are an order of magnitude larger than previous estimates.

Evaluation of normalization methods in mammalian microRNA-Seq data

PubMed Central

Garmire, Lana Xia; Subramaniam, Shankar

2012-01-01

Simple total tag count normalization is inadequate for microRNA sequencing data generated from the next generation sequencing technology. However, so far systematic evaluation of normalization methods on microRNA sequencing data is lacking. We comprehensively evaluate seven commonly used normalization methods including global normalization, Lowess normalization, Trimmed Mean Method (TMM), quantile normalization, scaling normalization, variance stabilization, and invariant method. We assess these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic. Additionally, we evaluate the methods with results from quantitative PCR validation. Our results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst. The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution. PMID:22532701

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

PubMed Central

Astola, Laura; Molenaar, Jaap

2014-01-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data. PMID:27600344

Constructing probabilistic scenarios for wide-area solar power generation

DOE PAGES

Woodruff, David L.; Deride, Julio; Staid, Andrea; ...

2017-12-22

Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions.more » Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.« less

Two types of physical inconsistency to avoid with quantile mapping: a case study with relative humidity over North America.

NASA Astrophysics Data System (ADS)

Grenier, P.

2017-12-01

Statistical post-processing techniques aim at generating plausible climate scenarios from climate simulations and observation-based reference products. These techniques are generally not physically-based, and consequently they remedy the problem of simulation biases at the risk of generating physical inconsistency (PI). Although this concern is often emphasized, it is rarely addressed quantitatively. Here, PI generated by quantile mapping (QM), a technique widely used in climatological and hydrological applications, is investigated using relative humidity (RH) and its parent variables, namely specific humidity (SH), temperature and pressure. PI is classified into two types: 1) inadequate value for an individual variable (e.g. RH > 100 %), and 2) breaking of an inter-variable relationship. Scenarios built for this study correspond to twelve sites representing a variety of climate types over North America. Data used are an ensemble of ten 3-hourly global (CMIP5) and regional (CORDEX-NAM) simulations, as well as the CFSR reanalysis. PI of type 1 is discussed in terms of frequency of occurrence and amplitude of unphysical cases for RH and SH variables. PI of type 2 is investigated with heuristic proxies designed to directly compare the physical inconsistency problem with the initial bias problem. Finally, recommendations are provided for an appropriate use of QM given the potential to generate physical inconsistency of types 1 and 2.

Fine-tuning satellite-based rainfall estimates

NASA Astrophysics Data System (ADS)

Harsa, Hastuadi; Buono, Agus; Hidayat, Rahmat; Achyar, Jaumil; Noviati, Sri; Kurniawan, Roni; Praja, Alfan S.

2018-05-01

Rainfall datasets are available from various sources, including satellite estimates and ground observation. The locations of ground observation scatter sparsely. Therefore, the use of satellite estimates is advantageous, because satellite estimates can provide data on places where the ground observations do not present. However, in general, the satellite estimates data contain bias, since they are product of algorithms that transform the sensors response into rainfall values. Another cause may come from the number of ground observations used by the algorithms as the reference in determining the rainfall values. This paper describe the application of bias correction method to modify the satellite-based dataset by adding a number of ground observation locations that have not been used before by the algorithm. The bias correction was performed by utilizing Quantile Mapping procedure between ground observation data and satellite estimates data. Since Quantile Mapping required mean and standard deviation of both the reference and the being-corrected data, thus the Inverse Distance Weighting scheme was applied beforehand to the mean and standard deviation of the observation data in order to provide a spatial composition of them, which were originally scattered. Therefore, it was possible to provide a reference data point at the same location with that of the satellite estimates. The results show that the new dataset have statistically better representation of the rainfall values recorded by the ground observation than the previous dataset.

Constructing probabilistic scenarios for wide-area solar power generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodruff, David L.; Deride, Julio; Staid, Andrea

Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions.more » Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.« less

[Socioeconomic factors conditioning obesity in adults. Evidence based on quantile regression and panel data].

PubMed

Temporelli, Karina L; Viego, Valentina N

2016-08-01

Objective To measure the effect of socioeconomic variables on the prevalence of obesity. Factors such as income level, urbanization, incorporation of women into the labor market and access to unhealthy foods are considered in this paper. Method Econometric estimates of the proportion of obese men and women by country were calculated using models based on panel data and quantile regressions, with data from 192 countries for the period 2002-2005.Levels of per capita income, urbanization, income/big mac ratio price and labor indicators for female population were considered as explanatory variables. Results Factors that have influence over obesity in adults differ between men and women; accessibility to fast food is related to male obesity, while the employment mode causes higher rates in women. The underlying socioeconomic factors for obesity are also different depending on the magnitude of this problem in each country; in countries with low prevalence, a greater level of income favor the transition to obesogenic habits, while a higher income level mitigates the problem in those countries with high rates of obesity. Discussion Identifying the socio-economic causes of the significant increase in the prevalence of obesity is essential for the implementation of effective strategies for prevention, since this condition not only affects the quality of life of those who suffer from it but also puts pressure on health systems due to the treatment costs of associated diseases.

Exploring the sequential lineup advantage using WITNESS.

PubMed

Goodsell, Charles A; Gronlund, Scott D; Carlson, Curt A

2010-12-01

Advocates claim that the sequential lineup is an improvement over simultaneous lineup procedures, but no formal (quantitatively specified) explanation exists for why it is better. The computational model WITNESS (Clark, Appl Cogn Psychol 17:629-654, 2003) was used to develop theoretical explanations for the sequential lineup advantage. In its current form, WITNESS produced a sequential advantage only by pairing conservative sequential choosing with liberal simultaneous choosing. However, this combination failed to approximate four extant experiments that exhibited large sequential advantages. Two of these experiments became the focus of our efforts because the data were uncontaminated by likely suspect position effects. Decision-based and memory-based modifications to WITNESS approximated the data and produced a sequential advantage. The next step is to evaluate the proposed explanations and modify public policy recommendations accordingly.

Scaling of Precipitation Extremes Modelled by Generalized Pareto Distribution

NASA Astrophysics Data System (ADS)

Rajulapati, C. R.; Mujumdar, P. P.

2017-12-01

Precipitation extremes are often modelled with data from annual maximum series or peaks over threshold series. The Generalized Pareto Distribution (GPD) is commonly used to fit the peaks over threshold series. Scaling of precipitation extremes from larger time scales to smaller time scales when the extremes are modelled with the GPD is burdened with difficulties arising from varying thresholds for different durations. In this study, the scale invariance theory is used to develop a disaggregation model for precipitation extremes exceeding specified thresholds. A scaling relationship is developed for a range of thresholds obtained from a set of quantiles of non-zero precipitation of different durations. The GPD parameters and exceedance rate parameters are modelled by the Bayesian approach and the uncertainty in scaling exponent is quantified. A quantile based modification in the scaling relationship is proposed for obtaining the varying thresholds and exceedance rate parameters for shorter durations. The disaggregation model is applied to precipitation datasets of Berlin City, Germany and Bangalore City, India. From both the applications, it is observed that the uncertainty in the scaling exponent has a considerable effect on uncertainty in scaled parameters and return levels of shorter durations.

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements

DOE PAGES

Jeon, Soyoung; Paciorek, Christopher J.; Wehner, Michael F.

2016-02-16

Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output basedmore » on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.« less

Nuclear morphology for the detection of alterations in bronchial cells from lung cancer: an attempt to improve sensitivity and specificity.

PubMed

Fafin-Lefevre, Mélanie; Morlais, Fabrice; Guittet, Lydia; Clin, Bénédicte; Launoy, Guy; Galateau-Sallé, Françoise; Plancoulaine, Benoît; Herlin, Paulette; Letourneux, Marc

2011-08-01

To identify which morphologic or densitometric parameters are modified in cell nuclei from bronchopulmonary cancer based on 18 parameters involving shape, intensity, chromatin, texture, and DNA content and develop a bronchopulmonary cancer screening method relying on analysis of sputum sample cell nuclei. A total of 25 sputum samples from controls and 22 bronchial aspiration samples from patients presenting with bronchopulmonary cancer who were professionally exposed to cancer were used. After Feulgen staining, 18 morphologic and DNA content parameters were measured on cell nuclei, via image cytom- etry. A method was developed for analyzing distribution quantiles, compared with simply interpreting mean values, to characterize morphologic modifications in cell nuclei. Distribution analysis of parameters enabled us to distinguish 13 of 18 parameters that demonstrated significant differences between controls and cancer cases. These parameters, used alone, enabled us to distinguish two population types, with both sensitivity and specificity > 70%. Three parameters offered 100% sensitivity and specificity. When mean values offered high sensitivity and specificity, comparable or higher sensitivity and specificity values were observed for at least one of the corresponding quantiles. Analysis of modification in morphologic parameters via distribution analysis proved promising for screening bronchopulmonary cancer from sputum.

Systematic and Iterative Development of a Smartphone App to Promote Sun-Protection Among Holidaymakers: Design of a Prototype and Results of Usability and Acceptability Testing.

PubMed

Rodrigues, Angela M; Sniehotta, Falko F; Birch-Machin, Mark A; Olivier, Patrick; Araújo-Soares, Vera

2017-06-12

Sunburn and intermittent exposure to ultraviolet rays are risk factors for melanoma. Sunburn is a common experience during holidays, making tourism settings of particular interest for skin cancer prevention. Holidaymakers are a volatile populations found at different locations, which may make them difficult to reach. Given the widespread use of smartphones, evidence suggests that this might be a novel, convenient, scalable, and feasible way of reaching the target population. The main objective of this study was to describe and appraise the process of systematically developing a smartphone intervention (mISkin app) to promote sun-protection during holidays. The iterative development process of the mISkin app was conducted over four sequential stages: (1) identify evidence on the most effective behavior change techniques (BCTs) used (active ingredients) as well as theoretical predictors and theories, (2) evidence-based intervention design, (3) co-design with users of the mISkin app prototype, and (4) refinement of the app. Each stage provided key findings that were subsequently used to inform the design of the mISkin app. The sequential approach to development integrates different strands of evidence to inform the design of an evidence-based intervention. A systematic review on previously tested interventions to promote sun-protection provided cues and constraints for the design of this intervention. The development and design of the mISkin app also incorporated other sources of information, such as other literature reviews and experts' consultations. The developed prototype of the mISkin app was evaluated by engaging potential holidaymakers in the refinement and further development of the mISkin app through usability (ease-of-use) and acceptability testing of the intervention prototype. All 17 participants were satisfied with the mISkin prototype and expressed willingness to use it. Feedback on the app was integrated in the optimization process of the mISkin app. The mISkin app was designed to promote sun-protection among holidaymakers and was based on current evidence, experts' knowledge and experience, and user involvement. Based on user feedback, the app has been refined and a fully functional version is ready for formal testing in a feasibility pilot study. ©Angela M Rodrigues, Falko F Sniehotta, Mark A Birch-Machin, Patrick Olivier, Vera Araújo-Soares. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 12.06.2017.

Online Graph Completion: Multivariate Signal Recovery in Computer Vision.

PubMed

Kim, Won Hwa; Jalal, Mona; Hwang, Seongjae; Johnson, Sterling C; Singh, Vikas

2017-07-01

The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.

A Protein Domain and Family Based Approach to Rare Variant Association Analysis.

PubMed

Richardson, Tom G; Shihab, Hashem A; Rivas, Manuel A; McCarthy, Mark I; Campbell, Colin; Timpson, Nicholas J; Gaunt, Tom R

2016-01-01

It has become common practice to analyse large scale sequencing data with statistical approaches based around the aggregation of rare variants within the same gene. We applied a novel approach to rare variant analysis by collapsing variants together using protein domain and family coordinates, regarded to be a more discrete definition of a biologically functional unit. Using Pfam definitions, we collapsed rare variants (Minor Allele Frequency ≤ 1%) together in three different ways 1) variants within single genomic regions which map to individual protein domains 2) variants within two individual protein domain regions which are predicted to be responsible for a protein-protein interaction 3) all variants within combined regions from multiple genes responsible for coding the same protein domain (i.e. protein families). A conventional collapsing analysis using gene coordinates was also undertaken for comparison. We used UK10K sequence data and investigated associations between regions of variants and lipid traits using the sequence kernel association test (SKAT). We observed no strong evidence of association between regions of variants based on Pfam domain definitions and lipid traits. Quantile-Quantile plots illustrated that the overall distributions of p-values from the protein domain analyses were comparable to that of a conventional gene-based approach. Deviations from this distribution suggested that collapsing by either protein domain or gene definitions may be favourable depending on the trait analysed. We have collapsed rare variants together using protein domain and family coordinates to present an alternative approach over collapsing across conventionally used gene-based regions. Although no strong evidence of association was detected in these analyses, future studies may still find value in adopting these approaches to detect previously unidentified association signals.

Robust Inference of Risks of Large Portfolios

PubMed Central

Fan, Jianqing; Han, Fang; Liu, Han; Vickers, Byron

2016-01-01

We propose a bootstrap-based robust high-confidence level upper bound (Robust H-CLUB) for assessing the risks of large portfolios. The proposed approach exploits rank-based and quantile-based estimators, and can be viewed as a robust extension of the H-CLUB procedure (Fan et al., 2015). Such an extension allows us to handle possibly misspecified models and heavy-tailed data, which are stylized features in financial returns. Under mixing conditions, we analyze the proposed approach and demonstrate its advantage over H-CLUB. We further provide thorough numerical results to back up the developed theory, and also apply the proposed method to analyze a stock market dataset. PMID:27818569

Characterization of mathematics instructional practises for prospective elementary teachers with varying levels of self-efficacy in classroom management and mathematics teaching

NASA Astrophysics Data System (ADS)

Lee, Carrie W.; Walkowiak, Temple A.; Nietfeld, John L.

2017-03-01

The purpose of this study was to investigate the relationship between prospective teachers' (PTs) instructional practises and their efficacy beliefs in classroom management and mathematics teaching. A sequential, explanatory mixed-methods design was employed. Results from efficacy surveys, implemented with 54 PTs were linked to a sample of teachers' instructional practises during the qualitative phase. In this phase, video-recorded lessons were analysed based on tasks, representations, discourse, and classroom management. Findings indicate that PTs with higher levels of mathematics teaching efficacy taught lessons characterised by tasks of higher cognitive demand, extended student explanations, student-to-student discourse, and explicit connections between representations. Classroom management efficacy seems to bear influence on the utilised grouping structures. These findings support explicit attention to PTs' mathematics teaching and classroom management efficacy throughout teacher preparation and a need for formative feedback to inform development of beliefs about teaching practises.

Reading Remediation Based on Sequential and Simultaneous Processing.

ERIC Educational Resources Information Center

Gunnison, Judy; And Others

1982-01-01

The theory postulating a dichotomy between sequential and simultaneous processing is reviewed and its implications for remediating reading problems are reviewed. Research is cited on sequential-simultaneous processing for early and advanced reading. A list of remedial strategies based on the processing dichotomy addresses decoding and lexical…

Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs – A Comparative Study

PubMed Central

Meyer, Swanhild U.; Kaiser, Sebastian; Wagner, Carola; Thirion, Christian; Pfaffl, Michael W.

2012-01-01

Background Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. Methodology/Principal Findings We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. Conclusions/Significance We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments. PMID:22723911

Assessing the pollution risk of a groundwater source field at western Laizhou Bay under seawater intrusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, Xiankui; Wu, Jichun; Wang, Dong, E-mail: wangdong@nju.edu.cn

Coastal areas have great significance for human living, economy and society development in the world. With the rapid increase of pressures from human activities and climate change, the safety of groundwater resource is under the threat of seawater intrusion in coastal areas. The area of Laizhou Bay is one of the most serious seawater intruded areas in China, since seawater intrusion phenomenon was firstly recognized in the middle of 1970s. This study assessed the pollution risk of a groundwater source filed of western Laizhou Bay area by inferring the probability distribution of groundwater Cl{sup −} concentration. The numerical model ofmore » seawater intrusion process is built by using SEAWAT4. The parameter uncertainty of this model is evaluated by Markov Chain Monte Carlo (MCMC) simulation, and DREAM{sub (ZS)} is used as sampling algorithm. Then, the predictive distribution of Cl{sup -} concentration at groundwater source field is inferred by using the samples of model parameters obtained from MCMC. After that, the pollution risk of groundwater source filed is assessed by the predictive quantiles of Cl{sup -} concentration. The results of model calibration and verification demonstrate that the DREAM{sub (ZS)} based MCMC is efficient and reliable to estimate model parameters under current observation. Under the condition of 95% confidence level, the groundwater source point will not be polluted by seawater intrusion in future five years (2015–2019). In addition, the 2.5% and 97.5% predictive quantiles show that the Cl{sup −} concentration of groundwater source field always vary between 175 mg/l and 200 mg/l. - Highlights: • The parameter uncertainty of seawater intrusion model is evaluated by MCMC. • Groundwater source field won’t be polluted by seawater intrusion in future 5 years. • The pollution risk is assessed by the predictive quantiles of Cl{sup −} concentration.« less

Cortical Feedback Regulates Feedforward Retinogeniculate Refinement

PubMed Central

Thompson, Andrew D; Picard, Nathalie; Min, Lia; Fagiolini, Michela; Chen, Chinfei

2016-01-01

SUMMARY According to the prevailing view of neural development, sensory pathways develop sequentially in a feedforward manner, whereby each local microcircuit refines and stabilizes before directing the wiring of its downstream target. In the visual system, retinal circuits are thought to mature first and direct refinement in the thalamus, after which cortical circuits refine with experience-dependent plasticity. In contrast, we now show that feedback from cortex to thalamus critically regulates refinement of the retinogeniculate projection during a discrete window in development, beginning at postnatal day 20 in mice. Disrupting cortical activity during this window, pharmacologically or chemogenetically, increases the number of retinal ganglion cells innervating each thalamic relay neuron. These results suggest that primary sensory structures develop through the concurrent and interdependent remodeling of subcortical and cortical circuits in response to sensory experience, rather than through a simple feedforward process. Our findings also highlight an unexpected function for the corticothalamic projection. PMID:27545712

Self-controlled practice enhances motor learning in introverts and extroverts.

PubMed

Kaefer, Angélica; Chiviacowsky, Suzete; Meira, Cassio de Miranda; Tani, Go

2014-06-01

The purpose of the present study was to investigate the effects of self-controlled feedback on the learning of a sequential-timing motor task in introverts and extroverts. Fifty-six university students were selected by the Eysenck Personality Questionnaire. They practiced a motor task consisting of pressing computer keyboard keys in a specific spatial and temporal pattern. The experiment consisted of practice, retention, and transfer phases. The participants were distributed into 4 groups, formed by the combination of personality trait (extraversion/introversion) and type of feedback frequency (self-controlled/yoked). The results showed superior learning for the groups that practiced in a self-controlled schedule, in relation to groups who practiced in an externally controlled schedule, F(1, 52) = 4.13, p < .05, eta2 = .07, regardless of personality trait. We conclude that self-controlled practice enhances motor learning in introverts and extroverts.

The N-shaped environmental Kuznets curve: an empirical evaluation using a panel quantile regression approach.

PubMed

Allard, Alexandra; Takman, Johanna; Uddin, Gazi Salah; Ahmed, Ali

2018-02-01

We evaluate the N-shaped environmental Kuznets curve (EKC) using panel quantile regression analysis. We investigate the relationship between CO 2 emissions and GDP per capita for 74 countries over the period of 1994-2012. We include additional explanatory variables, such as renewable energy consumption, technological development, trade, and institutional quality. We find evidence for the N-shaped EKC in all income groups, except for the upper-middle-income countries. Heterogeneous characteristics are, however, observed over the N-shaped EKC. Finally, we find a negative relationship between renewable energy consumption and CO 2 emissions, which highlights the importance of promoting greener energy in order to combat global warming.

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care

PubMed Central

Kowalski, Amanda

2015-01-01

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member’s injury to induce variation in an individual’s own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from −0.76 to −1.49, which are an order of magnitude larger than previous estimates. PMID:26977117

Exploring the role of 3-dimensional simulation in surgical training: feedback from a pilot study.

PubMed

Podolsky, Dale J; Martin, Allan R; Whyne, Cari M; Massicotte, Eric M; Hardisty, Michael R; Ginsberg, Howard J

2010-12-01

Randomized control study assessing the efficacy of a pedicle screw insertion simulator. To evaluate the efficacy of an in-house developed 3-dimensional software simulation tool for teaching pedicle screw insertion, to gather feedback about the utility of the simulator, and to help identify the context and role such simulation has in surgical education. Traditional instruction for pedicle screw insertion technique consists of didactic teaching and limited hands-on training on artificial or cadaveric models before guided supervision within the operating room. Three-dimensional computer simulation can provide a valuable tool for practicing challenging surgical procedures; however, its potential lies in its effective integration into student learning. Surgical residents were recruited from 2 sequential years of a spine surgery course. Patient and control groups both received standard training on pedicle screw insertion. The patient group received an additional 1-hour session of training on the simulator using a CT-based 3-dimensional model of their assigned cadaver's spine. Qualitative feedback about the simulator was gathered from the trainees, fellows, and staff surgeons, and all pedicles screws physically inserted into the cadavers during the courses were evaluated through CT. A total of 185 thoracic and lumbar pedicle screws were inserted by 37 trainees. Eighty-two percent of the 28 trainees who responded to the questionnaire and all fellows and staff surgeons felt the simulator to be a beneficial educational tool. However, the 1-hour training session did not yield improved performance in screw placement. A 3-dimensional computer-based simulation for pedicle screw insertion was integrated into a cadaveric spine surgery instructional course. Overall, the tool was positively regarded by the trainees, fellows, and staff surgeons. However, the limited training with the simulator did not translate into widespread comfort with its operation or into improvement in physical screw placement.

UAV-imaging to model growth response of marram grass to sand burial: Implications for coastal dune development

NASA Astrophysics Data System (ADS)

Nolet, Corjan; van Puijenbroek, Marinka; Suomalainen, Juha; Limpens, Juul; Riksen, Michel

2018-04-01

Vegetated coastal dunes have the capacity to keep up with sea-level rise by accumulating and stabilizing wind-blown sand. In Europe, this is attributed to marram grass (Ammophila arenaria), a coastal grass species that combines two unique advantages for dune-building: (1) a very high tolerance to burial by wind-blown sand, and (2) more vigorous growth due to positive feedback to sand burial. However, while these vegetation characteristics have been demonstrated, observational data has not been used to model a function to describe the growth response of Ammophila to sand burial. Studies that model coastal dune development by incorporating positive feedback, as a result, may be hampered by growth functions that are unvalidated against field data. Therefore, this study aims to parameterize an empirical relationship to model the growth response of Ammophila to burial by wind-blown sand. A coastal foredune along a nourished beach in the Netherlands was monitored from April 2015 to April 2016. High-resolution geospatial data was acquired using an Unmanned Aerial Vehicle (UAV). Growth response of Ammophila, expressed by changes in Normalized Difference Vegetation Index (Δ NDVI) and vegetation cover (Δ Cover), is related to a sand burial gradient by fitting a Gaussian function using nonlinear quantile regression. The regression curves indicate an optimal burial rate for Ammophila of 0.31 m of sand per growing season, and suggest (by extrapolation of the data) a maximum burial tolerance for Ammophila between 0.78 (for Δ Cover) and 0.96 m (for Δ NDVI) of sand per growing season. These findings are advantageous to coastal management: maximizing the potential of Ammophila to develop dunes maximizes the potential of coastal dunes to provide coastal safety.

[Professor GAO Yuchun's experience on "sequential acupuncture leads to smooth movement of qi"].

PubMed

Wang, Yanjun; Xing, Xiao; Cui, Linhua

2016-01-01

Professor GAO Yuchun is considered as the key successor of GAO's academic school of acupuncture and moxibustion in Yanzhao region. Professor GAO's clinical experience of, "sequential acupuncture" is introduced in details in this article. In Professor GAO's opinions, appropriate acupuncture sequence is the key to satisfactory clinical effects during treatment. Based on different acupoints, sequential acupuncture can achieve the aim of qi following needles and needles leading qi; based on different symptoms, sequential acupuncture can regulate qi movement; based on different body positions, sequential acupuncture can harmonize qi-blood and reinforcing deficiency and reducing excess. In all, according to the differences of disease condition and constitution, based on the accurate acupoint selection and appropriate manipulation, it is essential to capture the nature of diseases and make the order of acupuncture, which can achieve the aim of regulating qi movement and reinforcing deficiency and reducing excess.

Multi-Level Sequential Pattern Mining Based on Prime Encoding

NASA Astrophysics Data System (ADS)

Lianglei, Sun; Yun, Li; Jiang, Yin

Encoding is not only to express the hierarchical relationship, but also to facilitate the identification of the relationship between different levels, which will directly affect the efficiency of the algorithm in the area of mining the multi-level sequential pattern. In this paper, we prove that one step of division operation can decide the parent-child relationship between different levels by using prime encoding and present PMSM algorithm and CROSS-PMSM algorithm which are based on prime encoding for mining multi-level sequential pattern and cross-level sequential pattern respectively. Experimental results show that the algorithm can effectively extract multi-level and cross-level sequential pattern from the sequence database.

High Risk Flash Flood Rainstorm Mapping Based on Regional L-moments Approach

NASA Astrophysics Data System (ADS)

Ding, Hui; Liao, Yifan; Lin, Bingzhang

2017-04-01

Difficulties and complexities in elaborating flash flood early-warning and forecasting system prompt hydrologists to develop some techniques to substantially reduce the disastrous outcome of a flash flood in advance. An ideal to specify those areas that are subject at high risk to flash flood in terms of rainfall intensity in a relatively large region is proposed in this paper. It is accomplished through design of the High Risk Flash Flood Rainstorm Area (HRFFRA) based on statistical analysis of historical rainfall data, synoptic analysis of prevailing storm rainfalls as well as the field survey of historical flash flood events in the region. A HRFFRA is defined as the area potentially under hitting by higher intense-precipitation for a given duration with certain return period that may cause a flash flood disaster in the area. This paper has presented in detail the development of the HRFFRA through the application of the end-to-end Regional L-moments Approach (RLMA) to precipitation frequency analysis in combination with the technique of spatial interpolation in Jiangxi Province, South China Mainland. Among others, the concept of hydrometeorologically homogenous region, the precision of frequency analysis in terms of parameter estimation, the accuracy of quantiles in terms of uncertainties and the consistency adjustments of quantiles over durations and space, etc., have been addressed. At the end of this paper, the mapping of the HRFFRA and an internet-based visualized user-friendly data-server of the HRFFRA are also introduced. Key words: HRFFRA; Flash Flood; RLMA; rainfall intensity; Hydrometeorological homogenous region.

Collaborative virtual reality based advanced cardiac life support training simulator using virtual reality principles.

PubMed

Khanal, Prabal; Vankipuram, Akshay; Ashby, Aaron; Vankipuram, Mithra; Gupta, Ashish; Drumm-Gurnee, Denise; Josey, Karen; Tinker, Linda; Smith, Marshall

2014-10-01

Advanced Cardiac Life Support (ACLS) is a series of team-based, sequential and time constrained interventions, requiring effective communication and coordination of activities that are performed by the care provider team on a patient undergoing cardiac arrest or respiratory failure. The state-of-the-art ACLS training is conducted in a face-to-face environment under expert supervision and suffers from several drawbacks including conflicting care provider schedules and high cost of training equipment. The major objective of the study is to describe, including the design, implementation, and evaluation of a novel approach of delivering ACLS training to care providers using the proposed virtual reality simulator that can overcome the challenges and drawbacks imposed by the traditional face-to-face training method. We compare the efficacy and performance outcomes associated with traditional ACLS training with the proposed novel approach of using a virtual reality (VR) based ACLS training simulator. One hundred and forty-eight (148) ACLS certified clinicians, translating into 26 care provider teams, were enrolled for this study. Each team was randomly assigned to one of the three treatment groups: control (traditional ACLS training), persuasive (VR ACLS training with comprehensive feedback components), or minimally persuasive (VR ACLS training with limited feedback components). The teams were tested across two different ACLS procedures that vary in the degree of task complexity: ventricular fibrillation or tachycardia (VFib/VTach) and pulseless electric activity (PEA). The difference in performance between control and persuasive groups was not statistically significant (P=.37 for PEA and P=.1 for VFib/VTach). However, the difference in performance between control and minimally persuasive groups was significant (P=.05 for PEA and P=.02 for VFib/VTach). The pre-post comparison of performances of the groups showed that control (P=.017 for PEA, P=.01 for VFib/VTach) and persuasive (P=.02 for PEA, P=.048 for VFib/VTach) groups improved their performances significantly, whereas minimally persuasive group did not (P=.45 for PEA, P=.46 for VFib/VTach). Results also suggest that the benefit of persuasiveness is constrained by the potentially interruptive nature of these features. Our results indicate that the VR-based ACLS training with proper feedback components can provide a learning experience similar to face-to-face training, and therefore could serve as a more easily accessed supplementary training tool to the traditional ACLS training. Our findings also suggest that the degree of persuasive features in VR environments have to be designed considering the interruptive nature of the feedback elements. Copyright © 2014 Elsevier Inc. All rights reserved.

Accelerating Approximate Bayesian Computation with Quantile Regression: application to cosmological redshift distributions

NASA Astrophysics Data System (ADS)

Kacprzak, T.; Herbel, J.; Amara, A.; Réfrégier, A.

2018-02-01

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis tool in cosmology and astrophysics. Its drawback, however, is a slow convergence rate. We propose a novel method, which we call qABC, to accelerate ABC with Quantile Regression. In this method, we create a model of quantiles of distance measure as a function of input parameters. This model is trained on a small number of simulations and estimates which regions of the prior space are likely to be accepted into the posterior. Other regions are then immediately rejected. This procedure is then repeated as more simulations are available. We apply it to the practical problem of estimation of redshift distribution of cosmological samples, using forward modelling developed in previous work. The qABC method converges to nearly same posterior as the basic ABC. It uses, however, only 20% of the number of simulations compared to basic ABC, achieving a fivefold gain in execution time for our problem. For other problems the acceleration rate may vary; it depends on how close the prior is to the final posterior. We discuss possible improvements and extensions to this method.

Bias correction of satellite-based rainfall data

NASA Astrophysics Data System (ADS)

Bhattacharya, Biswa; Solomatine, Dimitri

2015-04-01

Limitation in hydro-meteorological data availability in many catchments limits the possibility of reliable hydrological analyses especially for near-real-time predictions. However, the variety of satellite based and meteorological model products for rainfall provides new opportunities. Often times the accuracy of these rainfall products, when compared to rain gauge measurements, is not impressive. The systematic differences of these rainfall products from gauge observations can be partially compensated by adopting a bias (error) correction. Many of such methods correct the satellite based rainfall data by comparing their mean value to the mean value of rain gauge data. Refined approaches may also first find out a suitable time scale at which different data products are better comparable and then employ a bias correction at that time scale. More elegant methods use quantile-to-quantile bias correction, which however, assumes that the available (often limited) sample size can be useful in comparing probabilities of different rainfall products. Analysis of rainfall data and understanding of the process of its generation reveals that the bias in different rainfall data varies in space and time. The time aspect is sometimes taken into account by considering the seasonality. In this research we have adopted a bias correction approach that takes into account the variation of rainfall in space and time. A clustering based approach is employed in which every new data point (e.g. of Tropical Rainfall Measuring Mission (TRMM)) is first assigned to a specific cluster of that data product and then, by identifying the corresponding cluster of gauge data, the bias correction specific to that cluster is adopted. The presented approach considers the space-time variation of rainfall and as a result the corrected data is more realistic. Keywords: bias correction, rainfall, TRMM, satellite rainfall

Impact of Community-Based HIV/AIDS Treatment on Household Incomes in Uganda

PubMed Central

Feulefack, Joseph F.; Luckert, Martin K.; Mohapatra, Sandeep; Cash, Sean B.; Alibhai, Arif; Kipp, Walter

2013-01-01

Though health benefits to households in developing countries from antiretroviral treatment (ART) programs are widely reported in the literature, specific estimates regarding impacts of treatments on household incomes are rare. This type of information is important to governments and donors, as it is an indication of returns to their ART investments, and to better understand the role of HIV/AIDS in development. The objective of this study is to estimate the impact of a community-based ART program on household incomes in a previously underserved rural region of Uganda. A community-based ART program, based largely on labor contributions from community volunteers, was implemented and evaluated. All households with HIV/AIDS patients enrolled in the treatment programme (n = 134 households) were surveyed five times; once at the beginning of the treatment and every three months thereafter for a period of one year. Data were collected on household income from cash earnings and value of own production. The analysis, using ordinary least squares and quantile regressions, identifies the impact of the ART program on household incomes over the first year of the treatment, while controlling for heterogeneity in household characteristics and temporal changes. As a result of the treatment, health conditions of virtually all patients improved, and household incomes increased by approximately 30% to 40%, regardless of household income quantile. These increases in income, however, varied significantly depending on socio-demographic and socio-economic control variables. Overall, results show large and significant impacts of the ART program on household incomes, suggesting large returns to public investments in ART, and that treating HIV/AIDS is an important precondition for development. Moreover, development programs that invest in human capital and build wealth are important complements that can increase the returns to ART programs. PMID:23840347

Assessment of a Mobile Game ("MobileKids Monster Manor") to Promote Physical Activity Among Children.

PubMed

Garde, Ainara; Umedaly, Aryannah; Abulnaga, S Mazdak; Robertson, Leah; Junker, Anne; Chanoine, Jean Pierre; Ansermino, J Mark; Dumont, Guy A

2015-04-01

The majority of children in North America are not meeting current physical activity guidelines. The purpose of this study was to evaluate the impact of a mobile phone game ("MobileKids Monster Manor") as a tool to promote voluntary physical activity among children. The game integrates data from an accelerometer-based activity monitor (Tractivity(®); Kineteks Corp., Vancouver, BC, Canada) wirelessly connected to a phone and was developed with the involvement of a team of young advisors (KidsCan Initiative: Involving Youth as Ambassadors for Research). Fifty-four children 8-13 years old completed a week of baseline data collection by wearing an accelerometer but receiving no feedback about their activity levels. The 54 children were then sequentially assigned to two groups: One group played "MobileKids Monster Manor," and the other received daily activity feedback (steps and active minutes) via an online program. The physical activity (baseline and intervention weeks) was measured using the activity monitor and compared using two-way repeated-measures analysis of variance (intervention×time). Forty-seven children with a body mass index (BMI) z-score of 0.35±1.18 successfully completed the study. Significant (P=0.01) increases in physical activity were observed during the intervention week in both the game and feedback groups (1191 and 796 steps/day, respectively). In the game group, greater physical activity was demonstrated in children with higher BMI z-score, showing 964 steps/day more per BMI z-score unit (P=0.03; 95 percent confidence interval of 98 to 1829). Further investigation is required to confirm that our game design promotes physical activity.

Automated surgical skill assessment in RMIS training.

PubMed

Zia, Aneeq; Essa, Irfan

2018-05-01

Manual feedback in basic robot-assisted minimally invasive surgery (RMIS) training can consume a significant amount of time from expert surgeons' schedule and is prone to subjectivity. In this paper, we explore the usage of different holistic features for automated skill assessment using only robot kinematic data and propose a weighted feature fusion technique for improving score prediction performance. Moreover, we also propose a method for generating 'task highlights' which can give surgeons a more directed feedback regarding which segments had the most effect on the final skill score. We perform our experiments on the publicly available JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) and evaluate four different types of holistic features from robot kinematic data-sequential motion texture (SMT), discrete Fourier transform (DFT), discrete cosine transform (DCT) and approximate entropy (ApEn). The features are then used for skill classification and exact skill score prediction. Along with using these features individually, we also evaluate the performance using our proposed weighted combination technique. The task highlights are produced using DCT features. Our results demonstrate that these holistic features outperform all previous Hidden Markov Model (HMM)-based state-of-the-art methods for skill classification on the JIGSAWS dataset. Also, our proposed feature fusion strategy significantly improves performance for skill score predictions achieving up to 0.61 average spearman correlation coefficient. Moreover, we provide an analysis on how the proposed task highlights can relate to different surgical gestures within a task. Holistic features capturing global information from robot kinematic data can successfully be used for evaluating surgeon skill in basic surgical tasks on the da Vinci robot. Using the framework presented can potentially allow for real-time score feedback in RMIS training and help surgical trainees have more focused training.

Use of Provider-Level Dashboards and Pay-for-Performance in Venous Thromboembolism Prophylaxis*

PubMed Central

Michtalik, Henry J.; Carolan, Howard T.; Haut, Elliott R.; Lau, Brandyn D.; Streiff, Michael B.; Finkelstein, Joseph; Pronovost, Peter J.; Durkin, Nowella; Brotman, Daniel J.

2014-01-01

Background Despite safe and cost-effective venous thromboembolism (VTE) prevention measures, VTE prophylaxis rates are often suboptimal. Healthcare reform efforts emphasize transparency through programs to report performance, and payment incentives through programs to pay-for-performance. Objective To sequentially examine an individualized physician dashboard and pay-for-performance program to improve VTE prophylaxis rates amongst hospitalists. Design Retrospective analysis of 3144 inpatient admissions. After a baseline observation period, VTE prophylaxis compliance was compared during both interventions. Setting 1060-bed tertiary care medical center. Participants 38 part- and full-time academic hospitalists. Interventions A Web-based hospitalist dashboard provided VTE prophylaxis feedback. After 6 months of feedback only, a pay-for-performance program was incorporated, with graduated payouts for compliance rates of 80-100%. Measurements Prescription of American College of Chest Physicians guideline-compliant VTE prophylaxis and subsequent pay-for-performance payments. Results Monthly VTE prophylaxis compliance rates were 86% (95% CI: 85, 88), 90% (95% CI: 88, 93), and 94% (95% CI: 93, 96) during the baseline, dashboard, and combined dashboard/pay-for-performance periods, respectively. Compliance significantly improved with the use of the dashboard (p=0.01) and addition of the pay-for-performance program (p=0.01). The highest rate of improvement occurred with the dashboard (1.58%/month; p=0.01). Annual individual physician performance payments ranged from $53 to $1244 (mean $633; SD ±350). Conclusions Direct feedback using dashboards was associated with significantly improved compliance, with further improvement after incorporating an individual physician pay-for-performance program. Real-time dashboards and physician-level incentives may assist hospitals in achieving higher safety and quality benchmarks. PMID:25545690

Use of provider-level dashboards and pay-for-performance in venous thromboembolism prophylaxis.

PubMed

Michtalik, Henry J; Carolan, Howard T; Haut, Elliott R; Lau, Brandyn D; Streiff, Michael B; Finkelstein, Joseph; Pronovost, Peter J; Durkin, Nowella; Brotman, Daniel J

2015-03-01

Despite safe and cost-effective venous thromboembolism (VTE) prevention measures, VTE prophylaxis rates are often suboptimal. Healthcare reform efforts emphasize transparency through programs to report performance and payment incentives through pay-for-performance programs. To sequentially examine an individualized physician dashboard and pay-for-performance program to improve VTE prophylaxis rates among hospitalists. Retrospective analysis of 3144 inpatient admissions. After a baseline observation period, VTE prophylaxis compliance was compared during both interventions. A 1060-bed tertiary care medical center. Thirty-eight part-time and full-time academic hospitalists. A Web-based hospitalist dashboard provided VTE prophylaxis feedback. After 6 months of feedback only, a pay-for-performance program was incorporated, with graduated payouts for compliance rates of 80% to 100%. Prescription of American College of Chest Physicians' guideline-compliant VTE prophylaxis and subsequent pay-for-performance payments. Monthly VTE prophylaxis compliance rates were 86% (95% confidence interval [CI]: 85-88), 90% (95% CI: 88-93), and 94% (95% CI: 93-96) during the baseline, dashboard, and combined dashboard/pay-for-performance periods, respectively. Compliance significantly improved with the use of the dashboard (P = 0.01) and addition of the pay-for-performance program (P = 0.01). The highest rate of improvement occurred with the dashboard (1.58%/month; P = 0.01). Annual individual physician performance payments ranged from $53 to $1244 (mean $633; standard deviation ±$350). Direct feedback using dashboards was associated with significantly improved compliance, with further improvement after incorporating an individual physician pay-for-performance program. Real-time dashboards and physician-level incentives may assist hospitals in achieving higher safety and quality benchmarks. © 2014 Society of Hospital Medicine.

Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature

PubMed Central

Greenville, Aaron C; Wardle, Glenda M; Dickman, Chris R

2012-01-01

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in extremes of temperature and rainfall compare to trends in the mean values of these variables and over what spatial scales the patterns are consistent. Using the longest historical weather records available for central Australia – 100 years – and quantile regression methods, we investigate if extreme climate events have changed at similar rates to median events, if annual rainfall has increased in variability, and if the frequency of large rainfall events has increased over this period. Specifically, we compared local (individual weather stations) and regional (Simpson Desert) spatial scales, and quantified trends in median (50th quantile) and extreme weather values (5th, 10th, 90th, and 95th quantiles). We found that median and extreme annual minimum and maximum temperatures have increased at both spatial scales over the past century. Rainfall changes have been inconsistent across the Simpson Desert; individual weather stations showed increases in annual rainfall, increased frequency of large rainfall events or more prolonged droughts, depending on the location. In contrast to our prediction, we found no evidence that intra-annual rainfall had become more variable over time. Using long-term live-trapping records (22 years) of desert small mammals as a case study, we demonstrate that irruptive events are driven by extreme rainfalls (>95th quantile) and that increases in the magnitude and frequency of extreme rainfall events are likely to drive changes in the populations of these species through direct and indirect changes in predation pressure and wildfires. PMID:23170202

Three nested randomized controlled trials of peer-only or multiple stakeholder group feedback within Delphi surveys during core outcome and information set development.

PubMed

Brookes, Sara T; Macefield, Rhiannon C; Williamson, Paula R; McNair, Angus G; Potter, Shelley; Blencowe, Natalie S; Strong, Sean; Blazeby, Jane M

2016-08-17

Methods for developing a core outcome or information set require involvement of key stakeholders to prioritise many items and achieve agreement as to the core set. The Delphi technique requires participants to rate the importance of items in sequential questionnaires (or rounds) with feedback provided in each subsequent round such that participants are able to consider the views of others. This study examines the impact of receiving feedback from different stakeholder groups, on the subsequent rating of items and the level of agreement between stakeholders. Randomized controlled trials were nested within the development of three core sets each including a Delphi process with two rounds of questionnaires, completed by patients and health professionals. Participants rated items from 1 (not essential) to 9 (absolutely essential). For round 2, participants were randomized to receive feedback from their peer stakeholder group only (peer) or both stakeholder groups separately (multiple). Decisions as to which items to retain following each round were determined by pre-specified criteria. Whilst type of feedback did not impact on the percentage of items for which a participant subsequently changed their rating, or the magnitude of change, it did impact on items retained at the end of round 2. Each core set contained discordant items retained by one feedback group but not the other (3-22 % discordant items). Consensus between patients and professionals in items to retain was greater amongst those receiving multiple group feedback in each core set (65-82 % agreement for peer-only feedback versus 74-94 % for multiple feedback). In addition, differences in round 2 scores were smaller between stakeholder groups receiving multiple feedback than between those receiving peer group feedback only. Variability in item scores across stakeholders was reduced following any feedback but this reduction was consistently greater amongst the multiple feedback group. In the development of a core outcome or information set, providing feedback within Delphi questionnaires from all stakeholder groups separately may influence the final core set and improve consensus between the groups. Further work is needed to better understand how participants rate and re-rate items within a Delphi process. The three randomized controlled trials reported here were each nested within the development of a core information or outcome set to investigate processes in core outcome and information set development. Outcomes were not health-related and therefore trial registration was not applicable.

Differences in BMI z-Scores between Offspring of Smoking and Nonsmoking Mothers: A Longitudinal Study of German Children from Birth through 14 Years of Age

PubMed Central

Fenske, Nora; Müller, Manfred J.; Plachta-Danielzik, Sandra; Keil, Thomas; Grabenhenrich, Linus; von Kries, Rüdiger

2014-01-01

Background: Children of mothers who smoked during pregnancy have a lower birth weight but have a higher chance to become overweight during childhood. Objectives: We followed children longitudinally to assess the age when higher body mass index (BMI) z-scores became evident in the children of mothers who smoked during pregnancy, and to evaluate the trajectory of changes until adolescence. Methods: We pooled data from two German cohort studies that included repeated anthropometric measurements until 14 years of age and information on smoking during pregnancy and other risk factors for overweight. We used longitudinal quantile regression to estimate age- and sex-specific associations between maternal smoking and the 10th, 25th, 50th, 75th, and 90th quantiles of the BMI z-score distribution in study participants from birth through 14 years of age, adjusted for potential confounders. We used additive mixed models to estimate associations with mean BMI z-scores. Results: Mean and median (50th quantile) BMI z-scores at birth were smaller in the children of mothers who smoked during pregnancy compared with children of nonsmoking mothers, but BMI z-scores were significantly associated with maternal smoking beginning at the age of 4–5 years, and differences increased over time. For example, the difference in the median BMI z-score between the daughters of smokers versus nonsmokers was 0.12 (95% CI: 0.01, 0.21) at 5 years, and 0.30 (95% CI: 0.08, 0.39) at 14 years of age. For lower BMI z-score quantiles, the association with smoking was more pronounced in girls, whereas in boys the association was more pronounced for higher BMI z-score quantiles. Conclusions: A clear difference in BMI z-score (mean and median) between children of smoking and nonsmoking mothers emerged at 4–5 years of age. The shape and size of age-specific effect estimates for maternal smoking during pregnancy varied by age and sex across the BMI z-score distribution. Citation: Riedel C, Fenske N, Müller MJ, Plachta-Danielzik S, Keil T, Grabenhenrich L, von Kries R. 2014. Differences in BMI z-scores between offspring of smoking and nonsmoking mothers: a longitudinal study of German children from birth through 14 years of age. Environ Health Perspect 122:761–767; http://dx.doi.org/10.1289/ehp.1307139 PMID:24695368

Choice in experiential learning: True preferences or experimental artifacts?

PubMed

Ashby, Nathaniel J S; Konstantinidis, Emmanouil; Yechiam, Eldad

2017-03-01

The rate of selecting different options in the decisions-from-feedback paradigm is commonly used to measure preferences resulting from experiential learning. While convergence to a single option increases with experience, some variance in choice remains even when options are static and offer fixed rewards. Employing a decisions-from-feedback paradigm followed by a policy-setting task, we examined whether the observed variance in choice is driven by factors related to the paradigm itself: Continued exploration (e.g., believing options are non-stationary) or exploitation of perceived outcome patterns (i.e., a belief that sequential choices are not independent). Across two studies, participants showed variance in their choices, which was related (i.e., proportional) to the policies they set. In addition, in Study 2, participants' reported under-confidence was associated with the amount of choice variance in later choices and policies. These results suggest that variance in choice is better explained by participants lacking confidence in knowing which option is better, rather than methodological artifacts (i.e., exploration or failures to recognize outcome independence). As such, the current studies provide evidence for the decisions-from-feedback paradigm's validity as a behavioral research method for assessing learned preferences. Copyright © 2017 Elsevier B.V. All rights reserved.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps

NASA Astrophysics Data System (ADS)

Xu, Xiwei; Zhang, Changhai

2013-12-01

Now some sequential patterns mining algorithms generate too many candidate sequences, and increase the processing cost of support counting. Therefore, we present an effective and scalable algorithm called SPMBR (Sequential Patterns Mining based on Bitmap Representation) to solve the problem of mining the sequential patterns for large databases. Our method differs from previous related works of mining sequential patterns. The main difference is that the database of sequential patterns is represented by bitmaps, and a simplified bitmap structure is presented firstly. In this paper, First the algorithm generate candidate sequences by SE(Sequence Extension) and IE(Item Extension), and then obtain all frequent sequences by comparing the original bitmap and the extended item bitmap .This method could simplify the problem of mining the sequential patterns and avoid the high processing cost of support counting. Both theories and experiments indicate that the performance of SPMBR is predominant for large transaction databases, the required memory size for storing temporal data is much less during mining process, and all sequential patterns can be mined with feasibility.

Mode-of-action evaluation for the effect of trans fatty acids on low-density lipoprotein cholesterol.

PubMed

Reichard, John F; Haber, Lynne T

2016-12-01

The purpose of this work is to systematically consider the data relating to the mode of action (MOA) for the effects of industrially produced trans fatty acid (iTFA) on plasma low-density lipoprotein (LDL) levels. The hypothesized MOA is composed of two key events: increased LDL production and decreased LDL clearance. A substantial database supports this MOA, although the key events are likely to be interdependent, rather than sequential. Both key events are functions of nonlinear biological processes including rate-limited clearance, receptor-mediated transcription, and both positive and negative feedback regulation. Each key event was evaluated based on weight-of-evidence analysis and for human relevance. We conclude that the data are inadequate for a detailed dose-response analysis in the context of the evolved Bradford Hill considerations; however, the weight of evidence is strong and the overall shape of the dose-response curves for markers of the key events and the key determinants of those relationships is well understood in many cases and is nonlinear. Feedback controls are responsible for maintaining homeostasis of cholesterol and triglyceride levels and underlie both of the key events, resulting in a less-than-linear or thresholded relationship between TFA and LDL-C. The inconsistencies and gaps in the database are discussed. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Smooth conditional distribution function and quantiles under random censorship.

PubMed

Leconte, Eve; Poiraud-Casanova, Sandrine; Thomas-Agnan, Christine

2002-09-01

We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional alpha-quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous. We propose and discuss two classes of estimators which are smooth with respect to the response variable as well as to the covariate. Some simulations demonstrate that the new methods have better mean square error performances than the generalized Kaplan-Meier estimator introduced by Beran (1981) and considered in the literature by Dabrowska (1989, 1992) and Gonzalez-Manteiga and Cadarso-Suarez (1994).

Modelling the behaviour of unemployment rates in the US over time and across space

NASA Astrophysics Data System (ADS)

Holmes, Mark J.; Otero, Jesús; Panagiotidis, Theodore

2013-11-01

This paper provides evidence that unemployment rates across US states are stationary and therefore behave according to the natural rate hypothesis. We provide new insights by considering the effect of key variables on the speed of adjustment associated with unemployment shocks. A highly-dimensional VAR analysis of the half-lives associated with shocks to unemployment rates in pairs of states suggests that the distance between states and vacancy rates respectively exert a positive and negative influence. We find that higher homeownership rates do not lead to higher half-lives. When the symmetry assumption is relaxed through quantile regression, support for the Oswald hypothesis through a positive relationship between homeownership rates and half-lives is found at the higher quantiles.

A comparison of three approaches to non-stationary flood frequency analysis

NASA Astrophysics Data System (ADS)

Debele, S. E.; Strupczewski, W. G.; Bogdanowicz, E.

2017-08-01

Non-stationary flood frequency analysis (FFA) is applied to statistical analysis of seasonal flow maxima from Polish and Norwegian catchments. Three non-stationary estimation methods, namely, maximum likelihood (ML), two stage (WLS/TS) and GAMLSS (generalized additive model for location, scale and shape parameters), are compared in the context of capturing the effect of non-stationarity on the estimation of time-dependent moments and design quantiles. The use of a multimodel approach is recommended, to reduce the errors due to the model misspecification in the magnitude of quantiles. The results of calculations based on observed seasonal daily flow maxima and computer simulation experiments showed that GAMLSS gave the best results with respect to the relative bias and root mean square error in the estimates of trend in the standard deviation and the constant shape parameter, while WLS/TS provided better accuracy in the estimates of trend in the mean value. Within three compared methods the WLS/TS method is recommended to deal with non-stationarity in short time series. Some practical aspects of the GAMLSS package application are also presented. The detailed discussion of general issues related to consequences of climate change in the FFA is presented in the second part of the article entitled "Around and about an application of the GAMLSS package in non-stationary flood frequency analysis".

Improving medium-range ensemble streamflow forecasts through statistical post-processing

NASA Astrophysics Data System (ADS)

Mendoza, Pablo; Wood, Andy; Clark, Elizabeth; Nijssen, Bart; Clark, Martyn; Ramos, Maria-Helena; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Probabilistic hydrologic forecasts are a powerful source of information for decision-making in water resources operations. A common approach is the hydrologic model-based generation of streamflow forecast ensembles, which can be implemented to account for different sources of uncertainties - e.g., from initial hydrologic conditions (IHCs), weather forecasts, and hydrologic model structure and parameters. In practice, hydrologic ensemble forecasts typically have biases and spread errors stemming from errors in the aforementioned elements, resulting in a degradation of probabilistic properties. In this work, we compare several statistical post-processing techniques applied to medium-range ensemble streamflow forecasts obtained with the System for Hydromet Applications, Research and Prediction (SHARP). SHARP is a fully automated prediction system for the assessment and demonstration of short-term to seasonal streamflow forecasting applications, developed by the National Center for Atmospheric Research, University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. The suite of post-processing techniques includes linear blending, quantile mapping, extended logistic regression, quantile regression, ensemble analogs, and the generalized linear model post-processor (GLMPP). We assess and compare these techniques using multi-year hindcasts in several river basins in the western US. This presentation discusses preliminary findings about the effectiveness of the techniques for improving probabilistic skill, reliability, discrimination, sharpness and resolution.

Assessment tools for urban catchments: developing biological indicators based on benthic macroinvertebrates

USGS Publications Warehouse

Purcell, A.H.; Bressler, D.W.; Paul, M.J.; Barbour, M.T.; Rankin, E.T.; Carter, J.L.; Resh, V.H.

2009-01-01

Biological indicators, particularly benthic macroinvertebrates, are widely used and effective measures of the impact of urbanization on stream ecosystems. A multimetric biological index of urbanization was developed using a large benthic macroinvertebrate dataset (n = 1,835) from the Baltimore, Maryland, metropolitan area and then validated with datasets from Cleveland, Ohio (n = 79); San Jose, California (n = 85); and a different subset of the Baltimore data (n = 85). The biological metrics used to develop the multimetric index were selected using several criteria and were required to represent ecological attributes of macroinvertebrate assemblages including taxonomic composition and richness (number of taxa in the insect orders of Ephemeroptera, Plecoptera, and Trichoptera), functional feeding group (number of taxa designated as filterers), and habit (percent of individuals which cling to the substrate). Quantile regression was used to select metrics and characterize the relationship between the final biological index and an urban gradient (composed of population density, road density, and urban land use). Although more complex biological indices exist, this simplified multimetric index showed a consistent relationship between biological indicators and urban conditions (as measured by quantile regression) in three climatic regions of the United States and can serve as an assessment tool for environmental managers to prioritize urban stream sites for restoration and protection.

Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables in the North Central USA

NASA Astrophysics Data System (ADS)

Hoss, F.; Fischbeck, P. S.

2014-10-01

This study further develops the method of quantile regression (QR) to predict exceedance probabilities of flood stages by post-processing forecasts. Using data from the 82 river gages, for which the National Weather Service's North Central River Forecast Center issues forecasts daily, this is the first QR application to US American river gages. Archived forecasts for lead times up to six days from 2001-2013 were analyzed. Earlier implementations of QR used the forecast itself as the only independent variable (Weerts et al., 2011; López López et al., 2014). This study adds the rise rate of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago to the QR model. Including those four variables significantly improved the forecasts, as measured by the Brier Skill Score (BSS). Mainly, the resolution increases, as the original QR implementation already delivered high reliability. Combining the forecast with the other four variables results in much less favorable BSSs. Lastly, the forecast performance does not depend on the size of the training dataset, but on the year, the river gage, lead time and event threshold that are being forecast. We find that each event threshold requires a separate model configuration or at least calibration.

Sequential actin-based pushing forces drive meiosis I chromosome migration and symmetry breaking in oocytes.

PubMed

Yi, Kexi; Rubinstein, Boris; Unruh, Jay R; Guo, Fengli; Slaughter, Brian D; Li, Rong

2013-03-04

Polar body extrusion during oocyte maturation is critically dependent on asymmetric positioning of the meiotic spindle, which is established through migration of the meiosis I (MI) spindle/chromosomes from the oocyte interior to a subcortical location. In this study, we show that MI chromosome migration is biphasic and driven by consecutive actin-based pushing forces regulated by two actin nucleators, Fmn2, a formin family protein, and the Arp2/3 complex. Fmn2 was recruited to endoplasmic reticulum structures surrounding the MI spindle, where it nucleated actin filaments to initiate an initially slow and poorly directed motion of the spindle away from the cell center. A fast and highly directed second migration phase was driven by actin-mediated cytoplasmic streaming and occurred as the chromosomes reach a sufficient proximity to the cortex to activate the Arp2/3 complex. We propose that decisive symmetry breaking in mouse oocytes results from Fmn2-mediated perturbation of spindle position and the positive feedback loop between chromosome signal-induced Arp2/3 activation and Arp2/3-orchestrated cytoplasmic streaming that transports the chromosomes.

Sequential actin-based pushing forces drive meiosis I chromosome migration and symmetry breaking in oocytes

PubMed Central

Yi, Kexi; Rubinstein, Boris; Unruh, Jay R.; Guo, Fengli; Slaughter, Brian D.

2013-01-01

Polar body extrusion during oocyte maturation is critically dependent on asymmetric positioning of the meiotic spindle, which is established through migration of the meiosis I (MI) spindle/chromosomes from the oocyte interior to a subcortical location. In this study, we show that MI chromosome migration is biphasic and driven by consecutive actin-based pushing forces regulated by two actin nucleators, Fmn2, a formin family protein, and the Arp2/3 complex. Fmn2 was recruited to endoplasmic reticulum structures surrounding the MI spindle, where it nucleated actin filaments to initiate an initially slow and poorly directed motion of the spindle away from the cell center. A fast and highly directed second migration phase was driven by actin-mediated cytoplasmic streaming and occurred as the chromosomes reach a sufficient proximity to the cortex to activate the Arp2/3 complex. We propose that decisive symmetry breaking in mouse oocytes results from Fmn2-mediated perturbation of spindle position and the positive feedback loop between chromosome signal-induced Arp2/3 activation and Arp2/3-orchestrated cytoplasmic streaming that transports the chromosomes. PMID:23439682

Modelling probabilities of heavy precipitation by regional approaches

NASA Astrophysics Data System (ADS)

Gaal, L.; Kysely, J.

2009-09-01

Extreme precipitation events are associated with large negative consequences for human society, mainly as they may trigger floods and landslides. The recent series of flash floods in central Europe (affecting several isolated areas) on June 24-28, 2009, the worst one over several decades in the Czech Republic as to the number of persons killed and the extent of damage to buildings and infrastructure, is an example. Estimates of growth curves and design values (corresponding e.g. to 50-yr and 100-yr return periods) of precipitation amounts, together with their uncertainty, are important in hydrological modelling and other applications. The interest in high quantiles of precipitation distributions is also related to possible climate change effects, as climate model simulations tend to project increased severity of precipitation extremes in a warmer climate. The present study compares - in terms of Monte Carlo simulation experiments - several methods to modelling probabilities of precipitation extremes that make use of ‘regional approaches’: the estimation of distributions of extremes takes into account data in a ‘region’ (‘pooling group’), in which one may assume that the distributions at individual sites are identical apart from a site-specific scaling factor (the condition is referred to as ‘regional homogeneity’). In other words, all data in a region - often weighted in some way - are taken into account when estimating the probability distribution of extremes at a given site. The advantage is that sampling variations in the estimates of model parameters and high quantiles are to a large extent reduced compared to the single-site analysis. We focus on the ‘region-of-influence’ (ROI) method which is based on the identification of unique pooling groups (forming the database for the estimation) for each site under study. The similarity of sites is evaluated in terms of a set of site attributes related to the distributions of extremes. The issue of the size of the region is linked with a built-in test on regional homogeneity of data. Once a pooling group is delineated, weights based on a dissimilarity measure are assigned to individual sites involved in a pooling group, and all (weighted) data are employed in the estimation of model parameters and high quantiles at a given location. The ROI method is compared with the Hosking-Wallis (HW) regional frequency analysis, which is based on delineating fixed regions (instead of flexible pooling groups) and assigning unit weights to all sites in a region. The comparison of the performance of the individual regional models makes use of data on annual maxima of 1-day precipitation amounts at 209 stations covering the Czech Republic, with altitudes ranging from 150 to 1490 m a.s.l. We conclude that the ROI methodology is superior to the HW analysis, particularly for very high quantiles (100-yr return values). Another advantage of the ROI approach is that subjective decisions - unavoidable when fixed regions in the HW analysis are formed - may efficiently be suppressed, and almost all settings of the ROI method may be justified by results of the simulation experiments. The differences between (any) regional method and single-site analysis are very pronounced and suggest that the at-site estimation is highly unreliable. The ROI method is then applied to estimate high quantiles of precipitation amounts at individual sites. The estimates and their uncertainty are compared with those from a single-site analysis. We focus on the eastern part of the Czech Republic, i.e. an area with complex orography and a particularly pronounced role of Mediterranean cyclones in producing precipitation extremes. The design values are compared with precipitation amounts recorded during the recent heavy precipitation events, including the one associated with the flash flood on June 24, 2009. We also show that the ROI methodology may easily be transferred to the analysis of precipitation extremes in climate model outputs. It efficiently reduces (random) variations in the estimates of parameters of the extreme value distributions in individual gridboxes that result from large spatial variability of heavy precipitation, and represents a straightforward tool for ‘weighting’ data from neighbouring gridboxes within the estimation procedure. The study is supported by the Grant Agency of AS CR under project B300420801.

Sequential social experiences interact to modulate aggression but not brain gene expression in the honey bee (Apis mellifera).

PubMed

Rittschof, Clare C

2017-01-01

In highly structured societies, individuals behave flexibly and cooperatively in order to achieve a particular group-level outcome. However, even in social species, environmental inputs can have long lasting effects on individual behavior, and variable experiences can even result in consistent individual differences and constrained behavioral flexibility. Despite the fact that such constraints on behavior could have implications for behavioral optimization at the social group level, few studies have explored how social experiences accumulate over time, and the mechanistic basis of these effects. In the current study, I evaluate how sequential social experiences affect individual and group level aggressive phenotypes, and individual brain gene expression, in the highly social honey bee ( Apis mellifera ). To do this, I combine a whole colony chronic predator disturbance treatment with a lab-based manipulation of social group composition. Compared to the undisturbed control, chronically disturbed individuals show lower aggression levels overall, but also enhanced behavioral flexibility in the second, lab-based social context. Disturbed bees display aggression levels that decline with increasing numbers of more aggressive, undisturbed group members. However, group level aggressive phenotypes are similar regardless of the behavioral tendencies of the individuals that make up the group, suggesting a combination of underlying behavioral tendency and negative social feedback influences the aggressive behaviors displayed, particularly in the case of disturbed individuals. An analysis of brain gene expression showed that aggression related biomarker genes reflect an individual's disturbance history, but not subsequent social group experience or behavioral outcomes. In highly social animals with collective behavioral phenotypes, social context may mask underlying variation in individual behavioral tendencies. Moreover, gene expression patterns may reflect behavioral tendency, while behavioral outcomes are further regulated by social cues perceived in real-time.

Constant speed control of four-stroke micro internal combustion swing engine

NASA Astrophysics Data System (ADS)

Gao, Dedong; Lei, Yong; Zhu, Honghai; Ni, Jun

2015-09-01

The increasing demands on safety, emission and fuel consumption require more accurate control models of micro internal combustion swing engine (MICSE). The objective of this paper is to investigate the constant speed control models of four-stroke MICSE. The operation principle of the four-stroke MICSE is presented based on the description of MICSE prototype. A two-level Petri net based hybrid model is proposed to model the four-stroke MICSE engine cycle. The Petri net subsystem at the upper level controls and synchronizes the four Petri net subsystems at the lower level. The continuous sub-models, including breathing dynamics of intake manifold, thermodynamics of the chamber and dynamics of the torque generation, are investigated and integrated with the discrete model in MATLAB Simulink. Through the comparison of experimental data and simulated DC voltage output, it is demonstrated that the hybrid model is valid for the four-stroke MICSE system. A nonlinear model is obtained from the cycle average data via the regression method, and it is linearized around a given nominal equilibrium point for the controller design. The feedback controller of the spark timing and valve duration timing is designed with a sequential loop closing design approach. The simulation of the sequential loop closure control design applied to the hybrid model is implemented in MATLAB. The simulation results show that the system is able to reach its desired operating point within 0.2 s, and the designed controller shows good MICSE engine performance with a constant speed. This paper presents the constant speed control models of four-stroke MICSE and carries out the simulation tests, the models and the simulation results can be used for further study on the precision control of four-stroke MICSE.

A Psychological Model for Aggregating Judgments of Magnitude

NASA Astrophysics Data System (ADS)

Merkle, Edgar C.; Steyvers, Mark

In this paper, we develop and illustrate a psychologically-motivated model for aggregating judgments of magnitude across experts. The model assumes that experts' judgments are perturbed from the truth by both systematic biases and random error, and it provides aggregated estimates that are implicitly based on the application of nonlinear weights to individual judgments. The model is also easily extended to situations where experts report multiple quantile judgments. We apply the model to expert judgments concerning flange leaks in a chemical plant, illustrating its use and comparing it to baseline measures.

Oscillatory magnetic tweezers based on ferromagnetic beads and simple coaxial coils

NASA Astrophysics Data System (ADS)

Trepat, Xavier; Grabulosa, Mireia; Buscemi, Lara; Rico, Fèlix; Fabry, Ben; Fredberg, Jeffrey J.; Farré, Ramon

2003-09-01

We report the design and validation of simple magnetic tweezers for oscillating ferromagnetic beads in the piconewton and nanometer scales. The system is based on a single pair of coaxial coils operating in two sequential modes: permanent magnetization of the beads through a large and brief pulse of magnetic field and generation of magnetic gradients to produce uniaxial oscillatory forces. By using this two step method, the magnetic moment of the beads remains constant during measurements. Therefore, the applied force can be computed and varies linearly with the driving signal. No feedback control is required to produce well defined force oscillations over a wide bandwidth. The design of the coils was optimized to obtain high magnetic fields (280 mT) and gradients (2 T/m) with high homogeneity (5% variation) within the sample. The magnetic tweezers were implemented in an inverted optical microscope with a videomicroscopy-based multiparticle tracking system. The apparatus was validated with 4.5 μm magnetite beads obtaining forces up to ˜2 pN and subnanometer resolution. The applicability of the device includes microrheology of biopolymer and cell cytoplasm, molecular mechanics, and mechanotransduction in living cells.

The association of fatigue, pain, depression and anxiety with work and activity impairment in immune mediated inflammatory diseases.

PubMed

Enns, Murray W; Bernstein, Charles N; Kroeker, Kristine; Graff, Lesley; Walker, John R; Lix, Lisa M; Hitchon, Carol A; El-Gabalawy, Renée; Fisk, John D; Marrie, Ruth Ann

2018-01-01

Impairment in work function is a frequent outcome in patients with chronic conditions such as immune-mediated inflammatory diseases (IMID), depression and anxiety disorders. The personal and economic costs of work impairment in these disorders are immense. Symptoms of pain, fatigue, depression and anxiety are potentially remediable forms of distress that may contribute to work impairment in chronic health conditions such as IMID. The present study evaluated the association between pain [Medical Outcomes Study Pain Effects Scale], fatigue [Daily Fatigue Impact Scale], depression and anxiety [Hospital Anxiety and Depression Scale] and work impairment [Work Productivity and Activity Impairment Scale] in four patient populations: multiple sclerosis (n = 255), inflammatory bowel disease (n = 248, rheumatoid arthritis (n = 154) and a depression and anxiety group (n = 307), using quantile regression, controlling for the effects of sociodemographic factors, physical disability, and cognitive deficits. Each of pain, depression symptoms, anxiety symptoms, and fatigue individually showed significant associations with work absenteeism, presenteeism, and general activity impairment (quantile regression standardized estimates ranging from 0.3 to 1.0). When the distress variables were entered concurrently into the regression models, fatigue was a significant predictor of work and activity impairment in all models (quantile regression standardized estimates ranging from 0.2 to 0.5). These findings have important clinical implications for understanding the determinants of work impairment and for improving work-related outcomes in chronic disease.

How important are determinants of obesity measured at the individual level for explaining geographic variation in body mass index distributions? Observational evidence from Canada using Quantile Regression and Blinder-Oaxaca Decomposition.

PubMed

Dutton, Daniel J; McLaren, Lindsay

2016-04-01

Obesity prevalence varies between geographic regions in Canada. The reasons for this variation are unclear but most likely implicate both individual-level and population-level factors. The objective of this study was to examine whether equalising correlates of body mass index (BMI) across these geographic regions could be reasonably expected to reduce differences in BMI distributions between regions. Using data from three cycles of the Canadian Community Health Survey (CCHS) 2001, 2003 and 2007 for males and females, we modelled between-region BMI cross-sectionally using quantile regression and Blinder-Oaxaca decomposition of the quantile regression results. We show that while individual-level variables (ie, age, income, education, physical activity level, fruit and vegetable consumption, smoking status, drinking status, family doctor status, rural status, employment in the past 12 months and marital status) may be Caucasian important correlates of BMI within geographic regions, those variables are not capable of explaining variation in BMI between regions. Equalisation of common correlates of BMI between regions cannot be reasonably expected to reduce differences in the BMI distributions between regions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Forecasting peak asthma admissions in London: an application of quantile regression models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Forecasting peak asthma admissions in London: an application of quantile regression models

NASA Astrophysics Data System (ADS)

Soyiri, Ireneous N.; Reidpath, Daniel D.; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Regional estimation of extreme suspended sediment concentrations using watershed characteristics

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Ouarda, Taha B. M. J.; St-Hilaire, André; Poulin, Jimmy

2010-01-01

SummaryThe number of stations monitoring daily suspended sediment concentration (SSC) has been decreasing since the 1980s in North America while suspended sediment is considered as a key variable for water quality. The objective of this study is to test the feasibility of regionalising extreme SSC, i.e. estimating SSC extremes values for ungauged basins. Annual maximum SSC for 72 rivers in Canada and USA were modelled with probability distributions in order to estimate quantiles corresponding to different return periods. Regionalisation techniques, originally developed for flood prediction in ungauged basins, were tested using the climatic, topographic, land cover and soils attributes of the watersheds. Two approaches were compared, using either physiographic characteristics or seasonality of extreme SSC to delineate the regions. Multiple regression models to estimate SSC quantiles as a function of watershed characteristics were built in each region, and compared to a global model including all sites. Regional estimates of SSC quantiles were compared with the local values. Results show that regional estimation of extreme SSC is more efficient than a global regression model including all sites. Groups/regions of stations have been identified, using either the watershed characteristics or the seasonality of occurrence for extreme SSC values providing a method to better describe the extreme events of SSC. The most important variables for predicting extreme SSC are the percentage of clay in the soils, precipitation intensity and forest cover.

The heterogeneous effects of urbanization and income inequality on CO2 emissions in BRICS economies: evidence from panel quantile regression.

PubMed

Zhu, Huiming; Xia, Hang; Guo, Yawei; Peng, Cheng

2018-04-12

This paper empirically examines the effects of urbanization and income inequality on CO 2 emissions in the BRICS economies (i.e., Brazil, Russia, India, China, and South Africa) during the periods 1994-2013. The method we used is the panel quantile regression, which takes into account the unobserved individual heterogeneity and distributional heterogeneity. Our empirical results indicate that urbanization has a significant and negative impact on carbon emissions, except in the 80 th , 90 th , and 95 th quantiles. We also quantitatively investigate the direct and indirect effect of urbanization on carbon emissions, and the results show that we may underestimate urbanization's effect on carbon emissions if we ignore its indirect effect. In addition, in middle- and high-emission countries, income inequality has a significant and positive impact on carbon emissions. The results of our study indicate that in the BRICS economies, there is an inverted U-shaped environmental Kuznets curve (EKC) between the GDP per capita and carbon emissions. The conclusions of this study have important policy implications for policymakers. Policymakers should try to narrow the income gap between the rich and the poor to improve environmental quality; the BRICS economies can speed up urbanization to reduce carbon emissions, but they must improve energy efficiency and use clean energy to the greatest extent in the process.

Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

NASA Astrophysics Data System (ADS)

Lombardo, Luigi; Saia, Sergio; Schillaci, Calogero; Mai, P. Martin; Huser, Raphaël

2018-05-01

Soil Organic Carbon (SOC) estimation is crucial to manage both natural and anthropic ecosystems and has recently been put under the magnifying glass after the Paris agreement 2016 due to its relationship with greenhouse gas. Statistical applications have dominated the SOC stock mapping at regional scale so far. However, the community has hardly ever attempted to implement Quantile Regression (QR) to spatially predict the SOC distribution. In this contribution, we test QR to estimate SOC stock (0-30 $cm$ depth) in the agricultural areas of a highly variable semi-arid region (Sicily, Italy, around 25,000 $km2$) by using topographic and remotely sensed predictors. We also compare the results with those from available SOC stock measurement. The QR models produced robust performances and allowed to recognize dominant effects among the predictors with respect to the considered quantile. This information, currently lacking, suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictors. In this work, the predictive map generated at the median shows lower errors than those of the Joint Research Centre and International Soil Reference, and Information Centre benchmarks. The results suggest the use of QR as a comprehensive and effective method to map SOC using legacy data in agro-ecosystems. The R code scripted in this study for QR is included.

The Balance-Scale Task Revisited: A Comparison of Statistical Models for Rule-Based and Information-Integration Theories of Proportional Reasoning

PubMed Central

Hofman, Abe D.; Visser, Ingmar; Jansen, Brenda R. J.; van der Maas, Han L. J.

2015-01-01

We propose and test three statistical models for the analysis of children’s responses to the balance scale task, a seminal task to study proportional reasoning. We use a latent class modelling approach to formulate a rule-based latent class model (RB LCM) following from a rule-based perspective on proportional reasoning and a new statistical model, the Weighted Sum Model, following from an information-integration approach. Moreover, a hybrid LCM using item covariates is proposed, combining aspects of both a rule-based and information-integration perspective. These models are applied to two different datasets, a standard paper-and-pencil test dataset (N = 779), and a dataset collected within an online learning environment that included direct feedback, time-pressure, and a reward system (N = 808). For the paper-and-pencil dataset the RB LCM resulted in the best fit, whereas for the online dataset the hybrid LCM provided the best fit. The standard paper-and-pencil dataset yielded more evidence for distinct solution rules than the online data set in which quantitative item characteristics are more prominent in determining responses. These results shed new light on the discussion on sequential rule-based and information-integration perspectives of cognitive development. PMID:26505905

Improving the Acquisition of Basic Technical Surgical Skills with VR-Based Simulation Coupled with Computer-Based Video Instruction.

PubMed

Rojas, David; Kapralos, Bill; Dubrowski, Adam

2016-01-01

Next to practice, feedback is the most important variable in skill acquisition. Feedback can vary in content and the way that it is used for delivery. Health professions education research has extensively examined the different effects provided by the different feedback methodologies. In this paper we compared two different types of knowledge of performance (KP) feedback. The first type was video-based KP feedback while the second type consisted of computer generated KP feedback. Results of this study showed that computer generated performance feedback is more effective than video based performance feedback. The combination of the two feedback methodologies provides trainees with a better understanding.

Age-, sex-, and education-specific norms for an extended CERAD Neuropsychological Assessment Battery-Results from the population-based LIFE-Adult-Study.

PubMed

Luck, Tobias; Pabst, Alexander; Rodriguez, Francisca S; Schroeter, Matthias L; Witte, Veronica; Hinz, Andreas; Mehnert, Anja; Engel, Christoph; Loeffler, Markus; Thiery, Joachim; Villringer, Arno; Riedel-Heller, Steffi G

2018-05-01

To provide new age-, sex-, and education-specific reference values for an extended version of the well-established Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery (CERAD-NAB) that additionally includes the Trail Making Test and the Verbal Fluency Test-S-Words. Norms were calculated based on the cognitive performances of n = 1,888 dementia-free participants (60-79 years) from the population-based German LIFE-Adult-Study. Multiple regressions were used to examine the association of the CERAD-NAB scores with age, sex, and education. In order to calculate the norms, quantile and censored quantile regression analyses were performed estimating marginal means of the test scores at 2.28, 6.68, 10, 15.87, 25, 50, 75, and 90 percentiles for age-, sex-, and education-specific subgroups. Multiple regression analyses revealed that younger age was significantly associated with better cognitive performance in 15 CERAD-NAB measures and higher education with better cognitive performance in all 17 measures. Women performed significantly better than men in 12 measures and men than women in four measures. The determined norms indicate ceiling effects for the cognitive performances in the Boston Naming, Word List Recognition, Constructional Praxis Copying, and Constructional Praxis Recall tests. The new norms for the extended CERAD-NAB will be useful for evaluating dementia-free German-speaking adults in a broad variety of relevant cognitive domains. The extended CERAD-NAB follows more closely the criteria for the new DSM-5 Mild and Major Neurocognitive Disorder. Additionally, it could be further developed to include a test for social cognition. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Ventricular stimulus site influences dynamic dispersion of repolarization in the intact human heart

PubMed Central

Orini, Michele; Simon, Ron B.; Providência, Rui; Khan, Fakhar Z.; Segal, Oliver R.; Babu, Girish G.; Bradley, Richard; Rowland, Edward; Ahsan, Syed; Chow, Anthony W.; Lowe, Martin D.; Taggart, Peter

2016-01-01

The spatial variation in restitution properties in relation to varying stimulus site is poorly defined. This study aimed to investigate the effect of varying stimulus site on apicobasal and transmural activation time (AT), action potential duration (APD) and repolarization time (RT) during restitution studies in the intact human heart. Ten patients with structurally normal hearts, undergoing clinical electrophysiology studies, were enrolled. Decapolar catheters were placed apex to base in the endocardial right ventricle (RVendo) and left ventricle (LVendo), and an LV branch of the coronary sinus (LVepi) for transmural recording. S1–S2 restitution protocols were performed pacing RVendo apex, LVendo base, and LVepi base. Overall, 725 restitution curves were analyzed, 74% of slopes had a maximum slope of activation recovery interval (ARI) restitution (Smax) > 1 (P < 0.001); mean Smax = 1.76. APD was shorter in the LVepi compared with LVendo, regardless of pacing site (30-ms difference during RVendo pacing, 25-ms during LVendo, and 48-ms during LVepi; 50th quantile, P < 0.01). Basal LVepi pacing resulted in a significant transmural gradient of RT (77 ms, 50th quantile: P < 0.01), due to loss of negative transmural AT-APD coupling (mean slope 0.63 ± 0.3). No significant transmural gradient in RT was demonstrated during endocardial RV or LV pacing, with preserved negative transmural AT-APD coupling (mean slope −1.36 ± 1.9 and −0.71 ± 0.4, respectively). Steep ARI restitution slopes predominate in the normal ventricle and dynamic ARI; RT gradients exist that are modulated by the site of activation. Epicardial stimulation to initiate ventricular activation promotes significant transmural gradients of repolarization that could be proarrhythmic. PMID:27371682

Uncertainty analysis of an inflow forecasting model: extension of the UNEEC machine learning-based method

NASA Astrophysics Data System (ADS)

Pianosi, Francesca; Lal Shrestha, Durga; Solomatine, Dimitri

2010-05-01

This research presents an extension of UNEEC (Uncertainty Estimation based on Local Errors and Clustering, Shrestha and Solomatine, 2006, 2008 & Solomatine and Shrestha, 2009) method in the direction of explicit inclusion of parameter uncertainty. UNEEC method assumes that there is an optimal model and the residuals of the model can be used to assess the uncertainty of the model prediction. It is assumed that all sources of uncertainty including input, parameter and model structure uncertainty are explicitly manifested in the model residuals. In this research, theses assumptions are relaxed, and the UNEEC method is extended to consider parameter uncertainty as well (abbreviated as UNEEC-P). In UNEEC-P, first we use Monte Carlo (MC) sampling in parameter space to generate N model realizations (each of which is a time series), estimate the prediction quantiles based on the empirical distribution functions of the model residuals considering all the residual realizations, and only then apply the standard UNEEC method that encapsulates the uncertainty of a hydrologic model (expressed by quantiles of the error distribution) in a machine learning model (e.g., ANN). UNEEC-P is applied first to a linear regression model of synthetic data, and then to a real case study of forecasting inflow to lake Lugano in northern Italy. The inflow forecasting model is a stochastic heteroscedastic model (Pianosi and Soncini-Sessa, 2009). The preliminary results show that the UNEEC-P method produces wider uncertainty bounds, which is consistent with the fact that the method considers also parameter uncertainty of the optimal model. In the future UNEEC method will be further extended to consider input and structure uncertainty which will provide more realistic estimation of model predictions.

Physics-based, Bayesian sequential detection method and system for radioactive contraband

DOEpatents

Candy, James V; Axelrod, Michael C; Breitfeller, Eric F; Chambers, David H; Guidry, Brian L; Manatt, Douglas R; Meyer, Alan W; Sale, Kenneth E

2014-03-18

A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.

A continuous-time neural model for sequential action.

PubMed

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Eyewitness identification in simultaneous and sequential lineups: an investigation of position effects using receiver operating characteristics.

PubMed

Meisters, Julia; Diedenhofen, Birk; Musch, Jochen

2018-04-20

For decades, sequential lineups have been considered superior to simultaneous lineups in the context of eyewitness identification. However, most of the research leading to this conclusion was based on the analysis of diagnosticity ratios that do not control for the respondent's response criterion. Recent research based on the analysis of ROC curves has found either equal discriminability for sequential and simultaneous lineups, or higher discriminability for simultaneous lineups. Some evidence for potential position effects and for criterion shifts in sequential lineups has also been reported. Using ROC curve analysis, we investigated the effects of the suspect's position on discriminability and response criteria in both simultaneous and sequential lineups. We found that sequential lineups suffered from an unwanted position effect. Respondents employed a strict criterion for the earliest lineup positions, and shifted to a more liberal criterion for later positions. No position effects and no criterion shifts were observed in simultaneous lineups. This result suggests that sequential lineups are not superior to simultaneous lineups, and may give rise to unwanted position effects that have to be considered when conducting police lineups.

Variability of daily UV index in Jokioinen, Finland, in 1995-2015

NASA Astrophysics Data System (ADS)

Heikkilä, A.; Uusitalo, K.; Kärhä, P.; Vaskuri, A.; Lakkala, K.; Koskela, T.

2017-02-01

UV Index is a measure for UV radiation harmful for the human skin, developed and used to promote the sun awareness and protection of people. Monitoring programs conducted around the world have produced a number of long-term time series of UV irradiance. One of the longest time series of solar spectral UV irradiance in Europe has been obtained from the continuous measurements of Brewer #107 spectrophotometer in Jokioinen (lat. 60°44'N, lon. 23°30'E), Finland, over the years 1995-2015. We have used descriptive statistics and estimates of cumulative distribution functions, quantiles and probability density functions in the analysis of the time series of daily UV Index maxima. Seasonal differences in the estimated distributions and in the trends of the estimated quantiles are found.

Factors Associated with Adherence to Adjuvant Endocrine Therapy Among Privately Insured and Newly Diagnosed Breast Cancer Patients: A Quantile Regression Analysis.

PubMed

Farias, Albert J; Hansen, Ryan N; Zeliadt, Steven B; Ornelas, India J; Li, Christopher I; Thompson, Beti

2016-08-01

Adherence to adjuvant endocrine therapy (AET) for estrogen receptor-positive breast cancer remains suboptimal, which suggests that women are not getting the full benefit of the treatment to reduce breast cancer recurrence and mortality. The majority of studies on adherence to AET focus on identifying factors among those women at the highest levels of adherence and provide little insight on factors that influence medication use across the distribution of adherence. To understand how factors influence adherence among women across low and high levels of adherence. A retrospective evaluation was conducted using the Truven Health MarketScan Commercial Claims and Encounters Database from 2007-2011. Privately insured women aged 18-64 years who were recently diagnosed and treated for breast cancer and who initiated AET within 12 months of primary treatment were assessed. Adherence was measured as the proportion of days covered (PDC) over a 12-month period. Simultaneous multivariable quantile regression was used to assess the association between treatment and demographic factors, use of mail order pharmacies, medication switching, and out-of-pocket costs and adherence. The effect of each variable was examined at the 40th, 60th, 80th, and 95th quantiles. Among the 6,863 women in the cohort, mail order pharmacies had the greatest influence on adherence at the 40th quantile, associated with a 29.6% (95% CI = 22.2-37.0) higher PDC compared with retail pharmacies. Out-of-pocket cost for a 30-day supply of AET greater than $20 was associated with an 8.6% (95% CI = 2.8-14.4) lower PDC versus $0-$9.99. The main factors that influenced adherence at the 95th quantile were mail order pharmacies, associated with a 4.4% higher PDC (95% CI = 3.8-5.0) versus retail pharmacies, and switching AET medication 2 or more times, associated with a 5.6% lower PDC versus not switching (95% CI = 2.3-9.0). Factors associated with adherence differed across quantiles. Addressing the use of mail order pharmacies and out-of-pocket costs for AET may have the greatest influence on improving adherence among those women with low adherence. This research was supported by a Ruth L. Kirschstein National Research Service Award for Individual Predoctoral Fellowship grant from the National Cancer Institute (grant number F31 CA174338), which was awarded to Farias. Additionally, Farias was funded by a Postdoctoral Fellowship at the University of Texas School of Public Health Cancer Education and Career Development Program through the National Cancer Institute (NIH Grant R25 CA57712). The other authors declare no conflicts of interest. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. Farias was primarily responsible for the study concept and design, along with Hansen and Zeliadt and with assistance from the other authors. Farias, Hansen, and Zeliadt took the lead in data interpretation, assisted by the other authors. The manuscript was written by Farias, along with Thompson and assisted by the other authors, and was revised by Ornelas, Li, and Farias, with assistance from the other authors.

Focusing of light through turbid media by curve fitting optimization

NASA Astrophysics Data System (ADS)

Gong, Changmei; Wu, Tengfei; Liu, Jietao; Li, Huijuan; Shao, Xiaopeng; Zhang, Jianqi

2016-12-01

The construction of wavefront phase plays a critical role in focusing light through turbid media. We introduce the curve fitting algorithm (CFA) into the feedback control procedure for wavefront optimization. Unlike the existing continuous sequential algorithm (CSA), the CFA locates the optimal phase by fitting a curve to the measured signals. Simulation results show that, similar to the genetic algorithm (GA), the proposed CFA technique is far less susceptible to the experimental noise than the CSA. Furthermore, only three measurements of feedback signals are enough for CFA to fit the optimal phase while obtaining a higher focal intensity than the CSA and the GA, dramatically shortening the optimization time by a factor of 3 compared with the CSA and the GA. The proposed CFA approach can be applied to enhance the focus intensity and boost the focusing speed in the fields of biological imaging, particle trapping, laser therapy, and so on, and might help to focus light through dynamic turbid media.

GIS-aided low flow mapping

NASA Astrophysics Data System (ADS)

Saghafian, B.; Mohammadi, A.

2003-04-01

Most studies involving water resources allocation, water quality, hydropower generation, and allowable water withdrawal and transfer require estimation of low flows. Normally, frequency analysis on at-station D-day low flow data is performed to derive various T-yr return period values. However, this analysis is restricted to the location of hydrometric stations where the flow discharge is measured. Regional analysis is therefore conducted to relate the at-station low flow quantiles to watershed characteristics. This enables the transposition of low flow quantiles to ungauged sites. Nevertheless, a procedure to map the regional regression relations for the entire stream network, within the bounds of the relations, is particularly helpful when one studies and weighs alternative sites for certain water resources project. In this study, we used a GIS-aided procedure for low flow mapping in Gilan province, part of northern region in Iran. Gilan enjoys a humid climate with an average of 1100 mm annual precipitation. Although rich in water resources, the highly populated area is quite dependent on minimum amount of water to sustain the vast rice farming and to maintain required flow discharge for quality purposes. To carry out the low flow analysis, a total of 36 hydrometric stations with sufficient and reliable discharge data were identified in the region. The average area of the watersheds was 250 sq. km. Log Pearson type 3 was found the best distribution for flow durations over 60 days, while log normal fitted well the shorter duration series. Low flows with return periods of 2, 5, 10, 25, 50, and 100 year were then computed. Cluster analysis identified two homogeneous areas. Although various watershed parameters were examined in factor analysis, the results showed watershed area, length of the main stream, and annual precipitation were the most effective low flow parameters. The regression equations were then mapped with the aid of GIS based on flow accumulation maps and the corresponding spatially averaged values of other parameters over the upslope area of all stream pixels exceeding a certain threshold area. Such map clearly shows the spatial variation of low flow quantiles along the stream network and enables the study of low flow profiles along any stream.

Self-, other-, and joint monitoring using forward models.

PubMed

Pickering, Martin J; Garrod, Simon

2014-01-01

In the psychology of language, most accounts of self-monitoring assume that it is based on comprehension. Here we outline and develop the alternative account proposed by Pickering and Garrod (2013), in which speakers construct forward models of their upcoming utterances and compare them with the utterance as they produce them. We propose that speakers compute inverse models derived from the discrepancy (error) between the utterance and the predicted utterance and use that to modify their production command or (occasionally) begin anew. We then propose that comprehenders monitor other people's speech by simulating their utterances using covert imitation and forward models, and then comparing those forward models with what they hear. They use the discrepancy to compute inverse models and modify their representation of the speaker's production command, or realize that their representation is incorrect and may develop a new production command. We then discuss monitoring in dialogue, paying attention to sequential contributions, concurrent feedback, and the relationship between monitoring and alignment.

Self-, other-, and joint monitoring using forward models

PubMed Central

Pickering, Martin J.; Garrod, Simon

2014-01-01

In the psychology of language, most accounts of self-monitoring assume that it is based on comprehension. Here we outline and develop the alternative account proposed by Pickering and Garrod (2013), in which speakers construct forward models of their upcoming utterances and compare them with the utterance as they produce them. We propose that speakers compute inverse models derived from the discrepancy (error) between the utterance and the predicted utterance and use that to modify their production command or (occasionally) begin anew. We then propose that comprehenders monitor other people’s speech by simulating their utterances using covert imitation and forward models, and then comparing those forward models with what they hear. They use the discrepancy to compute inverse models and modify their representation of the speaker’s production command, or realize that their representation is incorrect and may develop a new production command. We then discuss monitoring in dialogue, paying attention to sequential contributions, concurrent feedback, and the relationship between monitoring and alignment. PMID:24723869

Accelerated search for materials with targeted properties by adaptive design

PubMed Central

Xue, Dezhen; Balachandran, Prasanna V.; Hogden, John; Theiler, James; Xue, Deqing; Lookman, Turab

2016-01-01

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set. PMID:27079901

Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT)

USGS Publications Warehouse

Legleiter, Carl

2016-01-01

Conventional, regression-based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile Transformation (IDQT), that involves linking the frequency distribution of pixel values to that of depth. In addition, a new image processing workflow involving deep water correction and Minimum Noise Fraction (MNF) transformation can reduce a hyperspectral data set to a single variable related to depth and thus suitable for input to IDQT. Applied to a gravel bed river, IDQT avoided negative depth estimates along channel margins and underpredictions of pool depth. Depth retrieval accuracy (R25 0.79) and precision (0.27 m) were comparable to an established band ratio-based method, although a small shallow bias (0.04 m) was observed. Several ways of specifying distributions of pixel values and depths were evaluated but had negligible impact on the resulting depth estimates, implying that IDQT was robust to these implementation details. In essence, IDQT uses frequency distributions of pixel values and depths to achieve an aspatial calibration; the image itself provides information on the spatial distribution of depths. The approach thus reduces sensitivity to misalignment between field and image data sets and allows greater flexibility in the timing of field data collection relative to image acquisition, a significant advantage in dynamic channels. IDQT also creates new possibilities for depth retrieval in the absence of field data if a model could be used to predict the distribution of depths within a reach.

Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints

PubMed Central

Virlet, Nicolas; Lebourgeois, Valentine; Martinez, Sébastien; Costes, Evelyne; Labbé, Sylvain; Regnard, Jean-Luc

2014-01-01

As field phenotyping of plant response to water constraints constitutes a bottleneck for breeding programmes, airborne thermal imagery can contribute to assessing the water status of a wide range of individuals simultaneously. However, the presence of mixed soil–plant pixels in heterogeneous plant cover complicates the interpretation of canopy temperature. Moran’s Water Deficit Index (WDI = 1–ETact/ETmax), which was designed to overcome this difficulty, was compared with surface minus air temperature (T s–T a) as a water stress indicator. As parameterization of the theoretical equations for WDI computation is difficult, particularly when applied to genotypes with large architectural variability, a simplified procedure based on quantile regression was proposed to delineate the Vegetation Index–Temperature (VIT) scatterplot. The sensitivity of WDI to variations in wet and dry references was assessed by applying more or less stringent quantile levels. The different stress indicators tested on a series of airborne multispectral images (RGB, near-infrared, and thermal infrared) of a population of 122 apple hybrids, under two irrigation regimes, significantly discriminated the tree water statuses. For each acquisition date, the statistical method efficiently delineated the VIT scatterplot, while the limits obtained using the theoretical approach overlapped it, leading to inconsistent WDI values. Once water constraint was established, the different stress indicators were linearly correlated to the stem water potential among a tree subset. T s–T a showed a strong sensitivity to evaporative demand, which limited its relevancy for temporal comparisons. Finally, the statistical approach of WDI appeared the most suitable for high-throughput phenotyping. PMID:25080086

Overweight and Obesity in Southern Italy: their association with social and life-style characteristics and their effect on levels of biologic markers.

PubMed

Osella, Alberto R; Díaz, María Del Pilar; Cozzolongo, Rafaelle; Bonfiglio, Caterina; Franco, Isabella; Abrescia, Daniela Isabel; Bianco, Antonella; Giampiero, Elba Silvana; Petruzzi, José; Elsa, Lanzilota; Mario, Correale; Mastrosimni, Anna María; Giocchino, Leandro

2014-01-01

In the last decades, overweight and obesity have been transformed from minor public health issues to a major threat to public health affecting the most affluent societies and also the less developed ones. To estimate overweight-obesity prevalence in adults, their association with some social determinants and to assess the effect of these two conditions on levels of biologic and biochemical characteristics, by means of a population-based study. A random sample of the general population of Putignano was drawn. All participants completed a general pre-coded and a Food Frequency questionnaire; anthropometric measures were taken and a venous blood sample was drawn. All subjects underwent liver ultra-sonography. Data description was done by means of tables and then Quantile Regression was performed. Overall prevalence of overweight and obesity were 34.5% and 16.1% respectively. Both overweight and obesity were more frequent among male, married and low socio-economic position subjects. There were increasing frequencies of normal weight with higher levels of education. Overweight and obese subjects had more frequently Nonalcoholic Fatty Liver Disease, Hypertension and altered biochemical markers. Quantile regression showed a statistically significant association of age with overweight and obesity (maximum about 64.8 yo), gender (female) and low levels of education in both overweight and obesity. More than 10 gr/day of wine intake was associated with overweight. The prevention and treatment of overweight/obesity on a population wide basis are needed. Population-based strategies should also improve social and physical environmental contexts for healthful lifestyles.

A weight modification sequential method for VSC-MTDC power system state estimation

NASA Astrophysics Data System (ADS)

Yang, Xiaonan; Zhang, Hao; Li, Qiang; Guo, Ziming; Zhao, Kun; Li, Xinpeng; Han, Feng

2017-06-01

This paper presents an effective sequential approach based on weight modification for VSC-MTDC power system state estimation, called weight modification sequential method. The proposed approach simplifies the AC/DC system state estimation algorithm through modifying the weight of state quantity to keep the matrix dimension constant. The weight modification sequential method can also make the VSC-MTDC system state estimation calculation results more ccurate and increase the speed of calculation. The effectiveness of the proposed weight modification sequential method is demonstrated and validated in modified IEEE 14 bus system.

A Data Centred Method to Estimate and Map Changes in the Full Distribution of Daily Precipitation and Its Exceedances

NASA Astrophysics Data System (ADS)

Chapman, S. C.; Stainforth, D. A.; Watkins, N. W.

2014-12-01

Estimates of how our climate is changing are needed locally in order to inform adaptation planning decisions. This requires quantifying the geographical patterns in changes at specific quantiles or thresholds in distributions of variables such as daily temperature or precipitation. We develop a method[1] for analysing local climatic timeseries to assess which quantiles of the local climatic distribution show the greatest and most robust changes, to specifically address the challenges presented by 'heavy tailed' distributed variables such as daily precipitation. We extract from the data quantities that characterize the changes in time of the likelihood of daily precipitation above a threshold and of the relative amount of precipitation in those extreme precipitation days. Our method is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of precipitation distributions are changing. This involves both determining which quantiles and geographical locations show the greatest change but also, those at which any change is highly uncertain. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily precipitation from specific locations across Europe over the last 60 years. We treat geographical location and precipitation as independent variables and thus obtain as outputs the pattern of change at a given threshold of precipitation and with geographical location. This is model- independent, thus providing data of direct value in model calibration and assessment. Our results identify regionally consistent patterns which, dependent on location, show systematic increase in precipitation on the wettest days, shifts in precipitation patterns to less moderate days and more heavy days, and drying across all days which is of potential value in adaptation planning. [1] S C Chapman, D A Stainforth, N W Watkins, 2013 Phil. Trans. R. Soc. A, 371 20120287; D. A. Stainforth, S. C. Chapman, N. W. Watkins, 2013 Environ. Res. Lett. 8, 034031 [2] Haylock et al. 2008 J. Geophys. Res (Atmospheres), 113, D20119

Estimating geographic variation on allometric growth and body condition of Blue Suckers with quantile regression

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Neely, B.C.

2011-01-01

Increasing our understanding of how environmental factors affect fish body condition and improving its utility as a metric of aquatic system health require reliable estimates of spatial variation in condition (weight at length). We used three statistical approaches that varied in how they accounted for heterogeneity in allometric growth to estimate differences in body condition of blue suckers Cycleptus elongatus across 19 large-river locations in the central USA. Quantile regression of an expanded allometric growth model provided the most comprehensive estimates, including variation in exponents within and among locations (range = 2.88–4.24). Blue suckers from more-southerly locations had the largest exponents. Mixed-effects mean regression of a similar expanded allometric growth model allowed exponents to vary among locations (range = 3.03–3.60). Mean relative weights compared across selected intervals of total length (TL = 510–594 and 594–692 mm) in a multiplicative model involved the implicit assumption that allometric exponents within and among locations were similar to the exponent (3.46) for the standard weight equation. Proportionate differences in the quantiles of weight at length for adult blue suckers (TL = 510, 594, 644, and 692 mm) compared with their average across locations ranged from 1.08 to 1.30 for southern locations (Texas, Mississippi) and from 0.84 to 1.00 for northern locations (Montana, North Dakota); proportionate differences for mean weight ranged from 1.13 to 1.17 and from 0.87 to 0.95, respectively, and those for mean relative weight ranged from 1.10 to 1.18 and from 0.86 to 0.98, respectively. Weights for fish at longer lengths varied by 600–700 g within a location and by as much as 2,000 g among southern and northern locations. Estimates for the Wabash River, Indiana (0.96–1.07 times the average; greatest increases for lower weights at shorter TLs), and for the Missouri River from Blair, Nebraska, to Sioux City, Iowa (0.90–1.00 times the average; greatest decreases for lower weights at longer TLs), were examined in detail to explain the additional information provided by quantile estimates.

Incense Burning during Pregnancy and Birth Weight and Head Circumference among Term Births: The Taiwan Birth Cohort Study.

PubMed

Chen, Le-Yu; Ho, Christine

2016-09-01

Incense burning for rituals or religious purposes is an important tradition in many countries. However, incense smoke contains particulate matter and gas products such as carbon monoxide, sulfur, and nitrogen dioxide, which are potentially harmful to health. We analyzed the relationship between prenatal incense burning and birth weight and head circumference at birth using the Taiwan Birth Cohort Study. We also analyzed whether the associations varied by sex and along the distribution of birth outcomes. We performed ordinary least squares (OLS) and quantile regressions analysis on a sample of 15,773 term births (> 37 gestational weeks; 8,216 boys and 7,557 girls) in Taiwan in 2005. The associations were estimated separately for boys and girls as well as for the population as a whole. We controlled extensively for factors that may be correlated with incense burning and birth weight and head circumference, such as parental religion, demographics, and health characteristics, as well as pregnancy-related variables. Findings from fully adjusted OLS regressions indicated that exposure to incense was associated with lower birth weight in boys (-18 g; 95% CI: -36, -0.94) but not girls (1 g; 95% CI: -17, 19; interaction p-value = 0.31). Associations with head circumference were negative for boys (-0.95 mm; 95% CI: -1.8, -0.16) and girls (-0.71 mm; 95% CI: -1.5, 0.11; interaction p-values = 0.73). Quantile regression results suggested that the negative associations were larger among the lower quantiles of birth outcomes. OLS regressions showed that prenatal incense burning was associated with lower birth weight for boys and smaller head circumference for boys and girls. The associations were more pronounced among the lower quantiles of birth outcomes. Further research is necessary to confirm whether incense burning has differential effects by sex. Chen LY, Ho C. 2016. Incense burning during pregnancy and birth weight and head circumference among term births: The Taiwan Birth Cohort Study. Environ Health Perspect 124:1487-1492; http://dx.doi.org/10.1289/ehp.1509922.

Incense Burning during Pregnancy and Birth Weight and Head Circumference among Term Births: The Taiwan Birth Cohort Study

PubMed Central

Chen, Le-Yu; Ho, Christine

2016-01-01

Background: Incense burning for rituals or religious purposes is an important tradition in many countries. However, incense smoke contains particulate matter and gas products such as carbon monoxide, sulfur, and nitrogen dioxide, which are potentially harmful to health. Objectives: We analyzed the relationship between prenatal incense burning and birth weight and head circumference at birth using the Taiwan Birth Cohort Study. We also analyzed whether the associations varied by sex and along the distribution of birth outcomes. Methods: We performed ordinary least squares (OLS) and quantile regressions analysis on a sample of 15,773 term births (> 37 gestational weeks; 8,216 boys and 7,557 girls) in Taiwan in 2005. The associations were estimated separately for boys and girls as well as for the population as a whole. We controlled extensively for factors that may be correlated with incense burning and birth weight and head circumference, such as parental religion, demographics, and health characteristics, as well as pregnancy-related variables. Results: Findings from fully adjusted OLS regressions indicated that exposure to incense was associated with lower birth weight in boys (–18 g; 95% CI: –36, –0.94) but not girls (1 g; 95% CI: –17, 19; interaction p-value = 0.31). Associations with head circumference were negative for boys (–0.95 mm; 95% CI: –1.8, –0.16) and girls (–0.71 mm; 95% CI: –1.5, 0.11; interaction p-values = 0.73). Quantile regression results suggested that the negative associations were larger among the lower quantiles of birth outcomes. Conclusions: OLS regressions showed that prenatal incense burning was associated with lower birth weight for boys and smaller head circumference for boys and girls. The associations were more pronounced among the lower quantiles of birth outcomes. Further research is necessary to confirm whether incense burning has differential effects by sex. Citation: Chen LY, Ho C. 2016. Incense burning during pregnancy and birth weight and head circumference among term births: The Taiwan Birth Cohort Study. Environ Health Perspect 124:1487–1492; http://dx.doi.org/10.1289/ehp.1509922 PMID:26967367

Sequential biases in accumulating evidence

PubMed Central

Huggins, Richard; Dogo, Samson Henry

2015-01-01

Whilst it is common in clinical trials to use the results of tests at one phase to decide whether to continue to the next phase and to subsequently design the next phase, we show that this can lead to biased results in evidence synthesis. Two new kinds of bias associated with accumulating evidence, termed ‘sequential decision bias’ and ‘sequential design bias’, are identified. Both kinds of bias are the result of making decisions on the usefulness of a new study, or its design, based on the previous studies. Sequential decision bias is determined by the correlation between the value of the current estimated effect and the probability of conducting an additional study. Sequential design bias arises from using the estimated value instead of the clinically relevant value of an effect in sample size calculations. We considered both the fixed‐effect and the random‐effects models of meta‐analysis and demonstrated analytically and by simulations that in both settings the problems due to sequential biases are apparent. According to our simulations, the sequential biases increase with increased heterogeneity. Minimisation of sequential biases arises as a new and important research area necessary for successful evidence‐based approaches to the development of science. © 2015 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd. PMID:26626562

CFD simulation of hemodynamics in sequential and individual coronary bypass grafts based on multislice CT scan datasets.

PubMed

Hajati, Omid; Zarrabi, Khalil; Karimi, Reza; Hajati, Azadeh

2012-01-01

There is still controversy over the differences in the patency rates of the sequential and individual coronary artery bypass grafting (CABG) techniques. The purpose of this paper was to non-invasively evaluate hemodynamic parameters using complete 3D computational fluid dynamics (CFD) simulations of the sequential and the individual methods based on the patient-specific data extracted from computed tomography (CT) angiography. For CFD analysis, the geometric model of coronary arteries was reconstructed using an ECG-gated 64-detector row CT. Modeling the sequential and individual bypass grafting, this study simulates the flow from the aorta to the occluded posterior descending artery (PDA) and the posterior left ventricle (PLV) vessel with six coronary branches based on the physiologically measured inlet flow as the boundary condition. The maximum calculated wall shear stress (WSS) in the sequential and the individual models were estimated to be 35.1 N/m(2) and 36.5 N/m(2), respectively. Compared to the individual bypass method, the sequential graft has shown a higher velocity at the proximal segment and lower spatial wall shear stress gradient (SWSSG) due to the flow splitting caused by the side-to-side anastomosis. Simulated results combined with its surgical benefits including the requirement of shorter vein length and fewer anastomoses advocate the sequential method as a more favorable CABG method.

Multi-atlas segmentation of the cartilage in knee MR images with sequential volume- and bone-mask-based registrations

NASA Astrophysics Data System (ADS)

Lee, Han Sang; Kim, Hyeun A.; Kim, Hyeonjin; Hong, Helen; Yoon, Young Cheol; Kim, Junmo

2016-03-01

In spite of its clinical importance in diagnosis of osteoarthritis, segmentation of cartilage in knee MRI remains a challenging task due to its shape variability and low contrast with surrounding soft tissues and synovial fluid. In this paper, we propose a multi-atlas segmentation of cartilage in knee MRI with sequential atlas registrations and locallyweighted voting (LWV). First, bone is segmented by sequential volume- and object-based registrations and LWV. Second, to overcome the shape variability of cartilage, cartilage is segmented by bone-mask-based registration and LWV. In experiments, the proposed method improved the bone segmentation by reducing misclassified bone region, and enhanced the cartilage segmentation by preventing cartilage leakage into surrounding similar intensity region, with the help of sequential registrations and LWV.

Computer-Based Feedback and Goal Intervention: Learning Effects

ERIC Educational Resources Information Center

Valdez, Alfred

2012-01-01

This study investigated how a goal intervention influences the learning effects gained from feedback when acquiring concepts and rules pertaining to the topic of descriptive statistics. Three feedback conditions; knowledge of correct response feedback (KCRF), principle-based feedback (PBF), and no-feedback (NF), were crossed with two goal…

A stacked sequential learning method for investigator name recognition from web-based medical articles

NASA Astrophysics Data System (ADS)

Zhang, Xiaoli; Zou, Jie; Le, Daniel X.; Thoma, George

2010-01-01

"Investigator Names" is a newly required field in MEDLINE citations. It consists of personal names listed as members of corporate organizations in an article. Extracting investigator names automatically is necessary because of the increasing volume of articles reporting collaborative biomedical research in which a large number of investigators participate. In this paper, we present an SVM-based stacked sequential learning method in a novel application - recognizing named entities such as the first and last names of investigators from online medical journal articles. Stacked sequential learning is a meta-learning algorithm which can boost any base learner. It exploits contextual information by adding the predicted labels of the surrounding tokens as features. We apply this method to tag words in text paragraphs containing investigator names, and demonstrate that stacked sequential learning improves the performance of a nonsequential base learner such as an SVM classifier.

Assessment of probabilistic areal reduction factors of precipitations for the entire French territory with gridded rainfall data.

NASA Astrophysics Data System (ADS)

Fouchier, Catherine; Maire, Alexis; Arnaud, Patrick; Cantet, Philippe; Odry, Jean

2016-04-01

The starting point of our study was the availability of maps of rainfall quantiles available for the entire French mainland territory at the spatial resolution of 1 km². These maps display the rainfall amounts estimated for different rainfall durations (from 15 minutes to 72 hours) and different return periods (from 2 years up to 1 000 years). They are provided by a regionalized stochastic hourly point rainfall generator, the SHYREG method which was previously developed by Irstea (Arnaud et al., 2007; Cantet and Arnaud, 2014). Being calibrated independently on numerous raingauges data (with an average density across the country of 1 raingauge per 200 km²), this method suffers from a limitation common to point-process rainfall generators: it can only reproduce point rainfall patterns and has no capacity to generate rainfall fields. It can't hence provide areal rainfall quantiles, the estimation of the latter being however needed for the construction of design rainfall or for the diagnostic of observed events. One means of bridging this gap between our local rainfall quantiles and areal rainfall quantiles is given by the concept of probabilistic areal reduction factors of rainfall (ARF) as defined by Omolayo (1993). This concept enables to estimate areal rainfall of a particular frequency within a certain amount of time from point rainfalls of the same frequency and duration. Assessing such ARF for the whole French territory is of particular interest since it should allow us to compute areal rainfall quantiles, and eventually watershed rainfall quantiles, by using the already available grids of statistical point rainfall of the SHYREG method. Our purpose was then to assess these ARF thanks to long time-series of spatial rainfall data. We have used two sets of rainfall fields: i) hourly rainfall fields from a 10-year reference database of Quantitative Precipitation Estimation (QPE) over France (Tabary et al., 2012), ii) daily rainfall fields resulting from a 53-year high-resolution atmospheric reanalysis over France with the SAFRAN-gauge-based analysis system (Vidal et al., 2010). We have then built samples of maximal rainfalls for each cell location (the "point" rainfalls) and for different areas centered on each cell location (the areal rainfalls) of these gridded data. To compute rainfall quantiles, we have fitted a Gumbel law, with the L-moment method, on each of these samples. Our daily and hourly ARF have then shown four main trends: i) a sensitivity to the return period, with ARF values decreasing when the return period increases; ii) a sensitivity to the rainfall duration, with ARF values decreasing when the rainfall duration decreases; iii) a sensitivity to the season, with ARF values smaller for the summer period than for the winter period; iv) a sensitivity to the geographical location, with low ARF values in the French Mediterranean area and ARF values close to 1 for the climatic zones of Northern and Western France (oceanic to semi-continental climate). The results of this data-intensive study led for the first time on the whole French territory are in agreement with studies led abroad (e.g. Allen and DeGaetano 2005, Overeem et al. 2010) and confirm and widen the results of previous studies that were carried out in France on smaller areas and with fewer rainfall durations (e.g. Ramos et al., 2006, Neppel et al., 2003). References Allen R. J. and DeGaetano A. T. (2005). Areal reduction factors for two eastern United States regions with high rain-gauge density. Journal of Hydrologic Engineering 10(4): 327-335. Arnaud P., Fine J.-A. and Lavabre J. (2007). An hourly rainfall generation model applicable to all types of climate. Atmospheric Research 85(2): 230-242. Cantet, P. and Arnaud, P. (2014). Extreme rainfall analysis by a stochastic model: impact of the copula choice on the sub-daily rainfall generation, Stochastic Environmental Research and Risk Assessment, Springer Berlin Heidelberg, 28(6), 1479-1492. Neppel L., Bouvier C. and Lavabre J. (2003). Areal reduction factor probabilities for rainfall in Languedoc Roussillon. IAHS-AISH Publication (278): 276-283. Omolayo, A. S. (1993). On the transposition of areal reduction factors for rainfall frequency estimation. Journal of Hydrology 145 (1-2): 191-205. Overeem A., Buishand T. A., Holleman I. and Uijlenhoet R. (2010). Extreme value modeling of areal rainfall from weather radar. Water Resources Research 46(9): 10 p. Ramos M.-H., Leblois E., Creutin J.-D. (2006). From point to areal rainfall: Linking the different approaches for the frequency characterisation of rainfalls in urban areas. Water Science and Technology. 54(6-7): 33-40. Tabary P., Dupuy P., L'Henaff G., Gueguen C., Moulin L., Laurantin O., Merlier C., Soubeyroux J. M. (2012). A 10-year (1997-2006) reanalysis of Quantitative Precipitation Estimation over France: methodology and first results. IAHS-AISH Publication (351) : 255-260. Vidal J.-P., Martin E., Franchistéguy L., Baillon M. and Soubeyroux J.-M. (2010). A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology 30(11): 1627-1644.

Catabolite regulation analysis of Escherichia coli for acetate overflow mechanism and co-consumption of multiple sugars based on systems biology approach using computer simulation.

PubMed

Matsuoka, Yu; Shimizu, Kazuyuki

2013-10-20

It is quite important to understand the basic principle embedded in the main metabolism for the interpretation of the fermentation data. For this, it may be useful to understand the regulation mechanism based on systems biology approach. In the present study, we considered the perturbation analysis together with computer simulation based on the models which include the effects of global regulators on the pathway activation for the main metabolism of Escherichia coli. Main focus is the acetate overflow metabolism and the co-fermentation of multiple carbon sources. The perturbation analysis was first made to understand the nature of the feed-forward loop formed by the activation of Pyk by FDP (F1,6BP), and the feed-back loop formed by the inhibition of Pfk by PEP in the glycolysis. Those together with the effect of transcription factor Cra caused by FDP level affected the glycolysis activity. The PTS (phosphotransferase system) acts as the feed-back system by repressing the glucose uptake rate for the increase in the glucose uptake rate. It was also shown that the increased PTS flux (or glucose consumption rate) causes PEP/PYR ratio to be decreased, and EIIA-P, Cya, cAMP-Crp decreased, where cAMP-Crp in turn repressed TCA cycle and more acetate is formed. This was further verified by the detailed computer simulation. In the case of multiple carbon sources such as glucose and xylose, it was shown that the sequential utilization of carbon sources was observed for wild type, while the co-consumption of multiple carbon sources with slow consumption rates were observed for the ptsG mutant by computer simulation, and this was verified by experiments. Moreover, the effect of a specific gene knockout such as Δpyk on the metabolic characteristics was also investigated based on the computer simulation. Copyright © 2013 Elsevier B.V. All rights reserved.

Internal Medicine Residents' Perspectives on Receiving Feedback in Milestone Format

PubMed Central

Angus, Steven; Moriarty, John; Nardino, Robert J.; Chmielewski, Amy; Rosenblum, Michael J.

2015-01-01

Background In contrast to historical feedback, which was vague or provided residents' numerical scores without clear meaning, milestone-based feedback is focused on specific knowledge, skills, and behaviors that define developmental trajectory. It was anticipated that residents would welcome the more specific and actionable feedback provided by the milestone framework, but this has not been studied. Objective We assessed internal medicine (IM) residents' perceptions of receiving feedback in the milestone framework, particularly assessing perception of the utility of milestone-based feedback compared to non–milestone-based feedback. Methods We surveyed a total of 510 IM residents from 7 institutions. Survey questions assessed resident perception of milestone feedback in identifying strengths, weaknesses, and trajectory of professional development. Postgraduate years 2 and 3 (PGY-2 and PGY-3) residents were asked to compare milestones with prior methods of feedback. Results Of 510 residents, 356 (69.8%) responded. Slightly less than half of the residents found milestone-based feedback “extremely useful” or “very useful” in identifying strengths (44%), weaknesses (43%), specific areas for improvement (45%), and appropriate education progress (48%). Few residents found such feedback “not very useful” or “not at all useful” in these domains. A total of 51% of PGY-2 and PGY-3 residents agreed that receiving milestone-based feedback was more helpful than previous forms of feedback. Conclusions IM residents are aware of the concepts of milestones, and half of the residents surveyed found milestone feedback more helpful than previous forms of feedback. More work needs to be done to understand how milestone-based feedback could be delivered more effectively to enhance resident development. PMID:26221438

Ensemble Response in Mushroom Body Output Neurons of the Honey Bee Outpaces Spatiotemporal Odor Processing Two Synapses Earlier in the Antennal Lobe

PubMed Central

Strube-Bloss, Martin F.; Herrera-Valdez, Marco A.; Smith, Brian H.

2012-01-01

Neural representations of odors are subject to computations that involve sequentially convergent and divergent anatomical connections across different areas of the brains in both mammals and insects. Furthermore, in both mammals and insects higher order brain areas are connected via feedback connections. In order to understand the transformations and interactions that this connectivity make possible, an ideal experiment would compare neural responses across different, sequential processing levels. Here we present results of recordings from a first order olfactory neuropile – the antennal lobe (AL) – and a higher order multimodal integration and learning center – the mushroom body (MB) – in the honey bee brain. We recorded projection neurons (PN) of the AL and extrinsic neurons (EN) of the MB, which provide the outputs from the two neuropils. Recordings at each level were made in different animals in some experiments and simultaneously in the same animal in others. We presented two odors and their mixture to compare odor response dynamics as well as classification speed and accuracy at each neural processing level. Surprisingly, the EN ensemble significantly starts separating odor stimuli rapidly and before the PN ensemble has reached significant separation. Furthermore the EN ensemble at the MB output reaches a maximum separation of odors between 84–120 ms after odor onset, which is 26 to 133 ms faster than the maximum separation at the AL output ensemble two synapses earlier in processing. It is likely that a subset of very fast PNs, which respond before the ENs, may initiate the rapid EN ensemble response. We suggest therefore that the timing of the EN ensemble activity would allow retroactive integration of its signal into the ongoing computation of the AL via centrifugal feedback. PMID:23209711

Quantile regression and Bayesian cluster detection to identify radon prone areas.

PubMed

Sarra, Annalina; Fontanella, Lara; Valentini, Pasquale; Palermi, Sergio

2016-11-01

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys. Copyright © 2016 Elsevier Ltd. All rights reserved.

Not dead yet: the seasonal water relations of two perennial ferns during California's exceptional drought.

PubMed

Baer, Alex; Wheeler, James K; Pittermann, Jarmila

2016-04-01

The understory of the redwood forests of California's coast harbors perennial ferns, including Polystichum munitum and Dryopteris arguta. Unusual for ferns, these species are adapted to the characteristic Mediterranean-type dry season, but the mechanisms of tolerance have not been studied. The water relations of P. munitum and D. arguta were surveyed for over a year, including measures of water potential (Ψ), stomatal conductance (gs) and frond stipe hydraulic conductivity (K). A dehydration and re-watering experiment on potted P. munitum plants corroborated the field data. The seasonal Ψ varied from 0 to below -3 MPa in both species, with gs and K generally tracking Ψ; the loss of K rarely exceeded 80%. Quantile regression analysis showed that, at the 0.1 quantile, 50% of K was lost at -2.58 and -3.84 MPa in P. munitum and D. arguta, respectively. The hydraulic recovery of re-watered plants was attributed to capillarity. The seasonal water relations of P. munitum and D. arguta are variable, but consistent with laboratory-based estimates of drought tolerance. Hydraulic and Ψ recovery following rain allows perennial ferns to survive severe drought, but prolonged water deficit, coupled with insect damage, may hamper frond survival. The legacy effects of drought on reproductive capacity and community dynamics are unknown. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Motivational Beliefs, Student Effort, and Feedback Behaviour in Computer-Based Formative Assessment

ERIC Educational Resources Information Center

Timmers, Caroline F.; Braber-van den Broek, Jannie; van den Berg, Stephanie M.

2013-01-01

Feedback can only be effective when students seek feedback and process it. This study examines the relations between students' motivational beliefs, effort invested in a computer-based formative assessment, and feedback behaviour. Feedback behaviour is represented by whether a student seeks feedback and the time a student spends studying the…

Trial Sequential Methods for Meta-Analysis

ERIC Educational Resources Information Center

Kulinskaya, Elena; Wood, John

2014-01-01

Statistical methods for sequential meta-analysis have applications also for the design of new trials. Existing methods are based on group sequential methods developed for single trials and start with the calculation of a required information size. This works satisfactorily within the framework of fixed effects meta-analysis, but conceptual…

The New Parent Checklist: A Tool to Promote Parental Reflection.

PubMed

Keys, Elizabeth M; McNeil, Deborah A; Wallace, Donna A; Bostick, Jason; Churchill, A Jocelyn; Dodd, Maureen M

To design and establish content and face validity of an evidence-informed tool that promotes parental self-reflection during the transition to parenthood. The New Parent Checklist was developed using a three-phase sequential approach: Phase 1 a scoping review and expert consultation to develop and refine a prototype tool; Phase 2 content analysis of parent focus groups; and Phase 3 assessment of utility in a cross-sectional sample of parents completing the New Parent Checklist and a questionnaire. The initial version of the checklist was considered by experts to contain key information. Focus group participants found it useful, appropriate, and nonjudgmental, and offered suggestions to enhance readability, utility, as well as face and content validity. In the cross-sectional survey, 83% of the participants rated the New Parent Checklist as "helpful" or "very helpful" and 90% found the New Parent Checklist "very easy" to use. Open-ended survey responses included predominantly positive feedback. Notable differences existed for some items based on respondents' first language, age, and sex. Results and feedback from all three phases informed the current version, available for download online. The New Parent Checklist is a comprehensive evidence-informed self-reflective tool with promising content and face validity. Depending on parental characteristics and infant age, certain items of the New Parent Checklist have particular utility but may also require further adaptation and testing. Local resources for information and/or support are included in the tool and could be easily adapted by other regions to incorporate their own local resources.

Transportable and vibration-free full-field low-coherent quantitative phase microscope

NASA Astrophysics Data System (ADS)

Yamauchi, Toyohiko; Yamada, Hidenao; Goto, Kentaro; Matsui, Hisayuki; Yasuhiko, Osamu; Ueda, Yukio

2018-02-01

We developed a transportable Linnik-type full-field low-coherent quantitative phase microscope that is able to compensate for optical path length (OPL) disturbance due to environmental mechanical noises. Though two-beam interferometers such as Linnik ones suffer from unstable OPL difference, we overcame this problem with a mechanical feedback system based on digital signal-processing that controls the OPL difference in sub-nanometer resolution precisely with a feedback bandwidth of 4 kHz. The developed setup has a footprint of 200 mm by 200 mm, a height of 500 mm, and a weight of 4.5 kilograms. In the transmission imaging mode, cells were cultured on a reflection-enhanced glass-bottom dish, and we obtained interference images sequentially while performing stepwise quarter-wavelength phase-shifting. Real-time image processing, including retrieval of the unwrapped phase from interference images and its background correction, along with the acquisition of interference images, was performed on a laptop computer. Emulation of the phase contrast (PhC) images and the differential interference contrast (DIC) images was also performed in real time. Moreover, our setup was applied for full-field cell membrane imaging in the reflection mode, where the cells were cultured on an anti-reflection (AR)-coated glass-bottom dish. The phase and intensity of the light reflected by the membrane revealed the outer shape of the cells independent of the refractive index. In this paper, we show imaging results on cultured cells in both transmission and reflection modes.

Treatment of mites folliculitis with an ornidazole-based sequential therapy: A randomized trial.

PubMed

Luo, Yang; Sun, Yu-Jiao; Zhang, Li; Luan, Xiu-Li

2016-07-01

Treatment of Demodex infestations is often inadequate and associated with low effective rate. We sought to evaluate the efficacy of an ornidazole-based sequential therapy for mites folliculitis treatment. Two-hundred patients with mites folliculitis were sequentially treated with either an ornidazole- or metronidazole-based regimen. Sebum cutaneum was extruded from the sebaceous glands of each patient's nose and the presence of Demodex mites were examined by light microscopy. The clinical manifestations of relapse of mites folliculitis were recorded and the subjects were followed up at 2, 4, 8, and 12 weeks post-treatment. Patients treated with the ornidazole-based regimen showed an overall effective rate of 94.0%. Additionally, at the 2, 4, 8, and 12-week follow-up, these patients had significantly lower rates of Demodex mite relapse and new lesion occurrence compared with patients treated with the metronidazole-based regimen (P < 0.05). Sequential therapy using ornidazole, betamethasone, and recombinant bovine basic fibroblast growth factor (rbFGF) gel is highly effective for treating mites folliculitis.

Traffic Predictive Control: Case Study and Evaluation

DOT National Transportation Integrated Search

2017-06-26

This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning

PubMed Central

McGregor, Heather R.; Mohatarem, Ayman

2017-01-01

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback. PMID:28753634

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.

PubMed

Cashaback, Joshua G A; McGregor, Heather R; Mohatarem, Ayman; Gribble, Paul L

2017-07-01

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.

The effect of smoking habit changes on body weight: Evidence from the UK.

PubMed

Pieroni, Luca; Salmasi, Luca

2016-03-01

This paper evaluates the causal relationship between smoking and body weight through two waves (2004-2006) of the British Household Panel Survey. We model the effect of changes in smoking habits, such as quitting or reducing, and account for the heterogeneous responses of individuals located at different points of the body mass distribution by quantile regression. We test our results by means of a large set of control groups and investigate their robustness by using the changes-in-changes estimator and accounting for different thresholds to define smoking reductions. Our results reveal the positive effect of quitting smoking on weight changes, which is also found to increase in the highest quantiles, whereas the decision to reduce smoking does not affect body weight. Copyright © 2015 Elsevier B.V. All rights reserved.

Using instant messaging to enhance the interpersonal relationships of Taiwanese adolescents: evidence from quantile regression analysis.

PubMed

Lee, Yueh-Chiang; Sun, Ya Chung

2009-01-01

Even though use of the internet by adolescents has grown exponentially, little is known about the correlation between their interaction via Instant Messaging (IM) and the evolution of their interpersonal relationships in real life. In the present study, 369 junior high school students in Taiwan responded to questions regarding their IM usage and their dispositional measures of real-life interpersonal relationships. Descriptive statistics, factor analysis, and quantile regression methods were used to analyze the data. Results indicate that (1) IM helps define adolescents' self-identity (forming and maintaining individual friendships) and social-identity (belonging to a peer group), and (2) how development of an interpersonal relationship is impacted by the use of IM since it appears that adolescents use IM to improve their interpersonal relationships in real life.

Streamflow trends in the United States

USGS Publications Warehouse

Lins, H.F.; Slack, J.R.

1999-01-01

Secular trends in streamflow are evaluated for 395 climate-sensitive streamgaging stations in the conterminous United States using the non-parametric Mann-Kendall test. Trends are calculated for selected quantiles of discharge, from the 0th to the 100th percentile, to evaluate differences between low-, medium-, and high-flow regimes during the twentieth century. Two general patterns emerge; trends are most prevalent in the annual minimum (Q0) to median (Q50) flow categories and least prevalent in the annual maximum (Q100) category; and, at all but the highest quantiles, streamflow has increased across broad sections of the United States. Decreases appear only in parts of the Pacific Northwest and the Southeast. Systematic patterns are less apparent in the Q100 flow. Hydrologically, these results indicate that the conterminous U.S. is getting wetter, but less extreme.

The repeatability of mean defect with size III and size V standard automated perimetry.

PubMed

Wall, Michael; Doyle, Carrie K; Zamba, K D; Artes, Paul; Johnson, Chris A

2013-02-15

The mean defect (MD) of the visual field is a global statistical index used to monitor overall visual field change over time. Our goal was to investigate the relationship of MD and its variability for two clinically used strategies (Swedish Interactive Threshold Algorithm [SITA] standard size III and full threshold size V) in glaucoma patients and controls. We tested one eye, at random, for 46 glaucoma patients and 28 ocularly healthy subjects with Humphrey program 24-2 SITA standard for size III and full threshold for size V each five times over a 5-week period. The standard deviation of MD was regressed against the MD for the five repeated tests, and quantile regression was used to show the relationship of variability and MD. A Wilcoxon test was used to compare the standard deviations of the two testing methods following quantile regression. Both types of regression analysis showed increasing variability with increasing visual field damage. Quantile regression showed modestly smaller MD confidence limits. There was a 15% decrease in SD with size V in glaucoma patients (P = 0.10) and a 12% decrease in ocularly healthy subjects (P = 0.08). The repeatability of size V MD appears to be slightly better than size III SITA testing. When using MD to determine visual field progression, a change of 1.5 to 4 decibels (dB) is needed to be outside the normal 95% confidence limits, depending on the size of the stimulus and the amount of visual field damage.

Counterintuitive Effects of Online Feedback in Middle School Math: Results from a Randomized Controlled Trial in ASSISTments

ERIC Educational Resources Information Center

McGuire, Patrick; Tu, Shihfen; Logue, Mary Ellin; Mason, Craig A.; Ostrow, Korinn

2017-01-01

This study compared the effects of three different feedback formats provided to sixth grade mathematics students within a web-based online learning platform, ASSISTments. A sample of 196 students were randomly assigned to one of three conditions: (1) text-based feedback; (2) image-based feedback; and (3) correctness only feedback. Regardless of…

Qualitative and quantitative feedback in the context of competency-based education.

PubMed

Tekian, Ara; Watling, Christopher J; Roberts, Trudie E; Steinert, Yvonne; Norcini, John

2017-12-01

Research indicates the importance and usefulness of feedback, yet with the shift of medical curricula toward competencies, feedback is not well understood in this context. This paper attempts to identify how feedback fits within a competency-based curriculum. After careful consideration of the literature, the following conclusions are drawn: (1) Because feedback is predicated on assessment, the assessment should be designed to optimize and prevent inaccuracies in feedback; (2) Giving qualitative feedback in the form of a conversation would lend credibility to the feedback, address emotional obstacles and create a context in which feedback is comfortable; (3) Quantitative feedback in the form of individualized data could fulfill the demand for more feedback, help students devise strategies on how to improve, allow students to compare themselves to their peers, recognizing that big data have limitations; and (4) Faculty development needs to incorporate and promote cultural and systems changes with regard to feedback. A better understanding of the role of feedback in competency-based education could result in more efficient learning for students.

Exploring Patients' Views Toward Giving Web-Based Feedback and Ratings to General Practitioners in England: A Qualitative Descriptive Study.

PubMed

Patel, Salma; Cain, Rebecca; Neailey, Kevin; Hooberman, Lucy

2016-08-05

Patient feedback websites or doctor rating websites are increasingly being used by patients to give feedback about their health care experiences. There is little known about why patients in England may give Web-based feedback and what may motivate or dissuade them from giving Web-based feedback. The aim of this study was to explore patients' views toward giving Web-based feedback and ratings to general practitioners (GPs), within the context of other feedback methods available in primary care in England, and in particular, paper-based feedback cards. A descriptive exploratory qualitative approach using face-to-face semistructured interviews was used in this study. Purposive sampling was used to recruit 18 participants from different age groups in London and Coventry. Interviews were transcribed verbatim and analyzed using applied thematic analysis. Half of the participants in this study were not aware of the opportunity to leave feedback for GPs, and there was limited awareness about the methods available to leave feedback for a GP. The majority of participants were not convinced that formal patient feedback was needed by GPs or would be used by GPs for improvement, regardless of whether they gave it via a website or on paper. Some participants said or suggested that they may leave feedback on a website rather than on a paper-based feedback card for several reasons: because of the ability and ease of giving it remotely; because it would be shared with the public; and because it would be taken more seriously by GPs. Others, however, suggested that they would not use a website to leave feedback for the opposite reasons: because of accessibility issues; privacy and security concerns; and because they felt feedback left on a website may be ignored. Patient feedback and rating websites as they currently are will not replace other mechanisms for patients in England to leave feedback for a GP. Rather, they may motivate a small number of patients who have more altruistic motives or wish to place collective pressure on a GP to give Web-based feedback. If the National Health Service or GP practices want more patients to leave Web-based feedback, we suggest they first make patients aware that they can leave anonymous feedback securely on a website for a GP. They can then convince them that their feedback is needed and wanted by GPs for improvement, and that the reviews they leave on the website will be of benefit to other patients to decide which GP to see or which GP practice to join.

A sampling and classification item selection approach with content balancing.

PubMed

Chen, Pei-Hua

2015-03-01

Existing automated test assembly methods typically employ constrained combinatorial optimization. Constructing forms sequentially based on an optimization approach usually results in unparallel forms and requires heuristic modifications. Methods based on a random search approach have the major advantage of producing parallel forms sequentially without further adjustment. This study incorporated a flexible content-balancing element into the statistical perspective item selection method of the cell-only method (Chen et al. in Educational and Psychological Measurement, 72(6), 933-953, 2012). The new method was compared with a sequential interitem distance weighted deviation model (IID WDM) (Swanson & Stocking in Applied Psychological Measurement, 17(2), 151-166, 1993), a simultaneous IID WDM, and a big-shadow-test mixed integer programming (BST MIP) method to construct multiple parallel forms based on matching a reference form item-by-item. The results showed that the cell-only method with content balancing and the sequential and simultaneous versions of IID WDM yielded results comparable to those obtained using the BST MIP method. The cell-only method with content balancing is computationally less intensive than the sequential and simultaneous versions of IID WDM.

Eyewitness decisions in simultaneous and sequential lineups: a dual-process signal detection theory analysis.

PubMed

Meissner, Christian A; Tredoux, Colin G; Parker, Janat F; MacLin, Otto H

2005-07-01

Many eyewitness researchers have argued for the application of a sequential alternative to the traditional simultaneous lineup, given its role in decreasing false identifications of innocent suspects (sequential superiority effect). However, Ebbesen and Flowe (2002) have recently noted that sequential lineups may merely bring about a shift in response criterion, having no effect on discrimination accuracy. We explored this claim, using a method that allows signal detection theory measures to be collected from eyewitnesses. In three experiments, lineup type was factorially combined with conditions expected to influence response criterion and/or discrimination accuracy. Results were consistent with signal detection theory predictions, including that of a conservative criterion shift with the sequential presentation of lineups. In a fourth experiment, we explored the phenomenological basis for the criterion shift, using the remember-know-guess procedure. In accord with previous research, the criterion shift in sequential lineups was associated with a reduction in familiarity-based responding. It is proposed that the relative similarity between lineup members may create a context in which fluency-based processing is facilitated to a greater extent when lineup members are presented simultaneously.

Sequential accelerated tests: Improving the correlation of accelerated tests to module performance in the field

NASA Astrophysics Data System (ADS)

Felder, Thomas; Gambogi, William; Stika, Katherine; Yu, Bao-Ling; Bradley, Alex; Hu, Hongjie; Garreau-Iles, Lucie; Trout, T. John

2016-09-01

DuPont has been working steadily to develop accelerated backsheet tests that correlate with solar panels observations in the field. This report updates efforts in sequential testing. Single exposure tests are more commonly used and can be completed more quickly, and certain tests provide helpful predictions of certain backsheet failure modes. DuPont recommendations for single exposure tests are based on 25-year exposure levels for UV and humidity/temperature, and form a good basis for sequential test development. We recommend a sequential exposure of damp heat followed by UV then repetitions of thermal cycling and UVA. This sequence preserves 25-year exposure levels for humidity/temperature and UV, and correlates well with a large body of field observations. Measurements can be taken at intervals in the test, although the full test runs 10 months. A second, shorter sequential test based on damp heat and thermal cycling tests mechanical durability and correlates with loss of mechanical properties seen in the field. Ongoing work is directed toward shorter sequential tests that preserve good correlation to field data.

CADDIS Volume 4. Data Analysis: Basic Analyses

EPA Pesticide Factsheets

Use of statistical tests to determine if an observation is outside the normal range of expected values. Details of CART, regression analysis, use of quantile regression analysis, CART in causal analysis, simplifying or pruning resulting trees.

Comparing Explicit Exemplar-Based and Rule-Based Corrective Feedback: Introducing Analogy-Based Corrective Feedback

ERIC Educational Resources Information Center

Thomas, Kavita E.

2018-01-01

This study introduces an approach to providing corrective feedback to L2 learners termed analogy-based corrective feedback that is motivated by analogical learning theories and syntactic alignment in dialogue. Learners are presented with a structurally similar synonymous version of their output where the erroneous form is corrected, and they must…

Linking flood peak, flood volume and inundation extent: a DEM-based approach

NASA Astrophysics Data System (ADS)

Rebolho, Cédric; Furusho-Percot, Carina; Blaquière, Simon; Brettschneider, Marco; Andréassian, Vazken

2017-04-01

Traditionally, flood inundation maps are computed based on the Shallow Water Equations (SWE) in one or two dimensions, with various simplifications that have proved to give good results. However, the complexity of the SWEs often requires a numerical resolution which can need long computing time, as well as detailed cross section data: this often results in restricting these models to rather small areas abundant with high quality data. This, along with the necessity for fast inundation mapping, are the reason why rapid inundation models are being designed, working for (almost) any river with a minimum amount of data and, above all, easily available data. Our model tries to follow this path by using a 100m DEM over France from which are extracted a drainage network and the associated drainage areas. It is based on two pre-existing methods: (1) SHYREG (Arnaud et al.,2013), a regionalized approach used to calculate the 2-year and 10-year flood quantiles (used as approximated bankfull flow and maximum discharge, respectively) for each river pixel of the DEM (below a 10 000 km2 drainage area) and (2) SOCOSE (Mailhol,1980), which gives, amongst other things, an empirical formula of a characteristic flood duration (for each pixel) based on catchment area, average precipitation and temperature. An overflow volume for each river pixel is extracted from a triangular shaped synthetic hydrograph designed with SHYREG quantiles and SOCOSE flood duration. The volume is then spread from downstream to upstream one river pixel at a time. When the entire hydrographic network is processed, the model stops and generates a map of potential inundation area associated with the 10-year flood quantile. Our model can also be calibrated using past-events inundation maps by adjusting two parameters, one which modifies the overflow duration, and the other, equivalent to a minimum drainage area for river pixels to be flooded. Thus, in calibration on a sample of 42 basins, the first draft of the model showed a 0.51 median Fit (intersection of simulated and observed areas divided by the union of the two, Bates and De Roo, 2000) and a 0.74 maximum. Obviously, this approach is quite rough, and would require testing on events of homogeneous return periods (which is not the case for now). The next steps in the test and the development of our method include the use of the AIGA distributed model to simulate past-events hydrographs, the search for a new way to automatically approach bankfull flow and the integration of the results in our model to build dynamic maps of the flood. References Arnaud, P., Eglin, Y., Janet, B., and Payrastre, O. (2013). Notice utilisateur : bases de données SHYREG-Débit. Méthode - Performances - Limites. Bates, P. D. and De Roo, A. P. J. (2000). A simple raster-based model for flood inundation simulation. Journal of Hydrology, 236(1-2):54-77. Mailhol, J. (1980). Pour une approche plus réaliste du temps caractéristique de crues des bassins versants. In Actes du Colloque d'Oxford, volume 129, pages 229-237, Oxford. IAHS-AISH.

Saving lives: A meta-analysis of team training in healthcare.

PubMed

Hughes, Ashley M; Gregory, Megan E; Joseph, Dana L; Sonesh, Shirley C; Marlow, Shannon L; Lacerenza, Christina N; Benishek, Lauren E; King, Heidi B; Salas, Eduardo

2016-09-01

As the nature of work becomes more complex, teams have become necessary to ensure effective functioning within organizations. The healthcare industry is no exception. As such, the prevalence of training interventions designed to optimize teamwork in this industry has increased substantially over the last 10 years (Weaver, Dy, & Rosen, 2014). Using Kirkpatrick's (1956, 1996) training evaluation framework, we conducted a meta-analytic examination of healthcare team training to quantify its effectiveness and understand the conditions under which it is most successful. Results demonstrate that healthcare team training improves each of Kirkpatrick's criteria (reactions, learning, transfer, results; d = .37 to .89). Second, findings indicate that healthcare team training is largely robust to trainee composition, training strategy, and characteristics of the work environment, with the only exception being the reduced effectiveness of team training programs that involve feedback. As a tertiary goal, we proposed and found empirical support for a sequential model of healthcare team training where team training affects results via learning, which leads to transfer, which increases results. We find support for this sequential model in the healthcare industry (i.e., the current meta-analysis) and in training across all industries (i.e., using meta-analytic estimates from Arthur, Bennett, Edens, & Bell, 2003), suggesting the sequential benefits of training are not unique to medical teams. Ultimately, this meta-analysis supports the expanded use of team training and points toward recommendations for optimizing its effectiveness within healthcare settings. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Vocal Generalization Depends on Gesture Identity and Sequence

PubMed Central

Sober, Samuel J.

2014-01-01

Generalization, the brain's ability to transfer motor learning from one context to another, occurs in a wide range of complex behaviors. However, the rules of generalization in vocal behavior are poorly understood, and it is unknown how vocal learning generalizes across an animal's entire repertoire of natural vocalizations and sequences. Here, we asked whether generalization occurs in a nonhuman vocal learner and quantified its properties. We hypothesized that adaptive error correction of a vocal gesture produced in one sequence would generalize to the same gesture produced in other sequences. To test our hypothesis, we manipulated the fundamental frequency (pitch) of auditory feedback in Bengalese finches (Lonchura striata var. domestica) to create sensory errors during vocal gestures (song syllables) produced in particular sequences. As hypothesized, error-corrective learning on pitch-shifted vocal gestures generalized to the same gestures produced in other sequential contexts. Surprisingly, generalization magnitude depended strongly on sequential distance from the pitch-shifted syllables, with greater adaptation for gestures produced near to the pitch-shifted syllable. A further unexpected result was that nonshifted syllables changed their pitch in the direction opposite from the shifted syllables. This apparently antiadaptive pattern of generalization could not be explained by correlations between generalization and the acoustic similarity to the pitch-shifted syllable. These findings therefore suggest that generalization depends on the type of vocal gesture and its sequential context relative to other gestures and may reflect an advantageous strategy for vocal learning and maintenance. PMID:24741046

Properties of the Nucleo-Olivary Pathway: An In Vivo Whole-Cell Patch Clamp Study

PubMed Central

Bazzigaluppi, Paolo; Ruigrok, Tom; Saisan, Payam; De Zeeuw, Chris I.; de Jeu, Marcel

2012-01-01

The inferior olivary nucleus (IO) forms the gateway to the cerebellar cortex and receives feedback information from the cerebellar nuclei (CN), thereby occupying a central position in the olivo-cerebellar loop. Here, we investigated the feedback input from the CN to the IO in vivo in mice using the whole-cell patch-clamp technique. This approach allows us to study how the CN-feedback input is integrated with the activity of olivary neurons, while the olivo-cerebellar system and its connections are intact. Our results show how IO neurons respond to CN stimulation sequentially with: i) a short depolarization (EPSP), ii) a hyperpolarization (IPSP) and iii) a rebound depolarization. The latter two phenomena can also be evoked without the EPSPs. The IPSP is sensitive to a GABAA receptor blocker. The IPSP suppresses suprathreshold and subthreshold activity and is generated mainly by activation of the GABAA receptors. The rebound depolarization re-initiates and temporarily phase locks the subthreshold oscillations. Lack of electrotonical coupling does not affect the IPSP of individual olivary neurons, nor the sensitivity of its GABAA receptors to blockers. The GABAergic feedback input from the CN does not only temporarily block the transmission of signals through the IO, it also isolates neurons from the network by shunting the junction current and re-initiates the temporal pattern after a fixed time point. These data suggest that the IO not only functions as a cerebellar controlled gating device, but also operates as a pattern generator for controlling motor timing and/or learning. PMID:23029495

Automated Microfluidic Platform for Serial Polymerase Chain Reaction and High-Resolution Melting Analysis.

PubMed

Cao, Weidong; Bean, Brian; Corey, Scott; Coursey, Johnathan S; Hasson, Kenton C; Inoue, Hiroshi; Isano, Taisuke; Kanderian, Sami; Lane, Ben; Liang, Hongye; Murphy, Brian; Owen, Greg; Shinoda, Nobuhiko; Zeng, Shulin; Knight, Ivor T

2016-06-01

We report the development of an automated genetic analyzer for human sample testing based on microfluidic rapid polymerase chain reaction (PCR) with high-resolution melting analysis (HRMA). The integrated DNA microfluidic cartridge was used on a platform designed with a robotic pipettor system that works by sequentially picking up different test solutions from a 384-well plate, mixing them in the tips, and delivering mixed fluids to the DNA cartridge. A novel image feedback flow control system based on a Canon 5D Mark II digital camera was developed for controlling fluid movement through a complex microfluidic branching network without the use of valves. The same camera was used for measuring the high-resolution melt curve of DNA amplicons that were generated in the microfluidic chip. Owing to fast heating and cooling as well as sensitive temperature measurement in the microfluidic channels, the time frame for PCR and HRMA was dramatically reduced from hours to minutes. Preliminary testing results demonstrated that rapid serial PCR and HRMA are possible while still achieving high data quality that is suitable for human sample testing. © 2015 Society for Laboratory Automation and Screening.

Conversion of rice straw to bio-based chemicals: an integrated process using Lactobacillus brevis.

PubMed

Kim, Jae-Han; Block, David E; Shoemaker, Sharon P; Mills, David A

2010-05-01

Commercialization of lignocellulosic biomass as a feedstock for bio-based chemical production is problematic due to the high processing costs of pretreatment and saccharifying enzymes combined with low product yields. Such low product yield can be attributed, in large part, to the incomplete utilization of the various carbohydrate sugars found in the lignocellulosic biomass. In this study, we demonstrate that Lactobacillus brevis is able to simultaneously metabolize all fermentable carbohydrates in acid pre-processed rice straw hydrolysate, thereby allowing complete utilization of all released sugars. Inhibitors present in rice straw hydrolysate did not affect lactic acid production. Moreover, the activity of exogenously added cellulases was not reduced in the presence of growing cultures of L. brevis. These factors enabled the use of L. brevis in a process termed simultaneous saccharification and mixed sugar fermentation (SSMSF). In SSMSF with L. brevis, sugars present in rice straw hydrolysate were completely utilized while the cellulase maintained its maximum activity due to the lack of feedback inhibition from glucose and/or cellobiose. By comparison to a sequential hydrolysis and fermentation process, SSMSF reduced operation time and the amount of cellulase enzyme necessary to produce the same amount of lactic acid.

Sequential and simultaneous SLAR block adjustment. [spline function analysis for mapping

NASA Technical Reports Server (NTRS)

Leberl, F.

1975-01-01

Two sequential methods of planimetric SLAR (Side Looking Airborne Radar) block adjustment, with and without splines, and three simultaneous methods based on the principles of least squares are evaluated. A limited experiment with simulated SLAR images indicates that sequential block formation with splines followed by external interpolative adjustment is superior to the simultaneous methods such as planimetric block adjustment with similarity transformations. The use of the sequential block formation is recommended, since it represents an inexpensive tool for satisfactory point determination from SLAR images.

Future extreme water levels and floodplains in Gironde Estuary considering climate change

NASA Astrophysics Data System (ADS)

Laborie, V.; Hissel, F.; Sergent, P.

2012-04-01

Within THESEUS European project, an overflowing model of Gironde Estuary has been used to evaluate future surge levels at Le Verdon and future water levels at 6 specific sites of the estuary : le Verdon, Richard, Laména, Pauillac, Le Marquis and Bordeaux. It was then used to study the evolution of floodplains' location and areas towards 2100 in the entire Estuary. In this study, no breaching and no modification in the elevation of the dike was considered. The model was fed by several data sources : wind fields at Royan and Mérignac interpolated from the grid of the European Climatolologic Model CLM/SGA, a tide signal at Le Verdon, the discharges of Garonne (at La Réole), the Dordogne (at Pessac) and Isle (at Libourne). A simplified mathematical model of surge levels has been adjusted at Le Verdon with 10 surge storms and by using wind and pressure fields given by CLM/SGA. This adjustment was led so that the statistical analysis of the global signal at Le Verdon gives the same quantiles as the same analysis driven on maregraphic observations for the period [1960 ; 2000]. The assumption used for sea level rise was the pessimistic one of the French national institute for climate change: 60 cm in 2100. The model was then used to study the evolution of extreme water levels towards 2100. The analysis of surge levels at Le Verdon shows a decrease in quantiles which is coherent with the analysis of climatologic fields. The analysis of water levels shows that the increase in mean water levels quantiles represents only a part of sea level rise in Gironde Estuary. Moreover this effect seems to decrease from the maritime limit of the model towards upstream. Concerning floodplains, those corresponding to return periods from 2 to 100 years for present conditions and 3 slices [2010; 2039], [2040; 2069] and [2070; 2099] have been mapped for 3 areas in Gironde Estuary : around Le Verdon, at the confluence between Garonne and Dordogne, and near Bordeaux. Concerning the evolution of floodplains in Gironde Estuary, taking into account IPCC scenario A1B, under the same assumptions, it appears that the impact of the climate change on the quantiles of water levels in floodplains depends on the sea level rise over the period considered ([2010; 2039], [2040; 2069], [2070; 2099]) and that areas which are not flooded today for weak return periods become submerged towards 2100. The neighborhood of Le Verdon undergoes a negative impact only in the medium and long term. For the period [2010; 2039], a small reduction of floodplains can be observed in quantiles of water levels for all return periods. Under those assumptions, in the area of Bordeaux, significant effects would be felt along the road RN230 towards 2100. The effects of the discharges and dike breaching will have to be studied in order to precise these results.

Exploring Patients’ Views Toward Giving Web-Based Feedback and Ratings to General Practitioners in England: A Qualitative Descriptive Study

PubMed Central

Cain, Rebecca; Neailey, Kevin; Hooberman, Lucy

2016-01-01

Background Patient feedback websites or doctor rating websites are increasingly being used by patients to give feedback about their health care experiences. There is little known about why patients in England may give Web-based feedback and what may motivate or dissuade them from giving Web-based feedback. Objective The aim of this study was to explore patients’ views toward giving Web-based feedback and ratings to general practitioners (GPs), within the context of other feedback methods available in primary care in England, and in particular, paper-based feedback cards. Methods A descriptive exploratory qualitative approach using face-to-face semistructured interviews was used in this study. Purposive sampling was used to recruit 18 participants from different age groups in London and Coventry. Interviews were transcribed verbatim and analyzed using applied thematic analysis. Results Half of the participants in this study were not aware of the opportunity to leave feedback for GPs, and there was limited awareness about the methods available to leave feedback for a GP. The majority of participants were not convinced that formal patient feedback was needed by GPs or would be used by GPs for improvement, regardless of whether they gave it via a website or on paper. Some participants said or suggested that they may leave feedback on a website rather than on a paper-based feedback card for several reasons: because of the ability and ease of giving it remotely; because it would be shared with the public; and because it would be taken more seriously by GPs. Others, however, suggested that they would not use a website to leave feedback for the opposite reasons: because of accessibility issues; privacy and security concerns; and because they felt feedback left on a website may be ignored. Conclusions Patient feedback and rating websites as they currently are will not replace other mechanisms for patients in England to leave feedback for a GP. Rather, they may motivate a small number of patients who have more altruistic motives or wish to place collective pressure on a GP to give Web-based feedback. If the National Health Service or GP practices want more patients to leave Web-based feedback, we suggest they first make patients aware that they can leave anonymous feedback securely on a website for a GP. They can then convince them that their feedback is needed and wanted by GPs for improvement, and that the reviews they leave on the website will be of benefit to other patients to decide which GP to see or which GP practice to join. PMID:27496366

Effects of computer-based immediate feedback on foreign language listening comprehension and test-associated anxiety.

PubMed

Lee, Shu-Ping; Su, Hui-Kai; Lee, Shin-Da

2012-06-01

This study investigated the effects of immediate feedback on computer-based foreign language listening comprehension tests and on intrapersonal test-associated anxiety in 72 English major college students at a Taiwanese University. Foreign language listening comprehension of computer-based tests designed by MOODLE, a dynamic e-learning environment, with or without immediate feedback together with the state-trait anxiety inventory (STAI) were tested and repeated after one week. The analysis indicated that immediate feedback during testing caused significantly higher anxiety and resulted in significantly higher listening scores than in the control group, which had no feedback. However, repeated feedback did not affect the test anxiety and listening scores. Computer-based immediate feedback did not lower debilitating effects of anxiety but enhanced students' intrapersonal eustress-like anxiety and probably improved their attention during listening tests. Computer-based tests with immediate feedback might help foreign language learners to increase attention in foreign language listening comprehension.

Connection stiffness and dynamical docking process of flux pinned spacecraft modules

NASA Astrophysics Data System (ADS)

Lu, Yong; Zhang, Mingliang; Gao, Dong

2014-02-01

This paper describes a novel kind of potential flux pinned docking system that consists of guidance navigation and control system, the traditional extrusion type propulsion system, and a flux pinned docking interface. Because of characteristics of passive stability of flux pinning, the docking control strategy of flux pinned docking system only needs a series of sequential control rather than necessary active feedback control, as well as avoidance of hazardous collision accident. The flux pinned force between YBaCuO (YBCO) high temperature superconductor bulk and permanent magnet is able to be given vent based on the identical current loop model and improved image dipole model, which can be validated experimentally. Thus, the connection stiffness between two flux pinned spacecraft modules can be calculated based on Hooke's law. This connection stiffness matrix at the equilibrium position has the positive definite performance, which can validate the passively stable connection of two flux pinned spacecraft modules theoretically. Furthermore, the relative orbital dynamical equation of two flux pinned spacecraft modules can be established based on Clohessy-Wiltshire's equations and improved image dipole model. The dynamical docking process between two flux pinned spacecraft modules can be obtained by way of numerical simulation, which suggests the feasibility of flux pinned docking system.

Connection stiffness and dynamical docking process of flux pinned spacecraft modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Yong; Zhang, Mingliang, E-mail: niudun12@126.com; Gao, Dong

2014-02-14

This paper describes a novel kind of potential flux pinned docking system that consists of guidance navigation and control system, the traditional extrusion type propulsion system, and a flux pinned docking interface. Because of characteristics of passive stability of flux pinning, the docking control strategy of flux pinned docking system only needs a series of sequential control rather than necessary active feedback control, as well as avoidance of hazardous collision accident. The flux pinned force between YBaCuO (YBCO) high temperature superconductor bulk and permanent magnet is able to be given vent based on the identical current loop model and improvedmore » image dipole model, which can be validated experimentally. Thus, the connection stiffness between two flux pinned spacecraft modules can be calculated based on Hooke's law. This connection stiffness matrix at the equilibrium position has the positive definite performance, which can validate the passively stable connection of two flux pinned spacecraft modules theoretically. Furthermore, the relative orbital dynamical equation of two flux pinned spacecraft modules can be established based on Clohessy-Wiltshire's equations and improved image dipole model. The dynamical docking process between two flux pinned spacecraft modules can be obtained by way of numerical simulation, which suggests the feasibility of flux pinned docking system.« less

Effects of Web-Based Feedback on Students' Learning

ERIC Educational Resources Information Center

van Kol, Simone; Rietz, Christian

2016-01-01

Feedback plays an important role in supporting students' learning process. Nonetheless, providing feedback is still rather unusual in higher education. Moreover, research on the design of ideal feedback as well as its effects is rare. In order to contribute to the development of this field, a web-based feedback system was implemented in a lecture…

Hybrid Computerized Adaptive Testing: From Group Sequential Design to Fully Sequential Design

ERIC Educational Resources Information Center

Wang, Shiyu; Lin, Haiyan; Chang, Hua-Hua; Douglas, Jeff

2016-01-01

Computerized adaptive testing (CAT) and multistage testing (MST) have become two of the most popular modes in large-scale computer-based sequential testing. Though most designs of CAT and MST exhibit strength and weakness in recent large-scale implementations, there is no simple answer to the question of which design is better because different…

Confirmatory Analysis of Simultaneous, Sequential, and Achievement Factors on the K-ABC at 11 Age Levels Ranging from 2 1/2 to 12 1/2 years.

ERIC Educational Resources Information Center

Willson, Victor L.; And Others

1985-01-01

Presents results of confirmatory factor analysis of the Kaufman Assessment Battery for children which is based on the underlying theoretical model of sequential, simultaneous, and achievement factors. Found support for the two-factor, simultaneous and sequential processing model. (MCF)