Binary Logistic Regression Analysis for Detecting Differential Item Functioning: Effectiveness of R[superscript 2] and Delta Log Odds Ratio Effect Size Measures

ERIC Educational Resources Information Center

Hidalgo, Mª Dolores; Gómez-Benito, Juana; Zumbo, Bruno D.

2014-01-01

The authors analyze the effectiveness of the R[superscript 2] and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing…

Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm

PubMed Central

Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui

2016-01-01

Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365

Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

PubMed

Xie, Yanmei; Zhang, Biao

2017-04-20

Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and Nutrition Examination Survey (NHANES).

No Evidence of Reaction Time Slowing in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Ferraro, F. Richard

2016-01-01

A total of 32 studies comprising 238 simple reaction time and choice reaction time conditions were examined in individuals with autism spectrum disorder (n?=?964) and controls (n?=?1032). A Brinley plot/multiple regression analysis was performed on mean reaction times, regressing autism spectrum disorder performance onto the control performance as…

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.…

Logarithmic Transformations in Regression: Do You Transform Back Correctly?

ERIC Educational Resources Information Center

Dambolena, Ismael G.; Eriksen, Steven E.; Kopcso, David P.

2009-01-01

The logarithmic transformation is often used in regression analysis for a variety of purposes such as the linearization of a nonlinear relationship between two or more variables. We have noticed that when this transformation is applied to the response variable, the computation of the point estimate of the conditional mean of the original response…

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Racial/ethnic and educational differences in the estimated odds of recent nitrite use among adult household residents in the United States: an illustration of matching and conditional logistic regression.

PubMed

Delva, J; Spencer, M S; Lin, J K

2000-01-01

This article compares estimates of the relative odds of nitrite use obtained from weighted unconditional logistic regression with estimates obtained from conditional logistic regression after post-stratification and matching of cases with controls by neighborhood of residence. We illustrate these methods by comparing the odds associated with nitrite use among adults of four racial/ethnic groups, with and without a high school education. We used aggregated data from the 1994-B through 1996 National Household Survey on Drug Abuse (NHSDA). Difference between the methods and implications for analysis and inference are discussed.

Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium.

PubMed

Ngo, Long H; Inouye, Sharon K; Jones, Richard N; Travison, Thomas G; Libermann, Towia A; Dillon, Simon T; Kuchel, George A; Vasunilashorn, Sarinnapha M; Alsop, David C; Marcantonio, Edward R

2017-06-06

The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy-whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Impact of clinical status and salivary conditions on xerostomia and oral health-related quality of life of adolescents with type 1 diabetes mellitus.

PubMed

Busato, Ivana Maria Saes; Ignácio, Sérgio Aparecido; Brancher, João Armando; Moysés, Simone Tetu; Azevedo-Alanis, Luciana Reis

2012-02-01

To investigate the influence of clinical status and salivary conditions on the presence of xerostomia on adolescents with and without type 1 diabetes mellitus (DM1), and further to investigate the influence of clinical status, salivary conditions and xerostomia on oral health-related quality of life (OHQoL) of those with DM1. A cross-sectional study was performed on 102 adolescents, 51 with DM1 and 51 nondiabetics. Xerostomia was detected by asking a question about the sensation of having 'dry mouth', and Oral Health Impact Profile-14 was used to measure the impact of xerostomia on OHQoL. The clinical status was assessed by using decayed, missing or filled and Community Periodontal indices, and by evaluating oral manifestations; and the following salivary conditions were evaluated: stimulated salivary flow, pH, buffer capacity, total protein, amylase, urea, calcium, and glucose salivary concentrations. Multiple logistic regression analysis was used to evaluate the influence of clinical status and salivary conditions on xerostomia and the impact of xerostomia on the OHQoL of adolescents with DM1. Clinical status and salivary conditions was shown to have no influence on the presence of xerostomia. Bivariate (P = 0.00) and logistic regression (P = 0.01) analysis showed a significant association between DM1 and xerostomia. Logistic regression analysis showed association between xerostomia (P = 0.00) and OHQoL, and caries experience (P = 0.03) and OHQoL. DM1 showed to be predictive of a high prevalence of xerostomia in adolescents. Caries experience and xerostomia showed to have a negative impact on the OHQoL of adolescents with DM1. © 2011 John Wiley & Sons A/S.

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.

Estimating current and future streamflow characteristics at ungaged sites, central and eastern Montana, with application to evaluating effects of climate change on fish populations

USGS Publications Warehouse

Sando, Roy; Chase, Katherine J.

2017-03-23

A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.

Flight display dynamics and compensatory head movements in pilots.

PubMed

Beer, Jeremy; Freeman, David

2007-06-01

Experiments measured the optokinetic cervical reflex (OKCR), wherein the banking pilot aligns the head with the horizon. In a synthetic cockpit, the flight display was manipulated to test whether changing the visual reference frame would alter OKCR. Eight subjects (five rated pilots) flew a route in simulated visual meteorological conditions that required them to bank the aircraft frequently. Pilots' head tilt was characterized using both the conventional method of regressing against simultaneous aircraft bank, and also an event-based analysis, which identified head movements before, during, and after each turn. Three display configurations were compared to determine whether pilots' orientation would ever migrate from the horizon to the aircraft symbol. The first was a conventional "Inside-Out" condition. A "Frequency-Separated" condition combined Inside-Out horizon geometry with Outside-In dynamics for the aircraft symbol, which depicted joystick bank inputs. In the "Outside-In" condition, the aircraft symbol rolled against a static horizon. Regressions identified an interaction (p < 0.001) between display condition and aircraft bank: head tilt followed horizon tilt in Inside-Out and Frequency-Separated conditions, while remaining mostly level in the Outside-In condition. The event-based analysis identified anticipatory head movements in Inside-Out and Frequency-Separated conditions: 95% CI indicated that before each turn, head tilt favored the direction of the imminent bank. While the conventional analysis confirmed that the horizon comprises a primary spatial reference, the finer-grained event-based analysis indicated that pilots' reference can migrate at least temporarily to the vehicle, and that OKCR can be preceded by anticipatory head movements in the opposite direction.

Innovating patient care delivery: DSRIP's interrupted time series analysis paradigm.

PubMed

Shenoy, Amrita G; Begley, Charles E; Revere, Lee; Linder, Stephen H; Daiger, Stephen P

2017-12-08

Adoption of Medicaid Section 1115 waiver is one of the many ways of innovating healthcare delivery system. The Delivery System Reform Incentive Payment (DSRIP) pool, one of the two funding pools of the waiver has four categories viz. infrastructure development, program innovation and redesign, quality improvement reporting and lastly, bringing about population health improvement. A metric of the fourth category, preventable hospitalization (PH) rate was analyzed in the context of eight conditions for two time periods, pre-reporting years (2010-2012) and post-reporting years (2013-2015) for two hospital cohorts, DSRIP participating and non-participating hospitals. The study explains how DSRIP impacted Preventable Hospitalization (PH) rates of eight conditions for both hospital cohorts within two time periods. Eight PH rates were regressed as the dependent variable with time, intervention and post-DSRIP Intervention as independent variables. PH rates of eight conditions were then consolidated into one rate for regressing with the above independent variables to evaluate overall impact of DSRIP. An interrupted time series regression was performed after accounting for auto-correlation, stationarity and seasonality in the dataset. In the individual regression model, PH rates showed statistically significant coefficients for seven out of eight conditions in DSRIP participating hospitals. In the combined regression model, the coefficient of the PH rate showed a statistically significant decrease with negative p-values for regression coefficients in DSRIP participating hospitals compared to positive/increased p-values for regression coefficients in DSRIP non-participating hospitals. Several macro- and micro-level factors may have likely contributed DSRIP hospitals outperforming DSRIP non-participating hospitals. Healthcare organization/provider collaboration, support from healthcare professionals, DSRIP's design, state reimbursement and coordination in care delivery methods may have led to likely success of DSRIP. IV, a retrospective cohort study based on longitudinal data. Copyright © 2017 Elsevier Inc. All rights reserved.

Influence of storage conditions on the stability of monomeric anthocyanins studied by reversed-phase high-performance liquid chromatography.

PubMed

Morais, Helena; Ramos, Cristina; Forgács, Esther; Cserháti, Tibor; Oliviera, José

2002-04-25

The effect of light, storage time and temperature on the decomposition rate of monomeric anthocyanin pigments extracted from skins of grape (Vitis vinifera var. Red globe) was determined by reversed-phase high-performance liquid chromatography (RP-HPLC). The impact of various storage conditions on the pigment stability was assessed by stepwise regression analysis. RP-HPLC separated well the five anthocyanins identified and proved the presence of other unidentified pigments at lower concentrations. Stepwise regression analysis confirmed that the overall decomposition rate of monomeric anthocyanins, peonidin-3-glucoside and malvidin-3-glucoside significantly depended on the time and temperature of storage, the effect of storage time being the most important. The presence or absence of light exerted a negligible impact on the decomposition rate.

Application of logistic regression to case-control association studies involving two causative loci.

PubMed

North, Bernard V; Curtis, David; Sham, Pak C

2005-01-01

Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci. We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci.

Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.

ERIC Educational Resources Information Center

Olson, Jeffery E.

Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

Successful Indicators Study (SIS) Methodology Report: Deviant Case Analysis Pilot.

ERIC Educational Resources Information Center

Bailey, Jerry; Hafner, Anne

A deviant case analysis pilot study analyzed California local education agency data to determine the usefulness of regression analysis in predicting change in achievement from 1984 to 1989 and identified outliers or districts that show greater achievement changes than would be expected given changed demographic conditions. This report on the…

The Effect of Multicollinearity and the Violation of the Assumption of Normality on the Testing of Hypotheses in Regression Analysis.

ERIC Educational Resources Information Center

Vasu, Ellen S.; Elmore, Patricia B.

The effects of the violation of the assumption of normality coupled with the condition of multicollinearity upon the outcome of testing the hypothesis Beta equals zero in the two-predictor regression equation is investigated. A monte carlo approach was utilized in which three differenct distributions were sampled for two sample sizes over…

Further comments on sensitivities, parameter estimation, and sampling design in one-dimensional analysis of solute transport in porous media

USGS Publications Warehouse

Knopman, Debra S.; Voss, Clifford I.

1988-01-01

Sensitivities of solute concentration to parameters associated with first-order chemical decay, boundary conditions, initial conditions, and multilayer transport are examined in one-dimensional analytical models of transient solute transport in porous media. A sensitivity is a change in solute concentration resulting from a change in a model parameter. Sensitivity analysis is important because minimum information required in regression on chemical data for the estimation of model parameters by regression is expressed in terms of sensitivities. Nonlinear regression models of solute transport were tested on sets of noiseless observations from known models that exceeded the minimum sensitivity information requirements. Results demonstrate that the regression models consistently converged to the correct parameters when the initial sets of parameter values substantially deviated from the correct parameters. On the basis of the sensitivity analysis, several statements may be made about design of sampling for parameter estimation for the models examined: (1) estimation of parameters associated with solute transport in the individual layers of a multilayer system is possible even when solute concentrations in the individual layers are mixed in an observation well; (2) when estimating parameters in a decaying upstream boundary condition, observations are best made late in the passage of the front near a time chosen by adding the inverse of an hypothesized value of the source decay parameter to the estimated mean travel time at a given downstream location; (3) estimation of a first-order chemical decay parameter requires observations to be made late in the passage of the front, preferably near a location corresponding to a travel time of √2 times the half-life of the solute; and (4) estimation of a parameter relating to spatial variability in an initial condition requires observations to be made early in time relative to passage of the solute front.

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Meta-regression approximations to reduce publication selection bias.

PubMed

Stanley, T D; Doucouliagos, Hristos

2014-03-01

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy. Copyright © 2013 John Wiley & Sons, Ltd.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Combination of a Stresor-Response Model with a Conditional Probability Anaylsis Approach to Develop Candidate Criteria from Empirical Data

EPA Science Inventory

We show that a conditional probability analysis that utilizes a stressor-response model based on a logistic regression provides a useful approach for developing candidate water quality criterai from empirical data. The critical step in this approach is transforming the response ...

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

Multi Objective Optimization of Multi Wall Carbon Nanotube Based Nanogrinding Wheel Using Grey Relational and Regression Analysis

NASA Astrophysics Data System (ADS)

Sethuramalingam, Prabhu; Vinayagam, Babu Kupusamy

2016-07-01

Carbon nanotube mixed grinding wheel is used in the grinding process to analyze the surface characteristics of AISI D2 tool steel material. Till now no work has been carried out using carbon nanotube based grinding wheel. Carbon nanotube based grinding wheel has excellent thermal conductivity and good mechanical properties which are used to improve the surface finish of the workpiece. In the present study, the multi response optimization of process parameters like surface roughness and metal removal rate of grinding process of single wall carbon nanotube (CNT) in mixed cutting fluids is undertaken using orthogonal array with grey relational analysis. Experiments are performed with designated grinding conditions obtained using the L9 orthogonal array. Based on the results of the grey relational analysis, a set of optimum grinding parameters is obtained. Using the analysis of variance approach the significant machining parameters are found. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared empirically, for conditions of with and without CNT grinding wheel in grinding process.

Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.

PubMed

Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty

2014-06-17

The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.

Microhabitat analysis using radiotelemetry locations and polytomous logistic regression

Treesearch

Malcolm P. North; Joel H. Reynolds

1996-01-01

Microhabitat analyses often use discriminant function analysis (DFA) to compare vegetation structures or environmental conditions between sites classified by a study animal's presence or absence. These presence/absence studies make questionable assumptions about the habitat value of the comparison sites and the microhabitat data often violate the DFA's...

Principal regression analysis and the index leverage effect

NASA Astrophysics Data System (ADS)

Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe

2011-09-01

We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.

The Health-Related Quality of Life for Patients with Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS).

PubMed

Falk Hvidberg, Michael; Brinth, Louise Schouborg; Olesen, Anne V; Petersen, Karin D; Ehlers, Lars

2015-01-01

Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) is a common, severe condition affecting 0.2 to 0.4 per cent of the population. Even so, no recent international EQ-5D based health-related quality of life (HRQoL) estimates exist for ME/CFS patients. The main purpose of this study was to estimate HRQoL scores using the EQ-5D-3L with Danish time trade-off tariffs. Secondary, the aims were to explore whether the results are not influenced by other conditions using regression, to compare the estimates to 20 other conditions and finally to present ME/CFS patient characteristics for use in clinical practice. All members of the Danish ME/CFS Patient Association in 2013 (n=319) were asked to fill out a questionnaire including the EQ-5D-3L. From these, 105 ME/CFS patients were identified and gave valid responses. Unadjusted EQ-5D-3L means were calculated and compared to the population mean as well as to the mean of 20 other conditions. Furthermore, adjusted estimates were calculated using ordinary least squares (OLS) regression, adjusting for gender, age, education, and co-morbidity of 18 self-reported conditions. Data from the North Denmark Health Profile 2010 was used as population reference in the regression analysis (n=23,392). The unadjusted EQ-5D-3L mean of ME/CFS was 0.47 [0.41-0.53] compared to a population mean of 0.85 [0.84-0.86]. The OLS regression estimated a disutility of -0.29 [-0.21;-0.34] for ME/CFS patients in this study. The characteristics of ME/CFS patients are different from the population with respect to gender, relationship, employment etc. The EQ-5D-3L-based HRQoL of ME/CFS is significantly lower than the population mean and the lowest of all the compared conditions. The adjusted analysis confirms that poor HRQoL of ME/CFS is distinctly different from and not a proxy of the other included conditions. However, further studies are needed to exclude the possible selection bias of the current study.

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

PubMed

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

Factors associated with variation in financial condition among voluntary hospitals.

PubMed Central

Brecher, C; Nesbitt, S

1985-01-01

This article uses multiple regression analysis to identify factors which affect variations in the financial condition of voluntary hospitals in New York State. Six separate ratios are used to measure financial condition and 18 independent variables are considered. The factors affecting financial conditions were found to vary among dimensions of financial health, and different causal relationships were evident among hospitals in New York City than among those in the rest of the state. PMID:4019212

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Spatial-temporal event detection in climate parameter imagery.

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

McKenna, Sean Andrew; Gutierrez, Karen A.

Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to themore » earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.« less

Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation.

PubMed

Hayes, Andrew F; Rockwood, Nicholas J

2017-11-01

There have been numerous treatments in the clinical research literature about various design, analysis, and interpretation considerations when testing hypotheses about mechanisms and contingencies of effects, popularly known as mediation and moderation analysis. In this paper we address the practice of mediation and moderation analysis using linear regression in the pages of Behaviour Research and Therapy and offer some observations and recommendations, debunk some popular myths, describe some new advances, and provide an example of mediation, moderation, and their integration as conditional process analysis using the PROCESS macro for SPSS and SAS. Our goal is to nudge clinical researchers away from historically significant but increasingly old school approaches toward modifications, revisions, and extensions that characterize more modern thinking about the analysis of the mechanisms and contingencies of effects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Digital Games, Design, and Learning: A Systematic Review and Meta-Analysis

ERIC Educational Resources Information Center

Clark, Douglas B.; Tanner-Smith, Emily E.; Killingsworth, Stephen S.

2016-01-01

In this meta-analysis, we systematically reviewed research on digital games and learning for K-16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust…

Social support at work and affective commitment to the organization: the moderating effect of job resource adequacy and ambient conditions.

PubMed

Rousseau, Vincent; Aubé, Caroline

2010-01-01

This study investigated whether both supervisor and coworker support may be positively related to affective commitment to the organization on one hand; and on the other hand, it examined the moderating effect of job resource adequacy and ambient conditions on these relationships. The sample included 215 participants working within a health care organization. Results of regression analysis showed that supervisor and coworker support have an additive effect on affective commitment. Hierarchical regression analyses indicated that supervisor and coworker support are more strongly related to affective commitment when job resource adequacy is high. Furthermore, ambient conditions moderate the relationship between supervisor support and affective commitment in such a way that favorable ambient conditions strengthen this relationship. Overall, these findings reinforce the importance of taking into account contingent factors in the study of antecedents of affective commitment to the organization.

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

Ridge Regression Signal Processing

NASA Technical Reports Server (NTRS)

Kuhl, Mark R.

1990-01-01

The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

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.

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

Estimating population prevalence of psychiatric conditions by small area with applications to analysing outcome and referral variations.

PubMed

Congdon, Peter

2006-12-01

This paper considers the development of estimates of mental illness prevalence for small areas and applications in explaining psychiatric outcomes and in assessing service provision. Estimates of prevalence are based on a logistic regression analysis of two national studies that provides model based estimates of relative morbidity risk by demographic, socio-economic and ethnic group for major psychiatric conditions; household/marital and area status also figure in the regression. Relative risk estimates are used, along with suitably disaggregated census populations, to make prevalence estimates for 354 English local authorities (LAs). Two applications are considered: the first involves analysis of variations in schizophrenia referrals and suicide mortality over English LAs that takes account of prevalence differences, and the second involves assessing hospital referral and bed use in relation to prevalence (for ages 16-74) for a case study area, Waltham Forest in NE London.

Analysis of the streamflow-gaging station network in Ohio for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Straub, D.E.

1998-01-01

The streamflow-gaging station network in Ohio was evaluated for its effectiveness in providing regional streamflow information. The analysis involved application of the principles of generalized least squares regression between streamflow and climatic and basin characteristics. Regression equations were developed for three flow characteristics: (1) the instantaneous peak flow with a 100-year recurrence interval (P100), (2) the mean annual flow (Qa), and (3) the 7-day, 10-year low flow (7Q10). All active and discontinued gaging stations with 5 or more years of unregulated-streamflow data with respect to each flow characteristic were used to develop the regression equations. The gaging-station network was evaluated for the current (1996) condition of the network and estimated conditions of various network strategies if an additional 5 and 20 years of streamflow data were collected. Any active or discontinued gaging station with (1) less than 5 years of unregulated-streamflow record, (2) previously defined basin and climatic characteristics, and (3) the potential for collection of more unregulated-streamflow record were included in the network strategies involving the additional 5 and 20 years of data. The network analysis involved use of the regression equations, in combination with location, period of record, and cost of operation, to determine the contribution of the data for each gaging station to regional streamflow information. The contribution of each gaging station was based on a cost-weighted reduction of the mean square error (average sampling-error variance) associated with each regional estimating equation. All gaging stations included in the network analysis were then ranked according to their contribution to the regional information for each flow characteristic. The predictive ability of the regression equations developed from the gaging station network could be improved for all three flow characteristics with the collection of additional streamflow data. The addition of new gaging stations to the network would result in an even greater improvement of the accuracy of the regional regression equations. Typically, continued data collection at stations with unregulated streamflow for all flow conditions that had less than 11 years of record with drainage areas smaller than 200 square miles contributed the largest cost-weighted reduction to the average sampling-error variance of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active gaging stations or the reactivation of discontinued gaging stations if the objective is to maximize the regional information content in the streamflow-gaging station network.

A general regression framework for a secondary outcome in case-control studies.

PubMed

Tchetgen Tchetgen, Eric J

2014-01-01

Modern case-control studies typically involve the collection of data on a large number of outcomes, often at considerable logistical and monetary expense. These data are of potentially great value to subsequent researchers, who, although not necessarily concerned with the disease that defined the case series in the original study, may want to use the available information for a regression analysis involving a secondary outcome. Because cases and controls are selected with unequal probability, regression analysis involving a secondary outcome generally must acknowledge the sampling design. In this paper, the author presents a new framework for the analysis of secondary outcomes in case-control studies. The approach is based on a careful re-parameterization of the conditional model for the secondary outcome given the case-control outcome and regression covariates, in terms of (a) the population regression of interest of the secondary outcome given covariates and (b) the population regression of the case-control outcome on covariates. The error distribution for the secondary outcome given covariates and case-control status is otherwise unrestricted. For a continuous outcome, the approach sometimes reduces to extending model (a) by including a residual of (b) as a covariate. However, the framework is general in the sense that models (a) and (b) can take any functional form, and the methodology allows for an identity, log or logit link function for model (a).

Practical Guidance for Conducting Mediation Analysis With Multiple Mediators Using Inverse Odds Ratio Weighting

PubMed Central

Nguyen, Quynh C.; Osypuk, Theresa L.; Schmidt, Nicole M.; Glymour, M. Maria; Tchetgen Tchetgen, Eric J.

2015-01-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994–2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. PMID:25693776

Investigation of pajama properties on skin under mild cold conditions: the interaction between skin and clothing.

PubMed

Yao, Lei; Gohel, Mayur D I; Li, Yi; Chung, Waiyee J

2011-07-01

Clothing is considered the second skin of the human body. The aim of this study was to determine clothing-wearer interaction on skin physiology under mild cold conditions. Skin physiological parameters, subjective sensory response, stress level, and physical properties of clothing fabric from two longitude parallel-designed wear trials were studied. The wear trials involved four kinds of pajamas made from cotton or polyester material that had hydrophilic or hydrophobic treatment, conducted for three weeks under mild cold conditions. Statistical tools, factor analysis, hierarchical linear regression, and logistic regression were applied to analyze the strong predictors of skin physiological parameters, stress level, and sensory response. A framework was established to illustrate clothing-wearer interactions with clothing fabric properties, skin physiology, stress level, and sensory response under mild cold conditions. Fabric has various effects on the human body under mild cold conditions. A fabric's properties influence skin physiology, sensation, and psychological response. © 2011 The International Society of Dermatology.

Thermal sensation and comfort during exposure to local airflow to face or legs.

PubMed

Yamashita, Kazuaki; Matsuo, Juntaro; Tochihara, Yutaka; Kondo, Youichiro; Takayama, Shizuka; Nagayama, Hiroki

2005-01-01

The present study examined the contribution of local airflow temperature to thermal sensation and comfort in humans. Eight healthy male students were exposed to local airflow to their faces (summer condition) or legs (winter condition) for 30 minutes. Local airflow temperature (Tf) was maintained at 18 degrees C to 36 degrees C, and ambient temperature (Ta) was maintained at 17.4 degrees C to 31.4 degrees C. Each subject was exposed to 16 conditions chosen from the combination of Tf and Ta. Based on the results of multiple regression analysis, the standardized partial regression coefficient of Tf and Ta were determined to be 0.93 and 0.13 in the summer condition, and 0.71 and 0.36 in the winter condition at the end of the exposure. Also, thermal comfort was observed to depend closely on the interrelation between Tf and Ta. The present data suggested that local airflow temperature is an important thermal factor regarding thermal sensation and comfort.

Trail impacts in Sagarmatha (Mt. Everest) National Park, Nepal: a logistic regression analysis.

PubMed

Nepal, S K

2003-09-01

A trail study was conducted in the Sagarmatha (Mt. Everest) National Park, Nepal, during 1997-1998. Based on that study, this paper examines the spatial variability of trail conditions and analyzes factors that influence trail conditions. Logistic regression (multinomial logit model) is applied to examine the influence of use and environmental factors on trail conditions. The assessment of trail conditions is based on a four-class rating system: (class I, very little damaged; class II, moderately damaged, class III, heavily damaged; and class IV, severely damaged). Wald statistics and a model classification table have been used for data interpretation. Results indicate that altitude, trail gradient, hazard potential, and vegetation type are positively associated with trail condition. Trails are more degraded at higher altitude, on steep gradients, in areas with natural hazard potential, and within shrub/grassland zones. Strong correlations between high levels of trail degradation and higher frequencies of visitors and lodges were found. A detailed analysis of environmental and use factors could provide valuable information to park managers in their decisions about trail design, layout and maintenance, and efficient and effective visitor management strategies. Comparable studies on high alpine environments are needed to predict precisely the effects of topographic and climatic extremes. More refined approaches and experimental methods are necessary to control the effects of environmental factors.

Background stratified Poisson regression analysis of cohort data.

PubMed

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Public Reporting of MRI of the Lumbar Spine for Low Back Pain and Changes in Clinical Documentation.

PubMed

Flug, Jonathan A; Lind, Kimberly E

2017-12-01

OP-8 is the Medicare imaging efficiency metric for MRI of the lumbar spine for low back pain in the outpatient hospital. We studied trends in exclusion criteria coding over time by site of service after implementation of OP-8 to evaluate provider's response to public reporting. We conducted a secondary data analysis using the Medicare Limited Data Set 5% sample for beneficiaries with MRI lumbar spine and lower back pain during 2009 to 2014. We evaluated the association between excluding condition prevalence and site by using generalized estimating equations regression. We produced model-based estimates of excluding condition prevalence by site and year. As a sensitivity analysis, we repeated the analysis while including additional conditions in the outcome measure. We included 285,911 MRIs of the lumbar spine for low back pain. Generalized estimating equations regression found that outpatient hospitals had a higher proportion of MRIs with at least one excluding condition documented compared with outpatient clinics (P < .05), but increases in excluding condition prevalence were similar across all sites during 2009 to 2014. Our results were not sensitive to the inclusion of additional conditions. Documentation of excluding conditions and other clinically reasonable exclusions for OP-8 increased over time for outpatient hospitals and clinics. Increases in documentation of comorbidities may not translate to actual improvement in imaging appropriateness for low back pain. When accounting for all relevant conditions, the proportion of patients with low back pain considered uncomplicated and being measured by OP-8 would be small, reflecting a small proportion of patients with low back pain. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Determination of variability in leaf biomass densities of conifers and mixed conifers under different environmental conditions in the San Joaquin Valley air basin. Final report

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

Temple, P.J.; Mutters, R.J.; Adams, C.

1995-06-01

Biomass sampling plots were established at 29 locations within the dominant vegetation zones of the study area. Estimates of foliar biomass were made for each plot by three independent methods: regression analysis on the basis of tree diameter, calculation of the amount of light intercepted by the leaf canopy, and extrapolation from branch leaf area. Multivariate regression analysis was used to relate these foliar biomass estimates for oak plots and conifer plots to several independent predictor variables, including elevation, slope, aspect, temperature, precipitation, and soil chemical characteristics.

Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea

NASA Astrophysics Data System (ADS)

Lee, Jangho; Kim, Kwang-Yul

2018-02-01

CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003-2012. Six meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area.

A systematic review and meta-regression analysis of mivacurium for tracheal intubation.

PubMed

Vanlinthout, L E H; Mesfin, S H; Hens, N; Vanacker, B F; Robertson, E N; Booij, L H D J

2014-12-01

We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent intubation conditions: mivacurium dose, 24.4%; opioid use, 29.9%; time to intubation and age together, 18.6%. The odds ratio (95% CI) for excellent intubation was 3.14 (1.65-5.73) for doubling the mivacurium dose, 5.99 (2.14-15.18) for adding opioids to the intubation sequence, and 6.55 (6.01-7.74) for increasing the delay between mivacurium injection and airway insertion from 1 to 2 min in subjects aged 25 years and 2.17 (2.01-2.69) for subjects aged 70 years, p < 0.001 for all. We conclude that good conditions for tracheal intubation are more likely by delaying laryngoscopy after injecting a higher dose of mivacurium with an opioid, particularly in older people. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

Analysis of Market Opportunities for Chinese Private Express Delivery Industry

NASA Astrophysics Data System (ADS)

Jiang, Changbing; Bai, Lijun; Tong, Xiaoqing

China's express delivery market has become the arena in which each express enterprise struggles to chase due to the huge potential demand and high profitable prospects. So certain qualitative and quantitative forecast for the future changes of China's express delivery market will help enterprises understand various types of market conditions and social changes in demand and adjust business activities to enhance their competitiveness timely. The development of China's express delivery industry is first introduced in this chapter. Then the theoretical basis of the regression model is overviewed. We also predict the demand trends of China's express delivery market by using Pearson correlation analysis and regression analysis from qualitative and quantitative aspects, respectively. Finally, we draw some conclusions and recommendations for China's express delivery industry.

Economic Conditions and the Divorce Rate: A Time-Series Analysis of the Postwar United States.

ERIC Educational Resources Information Center

South, Scott J.

1985-01-01

Challenges the belief that the divorce rate rises during prosperity and falls during economic recessions. Time-series regression analysis of postwar United States reveals small but positive effects of unemployment on divorce rate. Stronger influences on divorce rates are changes in age structure and labor-force participation rate of women.…

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations.

PubMed

Yang, Yang; Velayudhan, Ajoy; Thornhill, Nina F; Farid, Suzanne S

2017-09-01

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043-2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.

Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations

PubMed Central

Velayudhan, Ajoy; Thornhill, Nina F.

2017-01-01

ABSTRACT The need for high‐concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high‐concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high‐concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high‐throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi‐criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi‐criteria decision‐making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043–2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc. PMID:28464235

Local linear regression for function learning: an analysis based on sample discrepancy.

PubMed

Cervellera, Cristiano; Macciò, Danilo

2014-11-01

Local linear regression models, a kind of nonparametric structures that locally perform a linear estimation of the target function, are analyzed in the context of empirical risk minimization (ERM) for function learning. The analysis is carried out with emphasis on geometric properties of the available data. In particular, the discrepancy of the observation points used both to build the local regression models and compute the empirical risk is considered. This allows to treat indifferently the case in which the samples come from a random external source and the one in which the input space can be freely explored. Both consistency of the ERM procedure and approximating capabilities of the estimator are analyzed, proving conditions to ensure convergence. Since the theoretical analysis shows that the estimation improves as the discrepancy of the observation points becomes smaller, low-discrepancy sequences, a family of sampling methods commonly employed for efficient numerical integration, are also analyzed. Simulation results involving two different examples of function learning are provided.

Vibration-based structural health monitoring using adaptive statistical method under varying environmental condition

NASA Astrophysics Data System (ADS)

Jin, Seung-Seop; Jung, Hyung-Jo

2014-03-01

It is well known that the dynamic properties of a structure such as natural frequencies depend not only on damage but also on environmental condition (e.g., temperature). The variation in dynamic characteristics of a structure due to environmental condition may mask damage of the structure. Without taking the change of environmental condition into account, false-positive or false-negative damage diagnosis may occur so that structural health monitoring becomes unreliable. In order to address this problem, an approach to construct a regression model based on structural responses considering environmental factors has been usually used by many researchers. The key to success of this approach is the formulation between the input and output variables of the regression model to take into account the environmental variations. However, it is quite challenging to determine proper environmental variables and measurement locations in advance for fully representing the relationship between the structural responses and the environmental variations. One alternative (i.e., novelty detection) is to remove the variations caused by environmental factors from the structural responses by using multivariate statistical analysis (e.g., principal component analysis (PCA), factor analysis, etc.). The success of this method is deeply depending on the accuracy of the description of normal condition. Generally, there is no prior information on normal condition during data acquisition, so that the normal condition is determined by subjective perspective with human-intervention. The proposed method is a novel adaptive multivariate statistical analysis for monitoring of structural damage detection under environmental change. One advantage of this method is the ability of a generative learning to capture the intrinsic characteristics of the normal condition. The proposed method is tested on numerically simulated data for a range of noise in measurement under environmental variation. A comparative study with conventional methods (i.e., fixed reference scheme) demonstrates the superior performance of the proposed method for structural damage detection.

Combined analysis of magnetic and gravity anomalies using normalized source strength (NSS)

NASA Astrophysics Data System (ADS)

Li, L.; Wu, Y.

2017-12-01

Gravity field and magnetic field belong to potential fields which lead inherent multi-solution. Combined analysis of magnetic and gravity anomalies based on Poisson's relation is used to determinate homology gravity and magnetic anomalies and decrease the ambiguity. The traditional combined analysis uses the linear regression of the reduction to pole (RTP) magnetic anomaly to the first order vertical derivative of the gravity anomaly, and provides the quantitative or semi-quantitative interpretation by calculating the correlation coefficient, slope and intercept. In the calculation process, due to the effect of remanent magnetization, the RTP anomaly still contains the effect of oblique magnetization. In this case the homology gravity and magnetic anomalies display irrelevant results in the linear regression calculation. The normalized source strength (NSS) can be transformed from the magnetic tensor matrix, which is insensitive to the remanence. Here we present a new combined analysis using NSS. Based on the Poisson's relation, the gravity tensor matrix can be transformed into the pseudomagnetic tensor matrix of the direction of geomagnetic field magnetization under the homologous condition. The NSS of pseudomagnetic tensor matrix and original magnetic tensor matrix are calculated and linear regression analysis is carried out. The calculated correlation coefficient, slope and intercept indicate the homology level, Poisson's ratio and the distribution of remanent respectively. We test the approach using synthetic model under complex magnetization, the results show that it can still distinguish the same source under the condition of strong remanence, and establish the Poisson's ratio. Finally, this approach is applied in China. The results demonstrated that our approach is feasible.

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

NASA Astrophysics Data System (ADS)

Sreekanth, V.; Mahesh, B.; Niranjan, K.

2017-11-01

In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy's air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

PubMed

Houpt, Joseph W; Bittner, Jennifer L

2018-07-01

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Weather Impact on Airport Arrival Meter Fix Throughput

NASA Technical Reports Server (NTRS)

Wang, Yao

2017-01-01

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

The dynamic correlation between policy uncertainty and stock market returns in China

NASA Astrophysics Data System (ADS)

Yang, Miao; Jiang, Zhi-Qiang

2016-11-01

The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

Asthma exacerbation and proximity of residence to major roads: a population-based matched case-control study among the pediatric Medicaid population in Detroit, Michigan

PubMed Central

2011-01-01

Background The relationship between asthma and traffic-related pollutants has received considerable attention. The use of individual-level exposure measures, such as residence location or proximity to emission sources, may avoid ecological biases. Method This study focused on the pediatric Medicaid population in Detroit, MI, a high-risk population for asthma-related events. A population-based matched case-control analysis was used to investigate associations between acute asthma outcomes and proximity of residence to major roads, including freeways. Asthma cases were identified as all children who made at least one asthma claim, including inpatient and emergency department visits, during the three-year study period, 2004-06. Individually matched controls were randomly selected from the rest of the Medicaid population on the basis of non-respiratory related illness. We used conditional logistic regression with distance as both categorical and continuous variables, and examined non-linear relationships with distance using polynomial splines. The conditional logistic regression models were then extended by considering multiple asthma states (based on the frequency of acute asthma outcomes) using polychotomous conditional logistic regression. Results Asthma events were associated with proximity to primary roads with an odds ratio of 0.97 (95% CI: 0.94, 0.99) for a 1 km increase in distance using conditional logistic regression, implying that asthma events are less likely as the distance between the residence and a primary road increases. Similar relationships and effect sizes were found using polychotomous conditional logistic regression. Another plausible exposure metric, a reduced form response surface model that represents atmospheric dispersion of pollutants from roads, was not associated under that exposure model. Conclusions There is moderately strong evidence of elevated risk of asthma close to major roads based on the results obtained in this population-based matched case-control study. PMID:21513554

Isovolumic relaxation period as an index of left ventricular relaxation under different afterload conditions--comparison with the time constant of left ventricular pressure decay in the dog.

PubMed

Ochi, H; Ikuma, I; Toda, H; Shimada, T; Morioka, S; Moriyama, K

1989-12-01

In order to determine whether isovolumic relaxation period (IRP) reflects left ventricular relaxation under different afterload conditions, 17 anesthetized, open chest dogs were studied, and the left ventricular pressure decay time constant (T) was calculated. In 12 dogs, angiotensin II and nitroprusside were administered, with the heart rate constant at 90 beats/min. Multiple linear regression analysis showed that the aortic dicrotic notch pressure (AoDNP) and T were major determinants of IRP, while left ventricular end-diastolic pressure was a minor determinant. Multiple linear regression analysis, correlating T with IRP and AoDNP, did not further improve the correlation coefficient compared with that between T and IRP. We concluded that correction of the IRP by AoDNP is not necessary to predict T from additional multiple linear regression. The effects of ascending aortic constriction or angiotensin II on IRP were examined in five dogs, after pretreatment with propranolol. Aortic constriction caused a significant decrease in IRP and T, while angiotensin II produced a significant increase in IRP and T. IRP was affected by the change of afterload. However, the IRP and T values were always altered in the same direction. These results demonstrate that IRP is substituted for T and it reflects left ventricular relaxation even in different afterload conditions. We conclude that IRP is a simple parameter easily used to evaluate left ventricular relaxation in clinical situations.

[A case-control study on the risk factors of work-related acute pesticide poisoning among farmers from Jiangsu province].

PubMed

Tu, Zhi-bin; Cui, Meng-jing; Yao, Hong-yan; Hu, Guo-qing; Xiang, Hui-yun; Stallones, Lorann; Zhang, Xu-jun

2012-04-01

To explore the risk factors on cases regarding work-related acute pesticide poisoning among farmers of Jiangsu province. A population-based, 1:2 matched case-control study was carried out, with 121 patients as case-group paired by 242 persons with same gender, district and age less then difference of 3 years, as controls. Cases were the ones who had suffered from work-related acute pesticide poisoning. A unified questionnaire was used. Data base was established by EpiData 3.1, and SPSS 16.0 was used for both data single factor and multi-conditional logistics regression analysis. Results from the single factor logistic regression analysis showed that the related risk factors were: lack of safety guidance, lack of readable labels before praying pesticides, no regression during application, using hand to wipe sweat, using leaking knapsack, body contaminated during application and continuing to work when feeling ill after the contact of pesticides. Results from multi-conditional logistic regression analysis indicated that the lack of safety guidance (OR=2.25, 95%CI: 1.35-3.74), no readable labels before praying pesticides (OR=1.95, 95%CI: 1.19-3.18), wiping the sweat by hand during application (OR=1.97, 95%CI: 1.20-3.24) and using leaking knapsack during application (OR=1.82, 95%CI:1.10-3.01) were risk factors for the occurrence of work-related acute pesticide poisoning. The lack of safety guidance, no readable labels before praying pesticides, wiping the sweat by hand or using leaking knapsack during application were correlated to the occurrence of work-related acute pesticide poisoning.

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

Development and Validation of a Practical Instrument for Injury Prevention: The Occupational Safety and Health Monitoring and Assessment Tool (OSH-MAT).

PubMed

Sun, Yi; Arning, Martin; Bochmann, Frank; Börger, Jutta; Heitmann, Thomas

2018-06-01

The Occupational Safety and Health Monitoring and Assessment Tool (OSH-MAT) is a practical instrument that is currently used in the German woodworking and metalworking industries to monitor safety conditions at workplaces. The 12-item scoring system has three subscales rating technical, organizational, and personnel-related conditions in a company. Each item has a rating value ranging from 1 to 9, with higher values indicating higher standard of safety conditions. The reliability of this instrument was evaluated in a cross-sectional survey among 128 companies and its validity among 30,514 companies. The inter-rater reliability of the instrument was examined independently and simultaneously by two well-trained safety engineers. Agreement between the double ratings was quantified by the intraclass correlation coefficient and absolute agreement of the rating values. The content validity of the OSH-MAT was evaluated by quantifying the association between OSH-MAT values and 5-year average injury rates by Poisson regression analysis adjusted for the size of the companies and industrial sectors. The construct validity of OSH-MAT was examined by principle component factor analysis. Our analysis indicated good to very good inter-rater reliability (intraclass correlation coefficient = 0.64-0.74) of OSH-MAT values with an absolute agreement of between 72% and 81%. Factor analysis identified three component subscales that met exactly the structure theory of this instrument. The Poisson regression analysis demonstrated a statistically significant exposure-response relationship between OSH-MAT values and the 5-year average injury rates. These analyses indicate that OSH-MAT is a valid and reliable instrument that can be used effectively to monitor safety conditions at workplaces.

Comparison of Subjective Refraction under Binocular and Monocular Conditions in Myopic Subjects.

PubMed

Kobashi, Hidenaga; Kamiya, Kazutaka; Handa, Tomoya; Ando, Wakako; Kawamorita, Takushi; Igarashi, Akihito; Shimizu, Kimiya

2015-07-28

To compare subjective refraction under binocular and monocular conditions, and to investigate the clinical factors affecting the difference in spherical refraction between the two conditions. We examined thirty eyes of 30 healthy subjects. Binocular and monocular refraction without cycloplegia was measured through circular polarizing lenses in both eyes, using the Landolt-C chart of the 3D visual function trainer-ORTe. Stepwise multiple regression analysis was used to assess the relations among several pairs of variables and the difference in spherical refraction in binocular and monocular conditions. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition (p < 0.001), whereas no significant differences were seen in subjective cylindrical refraction (p = 0.99). The explanatory variable relevant to the difference in spherical refraction between binocular and monocular conditions was the binocular spherical refraction (p = 0.032, partial regression coefficient B = 0.029) (adjusted R(2) = 0.230). No significant correlation was seen with other clinical factors. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition. Eyes with higher degrees of myopia are more predisposed to show the large difference in spherical refraction between these two conditions.

Comparison of Subjective Refraction under Binocular and Monocular Conditions in Myopic Subjects

PubMed Central

Kobashi, Hidenaga; Kamiya, Kazutaka; Handa, Tomoya; Ando, Wakako; Kawamorita, Takushi; Igarashi, Akihito; Shimizu, Kimiya

2015-01-01

To compare subjective refraction under binocular and monocular conditions, and to investigate the clinical factors affecting the difference in spherical refraction between the two conditions. We examined thirty eyes of 30 healthy subjects. Binocular and monocular refraction without cycloplegia was measured through circular polarizing lenses in both eyes, using the Landolt-C chart of the 3D visual function trainer-ORTe. Stepwise multiple regression analysis was used to assess the relations among several pairs of variables and the difference in spherical refraction in binocular and monocular conditions. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition (p < 0.001), whereas no significant differences were seen in subjective cylindrical refraction (p = 0.99). The explanatory variable relevant to the difference in spherical refraction between binocular and monocular conditions was the binocular spherical refraction (p = 0.032, partial regression coefficient B = 0.029) (adjusted R2 = 0.230). No significant correlation was seen with other clinical factors. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition. Eyes with higher degrees of myopia are more predisposed to show the large difference in spherical refraction between these two conditions. PMID:26218972

Access to Care and Satisfaction Among Health Center Patients With Chronic Conditions.

PubMed

Shi, Leiyu; Lee, De-Chih; Haile, Geraldine Pierre; Liang, Hailun; Chung, Michelle; Sripipatana, Alek

This study examined access to care and satisfaction among health center patients with chronic conditions. Data for this study were obtained from the 2009 Health Center Patient Survey. Dependent variables of interest included 5 measures of access to and satisfaction with care, whereas the main independent variable was number of chronic conditions. Results of bivariate analysis and multiple logistic regressions showed that patients with chronic conditions had significantly higher odds of reporting access barriers than those without chronic conditions. Our results suggested that additional efforts and resources are necessary to address the needs of health center patients with chronic conditions.

Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension

PubMed Central

Wang, Lan; Wu, Yichao

2012-01-01

Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distribution. In this framework, we investigate the methodology and theory of nonconvex penalized quantile regression in ultra-high dimension. The proposed approach has two distinctive features: (1) it enables us to explore the entire conditional distribution of the response variable given the ultra-high dimensional covariates and provides a more realistic picture of the sparsity pattern; (2) it requires substantially weaker conditions compared with alternative methods in the literature; thus, it greatly alleviates the difficulty of model checking in the ultra-high dimension. In theoretic development, it is challenging to deal with both the nonsmooth loss function and the nonconvex penalty function in ultra-high dimensional parameter space. We introduce a novel sufficient optimality condition which relies on a convex differencing representation of the penalized loss function and the subdifferential calculus. Exploring this optimality condition enables us to establish the oracle property for sparse quantile regression in the ultra-high dimension under relaxed conditions. The proposed method greatly enhances existing tools for ultra-high dimensional data analysis. Monte Carlo simulations demonstrate the usefulness of the proposed procedure. The real data example we analyzed demonstrates that the new approach reveals substantially more information compared with alternative methods. PMID:23082036

[Determination of Bloodstain Age by UV Visible Integrating Sphere Reflection Spectrum].

PubMed

Yan, L Q; Gao, Y

2016-10-01

To establish a method for rapid identification of bloodstain age. Under laboratory conditions （20 ℃, 25 ℃ and 30 ℃）, an integrating sphere ISR-240A was used as a reflection accessory on an UV-2450 UV-vis spectrophotometer, and a standard white board of BaSO₄ was used as reference, the reflection spectrums of bloodstain from human ears' venous blood were measured at regular intervals. The reflection radios R ₅₄₁ and R ₅₇₇ at a specific wavelength were collected and the value of R ₅₄₁/ R ₅₇₇ was calculated. The linear fitting and regression analysis were done by SPSS 17.0. The results of regression analysis showed that R ² of the ratios of bloodstain age to UV visible reflectivity in specific wavelengths were larger than 0.8 within 8 hours and under certain circumstances. The regression equation was established. The bloodstain age had significant correlation with the value of R ₅₄₁/ R ₅₇₇. The method of inspection is simple, rapid and nondestructive with a good reliability, and can be used to identify the bloodstain age within 8 hours elapsed-time standards under laboratory conditions. Copyright© by the Editorial Department of Journal of Forensic Medicine

Modeling of Engine Parameters for Condition-Based Maintenance of the MTU Series 2000 Diesel Engine

DTIC Science & Technology

2016-09-01

are suitable. To model the behavior of the engine, an autoregressive distributed lag (ARDL) time series model of engine speed and exhaust gas... time series model of engine speed and exhaust gas temperature is derived. The lag length for ARDL is determined by whitening of residuals using the...15 B. REGRESSION ANALYSIS ....................................................................15 1. Time Series Analysis

Pseudo and conditional score approach to joint analysis of current count and current status data.

PubMed

Wen, Chi-Chung; Chen, Yi-Hau

2018-04-17

We develop a joint analysis approach for recurrent and nonrecurrent event processes subject to case I interval censorship, which are also known in literature as current count and current status data, respectively. We use a shared frailty to link the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty fully unspecified. Conditional on the frailty, the recurrent event is assumed to follow a nonhomogeneous Poisson process, and the mean function of the recurrent event and the survival function of the nonrecurrent event are assumed to follow some general form of semiparametric transformation models. Estimation of the models is based on the pseudo-likelihood and the conditional score techniques. The resulting estimators for the regression parameters and the unspecified baseline functions are shown to be consistent with rates of square and cubic roots of the sample size, respectively. Asymptotic normality with closed-form asymptotic variance is derived for the estimator of the regression parameters. We apply the proposed method to a fracture-osteoporosis survey data to identify risk factors jointly for fracture and osteoporosis in elders, while accounting for association between the two events within a subject. © 2018, The International Biometric Society.

Prevalence of Depressive Symptoms and Related Factors in Korean Employees: The Third Korean Working Conditions Survey (2011).

PubMed

Park, Ji Nam; Han, Mi Ah; Park, Jong; Ryu, So Yeon

2016-04-14

The aim of this study was to analyze the association between general working conditions and depressive symptoms among Korean employees. The target population of the study was native employees nationwide who were at least 15 years old, and 50,032 such individuals were enrolled in the study. Depressive symptoms was assessed using the WHO-5 wellbeing index. Associations between general characteristics, job-related characteristics, work environment, and depressive symptoms were tested using chi-square tests, t-tests, and multiple logistic regression analysis. The prevalence of depressive symptoms was 39% (40.7% in males and 36.5% in females). Multiple regression analysis revealed that male subjects, older subjects, subjects with higher education status, subjects with lower monthly income, current smokers, and frequent drinkers were more likely to have depressive symptoms. In addition, longer weekly work hours, occupation type (skilled, unskilled, operative, or economic sector), shift work, working to tight deadlines, exposure to stress at work, and hazard exposure were associated with depressive symptoms. This representative study will be a guide to help manage depression among Korean employees. We expect that further research will identify additional causal relationships between general or specific working conditions and depression.

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression.

PubMed

Beckstead, Jason W

2012-03-30

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic strategy to isolate, examine, and remove suppression effects has been offered. In this article such an approach, rooted in confirmatory factor analysis theory and employing matrix algebra, is developed. Suppression is viewed as the result of criterion-irrelevant variance operating among predictors. Decomposition of predictor variables into criterion-relevant and criterion-irrelevant components using structural equation modeling permits derivation of regression weights with the effects of criterion-irrelevant variance omitted. Three examples with data from applied research are used to illustrate the approach: the first assesses child and parent characteristics to explain why some parents of children with obsessive-compulsive disorder accommodate their child's compulsions more so than do others, the second examines various dimensions of personal health to explain individual differences in global quality of life among patients following heart surgery, and the third deals with quantifying the relative importance of various aptitudes for explaining academic performance in a sample of nursing students. The approach is offered as an analytic tool for investigators interested in understanding predictor-criterion relationships when complex patterns of intercorrelation among predictors are present and is shown to augment dominance analysis.

[Optimization of processing technology for semen cuscuta by uniform and regression analysis].

PubMed

Li, Chun-yu; Luo, Hui-yu; Wang, Shu; Zhai, Ya-nan; Tian, Shu-hui; Zhang, Dan-shen

2011-02-01

To optimize the best preparation technology for the contains of total flavornoids, polysaccharides, the percentage of water and alcohol-soluble components in Semen Cuscuta herb processing. UV-spectrophotometry was applied to determine the contains of total flavornoids and polysaccharides, which were extracted from Semen Cuscuta. And the processing was optimized by the way of uniform design and contour map. The best preparation technology was satisfied with some conditions as follows: baking temperature 150 degrees C, baking time 140 seconds. The regression models are notable and reasonable, which can forecast results precisely.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.

PubMed

Nguyen, Quynh C; Osypuk, Theresa L; Schmidt, Nicole M; Glymour, M Maria; Tchetgen Tchetgen, Eric J

2015-03-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries.

PubMed

Ayuso, Mercedes; Bermúdez, Lluís; Santolino, Miguel

2016-04-01

The analysis of factors influencing the severity of the personal injuries suffered by victims of motor accidents is an issue of major interest. Yet, most of the extant literature has tended to address this question by focusing on either the severity of temporary disability or the severity of permanent injury. In this paper, a bivariate copula-based regression model for temporary disability and permanent injury severities is introduced for the joint analysis of the relationship with the set of factors that might influence both categories of injury. Using a motor insurance database with 21,361 observations, the copula-based regression model is shown to give a better performance than that of a model based on the assumption of independence. The inclusion of the dependence structure in the analysis has a higher impact on the variance estimates of the injury severities than it does on the point estimates. By taking into account the dependence between temporary and permanent severities a more extensive factor analysis can be conducted. We illustrate that the conditional distribution functions of injury severities may be estimated, thus, providing decision makers with valuable information. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Predicting the rate of change in timber value for forest stands infested with gypsy moth

Treesearch

David A. Gansner; Owen W. Herrick

1982-01-01

Presents a method for estimating the potential impact of gypsy moth attacks on forest-stand value. Robust regression analysis is used to develop an equation for predicting the rate of change in timber value from easy-to-measure key characteristics of stand condition.

Modeling enterococcus densities measured by quantitative polymerase chain reaction and membrane filtration using environmental conditions at four Great Lakes beaches

EPA Science Inventory

Data collected by the US Environmental Protection Agency (EPA) during the summer months of 2003 and 2004 at four US Great Lakes beaches were analyzed using regression analysis to identify relationships between meteorological, physical water characteristics, and beach characterist...

Job satisfaction among recreation practitioners

Treesearch

Erin Parks; Andrew Holdnak

2002-01-01

Job satisfaction among recreation professionals can be affected by many working conditions. This study has investigated the impact fourteen variables had on the job satisfaction of recreation practitioners. The sample consisted of 106 responses from members of the Resort and Commercial Recreation Association (RCRA). The results of the regression analysis for job...

Mixed oxidizer hybrid propulsion system optimization under uncertainty using applied response surface methodology and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Whitehead, James Joshua

The analysis documented herein provides an integrated approach for the conduct of optimization under uncertainty (OUU) using Monte Carlo Simulation (MCS) techniques coupled with response surface-based methods for characterization of mixture-dependent variables. This novel methodology provides an innovative means of conducting optimization studies under uncertainty in propulsion system design. Analytic inputs are based upon empirical regression rate information obtained from design of experiments (DOE) mixture studies utilizing a mixed oxidizer hybrid rocket concept. Hybrid fuel regression rate was selected as the target response variable for optimization under uncertainty, with maximization of regression rate chosen as the driving objective. Characteristic operational conditions and propellant mixture compositions from experimental efforts conducted during previous foundational work were combined with elemental uncertainty estimates as input variables. Response surfaces for mixture-dependent variables and their associated uncertainty levels were developed using quadratic response equations incorporating single and two-factor interactions. These analysis inputs, response surface equations and associated uncertainty contributions were applied to a probabilistic MCS to develop dispersed regression rates as a function of operational and mixture input conditions within design space. Illustrative case scenarios were developed and assessed using this analytic approach including fully and partially constrained operational condition sets over all of design mixture space. In addition, optimization sets were performed across an operationally representative region in operational space and across all investigated mixture combinations. These scenarios were selected as representative examples relevant to propulsion system optimization, particularly for hybrid and solid rocket platforms. Ternary diagrams, including contour and surface plots, were developed and utilized to aid in visualization. The concept of Expanded-Durov diagrams was also adopted and adapted to this study to aid in visualization of uncertainty bounds. Regions of maximum regression rate and associated uncertainties were determined for each set of case scenarios. Application of response surface methodology coupled with probabilistic-based MCS allowed for flexible and comprehensive interrogation of mixture and operating design space during optimization cases. Analyses were also conducted to assess sensitivity of uncertainty to variations in key elemental uncertainty estimates. The methodology developed during this research provides an innovative optimization tool for future propulsion design efforts.

A Data Analysis Approach to Evaluating Achievement Outcomes of Instruction. Technical Report No. 338. Report from the Project on Conditions of School Learning and Instructional Strategies.

ERIC Educational Resources Information Center

Quilling, Mary Rintoul

The purpose of the present study is to demonstrate the utility of data analysis methodology in evaluative research relating pupil and curriculum variables to pupil achievement. Regression models which account for achievement will result from the application of the methodology to two evaluative problems--one of curriculum comparison and another…

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

PubMed

Johnson, Justin A; Rodeberg, Nathan T; Wightman, R Mark

2016-03-16

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study.

PubMed

Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier

2018-02-01

Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Sheyenne River, North Dakota, 1980-2006

USGS Publications Warehouse

Ryberg, Karen R.

2007-01-01

This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the North Dakota State Water Commission, to estimate water-quality constituent concentrations at seven sites on the Sheyenne River, N. Dak. Regression analysis of water-quality data collected in 1980-2006 was used to estimate concentrations for hardness, dissolved solids, calcium, magnesium, sodium, and sulfate. The explanatory variables examined for the regression relations were continuously monitored streamflow, specific conductance, and water temperature. For the conditions observed in 1980-2006, streamflow was a significant explanatory variable for some constituents. Specific conductance was a significant explanatory variable for all of the constituents, and water temperature was not a statistically significant explanatory variable for any of the constituents in this study. The regression relations were evaluated using common measures of variability, including R2, the proportion of variability in the estimated constituent concentration explained by the explanatory variables and regression equation. R2 values ranged from 0.784 for calcium to 0.997 for dissolved solids. The regression relations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.7 for dissolved solids to 11.5 for sulfate. The regression relations also may be used to estimate daily constituent loads. The relations should be monitored for change over time, especially at sites 2 and 3 which have a short period of record. In addition, caution should be used when the Sheyenne River is affected by ice or when upstream sites are affected by isolated storm runoff. Almost all of the outliers and highly influential samples removed from the analysis were made during periods when the Sheyenne River might be affected by ice.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

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.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Establishing a Mathematical Equations and Improving the Production of L-tert-Leucine by Uniform Design and Regression Analysis.

PubMed

Jiang, Wei; Xu, Chao-Zhen; Jiang, Si-Zhi; Zhang, Tang-Duo; Wang, Shi-Zhen; Fang, Bai-Shan

2017-04-01

L-tert-Leucine (L-Tle) and its derivatives are extensively used as crucial building blocks for chiral auxiliaries, pharmaceutically active ingredients, and ligands. Combining with formate dehydrogenase (FDH) for regenerating the expensive coenzyme NADH, leucine dehydrogenase (LeuDH) is continually used for synthesizing L-Tle from α-keto acid. A multilevel factorial experimental design was executed for research of this system. In this work, an efficient optimization method for improving the productivity of L-Tle was developed. And the mathematical model between different fermentation conditions and L-Tle yield was also determined in the form of the equation by using uniform design and regression analysis. The multivariate regression equation was conveniently implemented in water, with a space time yield of 505.9 g L -1  day -1 and an enantiomeric excess value of >99 %. These results demonstrated that this method might become an ideal protocol for industrial production of chiral compounds and unnatural amino acids such as chiral drug intermediates.

Constitution of traditional chinese medicine and related factors in women of childbearing age.

PubMed

Jiang, Qiao-Yu; Li, Jue; Zheng, Liang; Wang, Guang-Hua; Wang, Jing

2018-04-01

This study investigates the constitution of traditional Chinese medicine (TCM) among women who want to be pregnant in one year and explores factors related to TCM constitution. This study was conducted on women who participated in free preconception check-ups provided by the Zhabei District Maternity and Child Care Center in Shanghai, China. The information regarding the female demographic characteristics, physical condition, history of pregnancy and childbearing, diet and behavior, and social psychological factors was collected, and TCM constitution assessment was performed. The Chi-square test, t-test, logistic regression analysis, and multinomial logistic regression analysis were used to explore the related factors of TCM constitution. The participants in this study were aged 28.3 ± 3.0 years. Approximately fifty-five women in this study had Unbalanced Constitution. Logistic regression analysis showed that Shanghai residence, dysmenorrhea, gum bleeding, aversion to vegetables, preference for raw meat, job stress, and economic stress were significantly and negatively associated with Balanced Constitution. Multinomial logistic analysis showed that Shanghai residence was significantly associated with Yang-deficiency, Yin-deficiency, and Stagnant Qi Constitutions; gum bleeding was significantly associated with Yin-deficiency, Stagnant Blood, Stagnant Qi, and Inherited Special Constitutions; aversion to vegetables was significantly associated with Damp-heat Constitution; job stress was significantly associated with Yang-deficiency, Phlegm-dampness, Damp-heat, Stagnant Blood, and Stagnant Qi Constitutions; and economic stress was significantly associated with Yang-deficiency, and Stagnant Qi Constitutions. The application of TCM constitution to preconception care would be beneficial for early identification of potential TCM constitution risks and be beneficial for early intervention (e.g., health education, and dietary education), especially during the women who do not have a medical condition and those who have related factors found in this study. Copyright © 2018. Published by Elsevier Taiwan LLC.

Optimization by response surface methodology of lutein recovery from paprika leaves using accelerated solvent extraction.

PubMed

Kang, Jae-Hyun; Kim, Suna; Moon, BoKyung

2016-08-15

In this study, we used response surface methodology (RSM) to optimize the extraction conditions for recovering lutein from paprika leaves using accelerated solvent extraction (ASE). The lutein content was quantitatively analyzed using a UPLC equipped with a BEH C18 column. A central composite design (CCD) was employed for experimental design to obtain the optimized combination of extraction temperature (°C), static time (min), and solvent (EtOH, %). The experimental data obtained from a twenty sample set were fitted to a second-order polynomial equation using multiple regression analysis. The adjusted coefficient of determination (R(2)) for the lutein extraction model was 0.9518, and the probability value (p=0.0000) demonstrated a high significance for the regression model. The optimum extraction conditions for lutein were temperature: 93.26°C, static time: 5 min, and solvent: 79.63% EtOH. Under these conditions, the predicted extraction yield of lutein was 232.60 μg/g. Copyright © 2016 Elsevier Ltd. All rights reserved.

Monitoring and Assessment of Military Installation Land Condition under Training Disturbance Using Remote Sensing

NASA Astrophysics Data System (ADS)

Rijal, Santosh

Various military training activities are conducted in more than 11.3 million hectares of land (> 5,500 training sites) in the United States (U.S.). These training activities directly and indirectly degrade the land. Land degradation can impede continuous military training. In order to sustain long term training missions and Army combat readiness, the environmental conditions of the military installations need to be carefully monitored and assessed. Furthermore, the National Environmental Policy Act of 1969 (NEPA) and the U.S. Army Regulation 200-2 require the DoD to minimize the environmental impacts of training and document the environmental consequences of their actions. To achieve these objectives, the Department of Army initiated an Integrated Training Area Management (ITAM) program to manage training lands through assessing their environmental requirements and establishing policies and procedures to achieve optimum, sustainable use of training lands. One of the programs under ITAM, Range and Training Land Assessment (RTLA) was established to collect field-based data for monitoring installation's environmental condition. Due to high cost and inefficiencies involved in the collection of field data, the RTLA program was stopped in several military installations. Therefore, there has been a strong need to develop an efficient and low cost remote sensing based methodology for assessing and monitoring land conditions of military installations. It is also important to make a long-term assessment of installation land condition for understanding cumulative impacts of continuous military training activities. Additionally, it is unclear that compared to non-military land condition, to what extent military training activities have led to the degradation of land condition for military installations. The first paper of this dissertation developed a soil erosion relevant and image derived cover factor (ICF) based on linear spectral mixture (LSM) analysis to assess and monitor the land condition of military land and compare it with non-military land. The results from this study can provide FR land managers with the information of the spatial variation and temporal trend of land condition in FR. Fort Riley land managers can also use this method for monitoring their land condition at a very low cost. This method can thus be applied to other military installations as well as non-military lands. Furthermore, one of the most significant environmental problems in military installations of the U.S. is the formation of gullies due to the intensive use of military vehicle. However, to our knowledge, no remote sensing based method has been developed and used to assess the detection of gullies in military installations. In the second paper of this dissertation, light detection and ranging (LiDAR) derived digital elevation model (DEM) of 2010 and WorldView-2 images of 2010 were used to quantify the gullies in FR. This method can be easily applied to assess gullies in non-military installations. On the other hand, modeling the land condition of military installation is critical to understand the spatial and temporal pattern of military training induced disturbance and land recovery. In the third paper, it was assumed that the military training induced disturbance was spatially auto-correlated and thus four regression models including i) linear stepwise regression (LSR) ii) logistic regression (LR), iii) geographically weighted linear regression (GWR), and iv) geographically weighted logistic regression (GWLR) were developed and compared using remote sensing image derived spectral variables for years 1990, 1997, 1998, 1999, and 2001. It was found that the spatial distribution of the military training disturbance was well demonstrated by all the regression models with higher intensities of military training disturbance in the northwest and central west parts of the installation. Compared to other regression models, GWR accurately estimated the land condition of FR. This result provided the applicability of using local variability based regression model to accurately predict land condition. Different plant communities of military installations respond differently to military training induced disturbance. The information of the spatial distribution of plant species in military installations is important to gain insight of the resilient capacity of the land following disturbances. For the purpose, in the fourth paper, hyperspectral in-situ data were collected from FR and KPBS in the summer of 2015 using a hyperspectral instrument. Principal component analysis (PCA) and band relative importance (BRI) were used to identify relative importance of each of the spectral bands. The results from this study provided useful information about the optimal wavelengths that help distinguish different plant species of FR and can be easily used with high resolution hyperspectral images for mapping the spatial distribution of the plant species. This information will be helpful for the sustainable management of the tallgrass prairie ecosystem. (Abstract shortened by ProQuest.).

Regression modeling and prediction of road sweeping brush load characteristics from finite element analysis and experimental results.

PubMed

Wang, Chong; Sun, Qun; Wahab, Magd Abdel; Zhang, Xingyu; Xu, Limin

2015-09-01

Rotary cup brushes mounted on each side of a road sweeper undertake heavy debris removal tasks but the characteristics have not been well known until recently. A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design. However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system. This study develops a mathematical regression model to summarize the FE modeled results. The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc. The data were then fitted by a multiple variable regression model using the maximum likelihood method. The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. The methodology may also be used in the design and optimization of rotary brush tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of exact, efron and breslow parameter approach method on hazard ratio and stratified cox regression model

NASA Astrophysics Data System (ADS)

Fatekurohman, Mohamat; Nurmala, Nita; Anggraeni, Dian

2018-04-01

Lungs are the most important organ, in the case of respiratory system. Problems related to disorder of the lungs are various, i.e. pneumonia, emphysema, tuberculosis and lung cancer. Comparing all those problems, lung cancer is the most harmful. Considering about that, the aim of this research applies survival analysis and factors affecting the endurance of the lung cancer patient using comparison of exact, Efron and Breslow parameter approach method on hazard ratio and stratified cox regression model. The data applied are based on the medical records of lung cancer patients in Jember Paru-paru hospital on 2016, east java, Indonesia. The factors affecting the endurance of the lung cancer patients can be classified into several criteria, i.e. sex, age, hemoglobin, leukocytes, erythrocytes, sedimentation rate of blood, therapy status, general condition, body weight. The result shows that exact method of stratified cox regression model is better than other. On the other hand, the endurance of the patients is affected by their age and the general conditions.

An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data.

PubMed

Ng, S K; McLachlan, G J

2003-04-15

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright 2003 John Wiley & Sons, Ltd.

Methods for estimating streamflow at mountain fronts in southern New Mexico

USGS Publications Warehouse

Waltemeyer, S.D.

1994-01-01

The infiltration of streamflow is potential recharge to alluvial-basin aquifers at or near mountain fronts in southern New Mexico. Data for 13 streamflow-gaging stations were used to determine a relation between mean annual stream- flow and basin and climatic conditions. Regression analysis was used to develop an equation that can be used to estimate mean annual streamflow on the basis of drainage areas and mean annual precipi- tation. The average standard error of estimate for this equation is 46 percent. Regression analysis also was used to develop an equation to estimate mean annual streamflow on the basis of active- channel width. Measurements of the width of active channels were determined for 6 of the 13 gaging stations. The average standard error of estimate for this relation is 29 percent. Stream- flow estimates made using a regression equation based on channel geometry are considered more reliable than estimates made from an equation based on regional relations of basin and climatic conditions. The sample size used to develop these relations was small, however, and the reported standard error of estimate may not represent that of the entire population. Active-channel-width measurements were made at 23 ungaged sites along the Rio Grande upstream from Elephant Butte Reservoir. Data for additional sites would be needed for a more comprehensive assessment of mean annual streamflow in southern New Mexico.

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North, Fargo, North Dakota, 2003-05

USGS Publications Warehouse

Ryberg, Karen R.

2006-01-01

This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the Bureau of Reclamation, U.S. Department of the Interior, to estimate water-quality constituent concentrations in the Red River of the North at Fargo, North Dakota. Regression analysis of water-quality data collected in 2003-05 was used to estimate concentrations and loads for alkalinity, dissolved solids, sulfate, chloride, total nitrite plus nitrate, total nitrogen, total phosphorus, and suspended sediment. The explanatory variables examined for regression relation were continuously monitored physical properties of water-streamflow, specific conductance, pH, water temperature, turbidity, and dissolved oxygen. For the conditions observed in 2003-05, streamflow was a significant explanatory variable for all estimated constituents except dissolved solids. pH, water temperature, and dissolved oxygen were not statistically significant explanatory variables for any of the constituents in this study. Specific conductance was a significant explanatory variable for alkalinity, dissolved solids, sulfate, and chloride. Turbidity was a significant explanatory variable for total phosphorus and suspended sediment. For the nutrients, total nitrite plus nitrate, total nitrogen, and total phosphorus, cosine and sine functions of time also were used to explain the seasonality in constituent concentrations. The regression equations were evaluated using common measures of variability, including R2, or the proportion of variability in the estimated constituent explained by the regression equation. R2 values ranged from 0.703 for total nitrogen concentration to 0.990 for dissolved-solids concentration. The regression equations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.1 for dissolved solids to 35.2 for total nitrite plus nitrate. Regression equations also were used to estimate daily constituent loads. Load estimates can be used by water-quality managers for comparison of current water-quality conditions to water-quality standards expressed as total maximum daily loads (TMDLs). TMDLs are a measure of the maximum amount of chemical constituents that a water body can receive and still meet established water-quality standards. The peak loads generally occurred in June and July when streamflow also peaked.

Design of overlays based on pavement condition, roughness, and deflections : part 1 : tentative method for overlay design based on visual pavement distress.

DOT National Transportation Integrated Search

1978-01-01

Data collected on 111 interstate highway projects in Virginia were analyzed by multi-regression analysis and the rating coefficient for each type of distress determined. By this means, the total pavement distress and, hence, the maintenance rating of...

Evaluating Effectiveness of Green Infrastructure Application of Stormwater Best Management Practices in Protecting Stream Habitat and Biotic Condition in New England

EPA Science Inventory

The US EPA is developing assessment tools to evaluate the effectiveness of green infrastructure (GI) applied in stormwater best management practices (BMPs) at the small watershed (HUC12 or finer) scale. Based on analysis of historical monitoring data using boosted regression tre...

The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments

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

Bihn T. Pham; Jeffrey J. Einerson

2010-06-01

This paper describes the development and application of statistical analysis techniques to support the AGR experimental program on NGNP fuel performance. The experiments conducted in the Idaho National Laboratory’s Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel/graphite temperature) is regulated by the He-Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the SAS-based NGNP Data Management and Analysis System (NDMAS) for automatedmore » processing and qualification of the AGR measured data. The NDMAS also stores daily neutronic (power) and thermal (heat transfer) code simulation results along with the measurement data, allowing for their combined use and comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the target quantity (fuel temperature) within a given range.« less

Understanding the alcohol harm paradox: an analysis of sex- and condition-specific hospital admissions by socio-economic group for alcohol-associated conditions in England.

PubMed

Sadler, Susannah; Angus, Colin; Gavens, Lucy; Gillespie, Duncan; Holmes, John; Hamilton, Jean; Brennan, Alan; Meier, Petra

2017-05-01

In many countries, conflicting gradients in alcohol consumption and alcohol-associated mortality have been observed. To understand this 'alcohol harm paradox' we analysed the socio-economic gradient in alcohol-associated hospital admissions to test whether it was greater in conditions which were: (1) chronic (associated with long-term drinking) and partially alcohol-attributable, (2) chronic and wholly alcohol-attributable, (3) acute (associated with intoxication) and partially alcohol-attributable and (4) acute and wholly alcohol-attributable. Our aim was to clarify how (1) drinking patterns (e.g. intoxication linked to acute admissions or dependence linked to chronic conditions) and (2) non-alcohol causes (e.g. smoking and poor diet which are risks for partially alcohol-attributable conditions) contribute to the paradox. Regression analysis testing the modifying effects of condition-group (1-4 above) and sex on the relationship between area-based deprivation and admissions. England, April 2010-March 2013. A total of 9 239 629 English hospital admissions where a primary or secondary cause was one of 36 alcohol-associated conditions. Admissions by condition and deciles of Index of Multiple Deprivation (IMD). Socio-economic gradient measured as the relative index of inequality (RII, the slope of a linear regression of IMD on admissions adjusted for overall admission rate). Conditions were categorized by ICD-10 code. A socio-economic gradient in hospitalizations was seen for all conditions, except partially attributable chronic conditions. The gradient was significantly steeper for conditions which were wholly attributable to alcohol and for acute conditions than for conditions partially alcohol-attributable and for chronic conditions. Gradients were steeper for men than for women in cases of wholly alcohol attributable conditions. There is a socio-economic gradient in English hospital admission for most alcohol-associated conditions. The greatest inequalities are in conditions associated with alcohol dependence, such as liver disease and mental and behavioural conditions, and in acute conditions, such as alcohol poisoning and assault. Socio-economic differences in harmful drinking patterns (dependence and intoxication) may contribute to the 'alcohol harm paradox'. © 2016 Society for the Study of Addiction.

Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA

USGS Publications Warehouse

Patino, Reynaldo; VanLandeghem, Matthew M.; Goodbred, Steven L.; Orsak, Erik; Jenkins, Jill A.; Echols, Kathy R.; Rosen, Michael R.; Torres, Leticia

2015-01-01

Adult male Common Carp were sampled in 2007/08 over a full reproductive cycle at Lake Mead National Recreation Area. Sites sampled included a stream dominated by treated wastewater effluent, a lake basin receiving the streamflow, an upstream lake basin (reference), and a site below Hoover Dam. Individual body burdens for 252 contaminants were measured, and biological variables assessed included physiological [plasma vitellogenin (VTG), estradiol-17β (E2), 11-ketotestosterone (11KT)] and organ [gonadosomatic index (GSI)] endpoints. Patterns in contaminant composition and biological condition were determined by Principal Component Analysis, and their associations modeled by Principal Component Regression. Three spatially distinct but temporally stable gradients of contaminant distribution were recognized: a contaminant mixture typical of wastewaters (PBDEs, methyl triclosan, galaxolide), PCBs, and DDTs. Two spatiotemporally variable patterns of biological condition were recognized: a primary pattern consisting of reproductive condition variables (11KT, E2, GSI), and a secondary pattern including general condition traits (condition factor, hematocrit, fork length). VTG was low in all fish, indicating low estrogenic activity of water at all sites. Wastewater contaminants associated negatively with GSI, 11KT and E2; PCBs associated negatively with GSI and 11KT; and DDTs associated positively with GSI and 11KT. Regression of GSI on sex steroids revealed a novel, nonlinear association between these variables. Inclusion of sex steroids in the GSI regression on contaminants rendered wastewater contaminants nonsignificant in the model and reduced the influence of PCBs and DDTs. Thus, the influence of contaminants on GSI may have been partially driven by organismal modes-of-action that include changes in sex steroid production. The positive association of DDTs with 11KT and GSI suggests that lifetime, sub-lethal exposures to DDTs have effects on male carp opposite of those reported by studies where exposure concentrations were relatively high. Lastly, this study highlighted advantages of multivariate/multiple regression approaches for exploring associations between complex contaminant mixtures and gradients and reproductive condition in wild fishes.

Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA.

PubMed

Patiño, Reynaldo; VanLandeghem, Matthew M; Goodbred, Steven L; Orsak, Erik; Jenkins, Jill A; Echols, Kathy; Rosen, Michael R; Torres, Leticia

2015-08-01

Adult male Common Carp were sampled in 2007/08 over a full reproductive cycle at Lake Mead National Recreation Area. Sites sampled included a stream dominated by treated wastewater effluent, a lake basin receiving the streamflow, an upstream lake basin (reference), and a site below Hoover Dam. Individual body burdens for 252 contaminants were measured, and biological variables assessed included physiological [plasma vitellogenin (VTG), estradiol-17β (E2), 11-ketotestosterone (11KT)] and organ [gonadosomatic index (GSI)] endpoints. Patterns in contaminant composition and biological condition were determined by Principal Component Analysis, and their associations modeled by Principal Component Regression. Three spatially distinct but temporally stable gradients of contaminant distribution were recognized: a contaminant mixture typical of wastewaters (PBDEs, methyl triclosan, galaxolide), PCBs, and DDTs. Two spatiotemporally variable patterns of biological condition were recognized: a primary pattern consisting of reproductive condition variables (11KT, E2, GSI), and a secondary pattern including general condition traits (condition factor, hematocrit, fork length). VTG was low in all fish, indicating low estrogenic activity of water at all sites. Wastewater contaminants associated negatively with GSI, 11KT and E2; PCBs associated negatively with GSI and 11KT; and DDTs associated positively with GSI and 11KT. Regression of GSI on sex steroids revealed a novel, nonlinear association between these variables. Inclusion of sex steroids in the GSI regression on contaminants rendered wastewater contaminants nonsignificant in the model and reduced the influence of PCBs and DDTs. Thus, the influence of contaminants on GSI may have been partially driven by organismal modes-of-action that include changes in sex steroid production. The positive association of DDTs with 11KT and GSI suggests that lifetime, sub-lethal exposures to DDTs have effects on male carp opposite of those reported by studies where exposure concentrations were relatively high. Lastly, this study highlighted advantages of multivariate/multiple regression approaches for exploring associations between complex contaminant mixtures and gradients and reproductive condition in wild fishes. Published by Elsevier Inc.

HIGHLIGHTING DIFFERENCES BETWEEN CONDITIONAL AND UNCONDITIONAL QUANTILE REGRESSION APPROACHES THROUGH AN APPLICATION TO ASSESS MEDICATION ADHERENCE

PubMed Central

BORAH, BIJAN J.; BASU, ANIRBAN

2014-01-01

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. PMID:23616446

Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study

NASA Astrophysics Data System (ADS)

Takeshita, Toru; Kageyama, Shinya; Furuta, Michiko; Tsuboi, Hidenori; Takeuchi, Kenji; Shibata, Yukie; Shimazaki, Yoshihiro; Akifusa, Sumio; Ninomiya, Toshiharu; Kiyohara, Yutaka; Yamashita, Yoshihisa

2016-02-01

This population-based study determined the salivary microbiota composition of 2,343 adult residents of Hisayama town, Japan, using 16S rRNA gene next-generation high-throughput sequencing. Of 550 identified species-level operational taxonomic units (OTUs), 72 were common, in ≥75% of all individuals, as well as in ≥75% of the individuals in the lowest quintile of phylogenetic diversity (PD). These “core” OTUs constituted 90.9 ± 6.1% of each microbiome. The relative abundance profiles of 22 of the core OTUs with mean relative abundances ≥1% were stratified into community type I and community type II by partitioning around medoids clustering. Multiple regression analysis revealed that a lower PD was associated with better conditions for oral health, including a lower plaque index, absence of decayed teeth, less gingival bleeding, shallower periodontal pockets and not smoking, and was also associated with tooth loss. By contrast, multiple Poisson regression analysis demonstrated that community type II, as characterized by a higher ratio of the nine dominant core OTUs, including Neisseria flavescens, was implicated in younger age, lower body mass index, fewer teeth with caries experience, and not smoking. Our large-scale data analyses reveal variation in the salivary microbiome among Japanese adults and oral health-related conditions associated with the salivary microbiome.

Detection of outliers in the response and explanatory variables of the simple circular regression model

NASA Astrophysics Data System (ADS)

Mahmood, Ehab A.; Rana, Sohel; Hussin, Abdul Ghapor; Midi, Habshah

2016-06-01

The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points "outliers" in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates.

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.

1982-01-01

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

[Mathematical modeling for conditionality of cardiovascular disease by housing conditions].

PubMed

Meshkov, N A

2014-01-01

There was studied the influence of living conditions (housing area per capita, availability of housing water supply, sewerage and central heating) on the morbidity of the cardiovascular diseases in child and adult population. With the method of regression analysis the morbidity rate was established to significantly decrease with the increase in the area of housing, constructed models are statistically significant, respectively, p = 0.01 and p = 0.02. There was revealed the relationship of the morbidity rate of cardiovascular diseases in children and adults with the supply with housing central heating (p = 0.02 and p = 0.009).

Spatial analysis of relative humidity during ungauged periods in a mountainous region

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Kim, Yeonjoo

2017-08-01

Although atmospheric humidity influences environmental and agricultural conditions, thereby influencing plant growth, human health, and air pollution, efforts to develop spatial maps of atmospheric humidity using statistical approaches have thus far been limited. This study therefore aims to develop statistical approaches for inferring the spatial distribution of relative humidity (RH) for a mountainous island, for which data are not uniformly available across the region. A multiple regression analysis based on various mathematical models was used to identify the optimal model for estimating monthly RH by incorporating not only temperature but also location and elevation. Based on the regression analysis, we extended the monthly RH data from weather stations to cover the ungauged periods when no RH observations were available. Then, two different types of station-based data, the observational data and the data extended via the regression model, were used to form grid-based data with a resolution of 100 m. The grid-based data that used the extended station-based data captured the increasing RH trend along an elevation gradient. Furthermore, annual RH values averaged over the regions were examined. Decreasing temporal trends were found in most cases, with magnitudes varying based on the season and region.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

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…

Evaluation of job satisfaction and working atmosphere of dental nurses in Germany.

PubMed

Goetz, Katja; Hasse, Philipp; Campbell, Stephen M; Berger, Sarah; Dörfer, Christof E; Hahn, Karolin; Szecsenyi, Joachim

2016-02-01

The purpose of the study was to assess the level of job satisfaction of dental nurses in ambulatory care and to explore the impact of aspects of working atmosphere on and their association with job satisfaction. This cross-sectional study was based on a job satisfaction survey. Data were collected from 612 dental nurses working in 106 dental care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Working atmosphere was measured with five items. Linear regression analyses were performed in which each item of the job satisfaction scale was handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction and the five items of working atmosphere, job satisfaction, and individual characteristics. The response rate was 88.3%. Dental nurses were satisfied with 'colleagues' and least satisfied with 'income.' Different aspects of job satisfaction were mostly associated with the following working atmosphere issues: 'responsibilities within the practice team are clear,' 'suggestions for improvement are taken seriously,' 'working atmosphere in the practice team is good,' and 'made easier to admit own mistakes.' Within the stepwise linear regression analysis, the aspect 'physical working condition' (β = 0.304) showed the highest association with overall job satisfaction. The total explained variance of the 14 associated variables was 0.722 with overall job satisfaction. Working atmosphere within this discrete sample of dental care practice seemed to be an important influence on reported working condition and job satisfaction for dental nurses. Because of the high association of job satisfaction with physical working condition, the importance of paying more attention to an ergonomic working position for dental nurses to ensure optimal quality of care is highlighted. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.

PubMed

Zheng, Jie; Erzurumluoglu, A Mesut; Elsworth, Benjamin L; Kemp, John P; Howe, Laurence; Haycock, Philip C; Hemani, Gibran; Tansey, Katherine; Laurin, Charles; Pourcain, Beate St; Warrington, Nicole M; Finucane, Hilary K; Price, Alkes L; Bulik-Sullivan, Brendan K; Anttila, Verneri; Paternoster, Lavinia; Gaunt, Tom R; Evans, David M; Neale, Benjamin M

2017-01-15

LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Correlation Equations for Condensing Heat Exchangers Based on an Algorithmic Performance-Data Classification

NASA Astrophysics Data System (ADS)

Pacheco-Vega, Arturo

2016-09-01

In this work a new set of correlation equations is developed and introduced to accurately describe the thermal performance of compact heat exchangers with possible condensation. The feasible operating conditions for the thermal system correspond to dry- surface, dropwise condensation, and film condensation. Using a prescribed form for each condition, a global regression analysis for the best-fit correlation to experimental data is carried out with a simulated annealing optimization technique. The experimental data were taken from the literature and algorithmically classified into three groups -related to the possible operating conditions- with a previously-introduced Gaussian-mixture-based methodology. Prior to their use in the analysis, the correct data classification was assessed and confirmed via artificial neural networks. Predictions from the correlations obtained for the different conditions are within the uncertainty of the experiments and substantially more accurate than those commonly used.

Extrapolating intensified forest inventory data to the surrounding landscape using landsat

Treesearch

Evan B. Brooks; John W. Coulston; Valerie A. Thomas; Randolph H. Wynne

2015-01-01

In 2011, a collection of spatially intensified plots was established on three of the Experimental Forests and Ranges (EFRs) sites with the intent of facilitating FIA program objectives for regional extrapolation. Characteristic coefficients from harmonic regression (HR) analysis of associated Landsat stacks are used as inputs into a conditional random forests model to...

Morphological Features in Children with Autism Spectrum Disorders: A Matched Case-Control Study

ERIC Educational Resources Information Center

Ozgen, Heval; Hellemann, Gerhard S.; Stellato, Rebecca K.; Lahuis, Bertine; van Daalen, Emma; Staal, Wouter G.; Rozendal, Marije; Hennekam, Raoul C.; Beemer, Frits A.; van Engeland, Herman

2011-01-01

This study was designed to examine morphological features in a large group of children with autism spectrum disorder versus normal controls. Amongst 421 patients and 1,007 controls, 224 matched pairs were created. Prevalence rates and odds ratios were analyzed by conditional regression analysis, McNemar test or paired t-test matched pairs.…

Methods for Improving Information from "Undesigned" Human Factors Experiments. Technical Report No. p75-287.

ERIC Educational Resources Information Center

Simon, Charles W.

An "undesigned" experiment is one in which the predictor variables are correlated, either due to a failure to complete a design or because the investigator was unable to select or control relevant experimental conditions. The traditional method of analyzing this class of experiment--multiple regression analysis based on a least squares…

Post-fire tree establishment patterns at the alpine treeline ecotone: Mount Rainier National Park, Washington, USA

Treesearch

Kirk M. Stueve; Dawna L. Cerney; Regina M. Rochefort; Laurie L. Kurth

2009-01-01

We performed classification analysis of 1970 satellite imagery and 2003 aerial photography to delineate establishment. Local site conditions were calculated from a LIDAR-based DEM, ancillary climate data, and 1970 tree locations in a GIS. We used logistic regression on a spatially weighted landscape matrix to rank variables.

Categorized or Continuous? Strength of an Association--And Linear Regression

ERIC Educational Resources Information Center

Drummond, Gordon B.; Vowler, Sarah L.

2012-01-01

In this article, the authors consider the possibility that groups could be different, because of the different conditions of a factor. This is as far as the analysis can extend: the consideration is restricted to groups characterized by the different category of the factor being considered. In many biological experiments, the factor considered may…

Pickled egg production: effect of brine acetic acid concentration and packing conditions on acidification rate.

PubMed

Acosta, Oscar; Gao, Xiaofan; Sullivan, Elizabeth K; Padilla-Zakour, Olga I

2014-05-01

U.S. federal regulations require that acidified foods must reach a pH of 4.6 or lower within 24 h of packaging or be kept refrigerated until then. Processes and formulations should be designed to satisfy this requirement, unless proper studies demonstrate the safety of other conditions. Our objective was to determine the effect of brine acetic acid concentration and packing conditions on the acidification rate of hard-boiled eggs. Eggs were acidified (60/40 egg-to-brine ratio) at various conditions of brine temperature, heat treatment to filled jars, and postpacking temperature: (i) 25 °C/none/25 °C (cold fill), (ii) 25 °C/none/2 °C (cold fill/refrigerated), (iii) 85 °C/none/25 °C (hot fill), and (iv) 25 °C/100 °C for 16 min/25 °C (water bath). Three brine concentrations were evaluated (7.5, 4.9, and 2.5% acetic acid) and egg pH values (whole, yolk, four points within egg) were measured from 4 to 144 h, with eggs equilibrating at pH 3.8, 4.0, and 4.3, respectively. Experiments were conducted in triplicate, and effects were considered significant when P < 0.05. Multiple linear regression analysis was conducted to evaluate the effect on pH values at the center of the yolk. Regression analysis showed that brine concentration of 2.5% decreased the acidification rate, while packing conditions of the hot fill trial increased it. Inverse prediction was used to determine the time for the center of the yolk and the total yolk to reach a pH value of 4.6. These results demonstrate the importance of conducting acidification studies with proper pH measurements to determine safe conditions to manufacture commercially stable pickled eggs.

Analysis of National Trends in Hospital Acquired Conditions Following Major Urological Surgery Before and After Implementation of the Hospital Acquired Condition Reduction Program,,✰✰✰.

PubMed

Rude, Tope L; Donin, Nicholas M; Cohn, Matthew R; Meeks, William; Gulig, Scott; Patel, Samir N; Wysock, James S; Makarov, Danil V; Bjurlin, Marc A

2018-06-07

To define the rates of common Hospital Acquired Conditions (HACs) in patients undergoing major urological surgery over a period of time encompassing the implementation of the Hospital Acquired Condition Reduction program, and to evaluate whether implementation of the HAC reimbursement penalties in 2008 was associated with a change in the rate of HACs. Using American College of Surgeons National Surgical Quality Improvement Program (NSQIP) data, we determined rates of HACs in patients undergoing major inpatient urological surgery from 2005 to 2012. Rates were stratified by procedure type and approach (open vs. laparoscopic/robotic). Multivariable logistic regression was used to determine the association between year of surgery and HACs. We identified 39,257 patients undergoing major urological surgery, of whom 2300 (5.9%) had at least one hospital acquired condition. Urinary tract infection (UTI, 2.6%) was the most common, followed by surgical site infection (SSI, 2.5%) and venous thrombotic events (VTE, 0.7%). Multivariable logistic regression analysis demonstrated that open surgical approach, diabetes, congestive heart failure, chronic obstructive pulmonary disease, weight loss, and ASA class were among the variables associated with higher likelihood of HAC. We observed a non-significant secular trend of decreasing rates of HAC from 7.4% to 5.8% HACs during the study period, which encompassed the implementation of the Hospital Acquired Condition Reduction Program. HACs occurred at a rate of 5.9% after major urological surgery, and are significantly affected by procedure type and patient health status. The rate of HAC appeared unaffected by national reduction program in this cohort. Better understanding of the factors associated with HACs is critical in developing effective reduction programs. Copyright © 2018. Published by Elsevier Inc.

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.

Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

DOE PAGES

Stewart, James A.; Kohnert, Aaron A.; Capolungo, Laurent; ...

2018-03-06

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditionsmore » can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

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

Stewart, James A.; Kohnert, Aaron A.; Capolungo, Laurent

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditionsmore » can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

Vascular Disease, ESRD, and Death: Interpreting Competing Risk Analyses

PubMed Central

Coresh, Josef; Segev, Dorry L.; Kucirka, Lauren M.; Tighiouart, Hocine; Sarnak, Mark J.

2012-01-01

Summary Background and objectives Vascular disease, a common condition in CKD, is a risk factor for mortality and ESRD. Optimal patient care requires accurate estimation and ordering of these competing risks. Design, setting, participants, & measurements This is a prospective cohort study of screened (n=885) and randomized participants (n=837) in the Modification of Diet in Renal Disease study (original study enrollment, 1989–1992), evaluating the association of vascular disease with ESRD and pre-ESRD mortality using standard survival analysis and competing risk regression. Results The method of analysis resulted in markedly different estimates. Cumulative incidence by standard analysis (censoring at the competing event) implied that, with vascular disease, the 15-year incidence was 66% and 51% for ESRD and pre-ESRD death, respectively. A more accurate representation of absolute risk was estimated with competing risk regression: 15-year incidence was 54% and 29% for ESRD and pre-ESRD death, respectively. For the association of vascular disease with pre-ESRD death, estimates of relative risk by the two methods were similar (standard survival analysis adjusted hazard ratio, 1.63; 95% confidence interval, 1.20–2.20; competing risk regression adjusted subhazard ratio, 1.57; 95% confidence interval, 1.15–2.14). In contrast, the hazard and subhazard ratios differed substantially for other associations, such as GFR and pre-ESRD mortality. Conclusions When competing events exist, absolute risk is better estimated using competing risk regression, but etiologic associations by this method must be carefully interpreted. The presence of vascular disease in CKD decreases the likelihood of survival to ESRD, independent of age and other risk factors. PMID:22859747

Vascular disease, ESRD, and death: interpreting competing risk analyses.

PubMed

Grams, Morgan E; Coresh, Josef; Segev, Dorry L; Kucirka, Lauren M; Tighiouart, Hocine; Sarnak, Mark J

2012-10-01

Vascular disease, a common condition in CKD, is a risk factor for mortality and ESRD. Optimal patient care requires accurate estimation and ordering of these competing risks. This is a prospective cohort study of screened (n=885) and randomized participants (n=837) in the Modification of Diet in Renal Disease study (original study enrollment, 1989-1992), evaluating the association of vascular disease with ESRD and pre-ESRD mortality using standard survival analysis and competing risk regression. The method of analysis resulted in markedly different estimates. Cumulative incidence by standard analysis (censoring at the competing event) implied that, with vascular disease, the 15-year incidence was 66% and 51% for ESRD and pre-ESRD death, respectively. A more accurate representation of absolute risk was estimated with competing risk regression: 15-year incidence was 54% and 29% for ESRD and pre-ESRD death, respectively. For the association of vascular disease with pre-ESRD death, estimates of relative risk by the two methods were similar (standard survival analysis adjusted hazard ratio, 1.63; 95% confidence interval, 1.20-2.20; competing risk regression adjusted subhazard ratio, 1.57; 95% confidence interval, 1.15-2.14). In contrast, the hazard and subhazard ratios differed substantially for other associations, such as GFR and pre-ESRD mortality. When competing events exist, absolute risk is better estimated using competing risk regression, but etiologic associations by this method must be carefully interpreted. The presence of vascular disease in CKD decreases the likelihood of survival to ESRD, independent of age and other risk factors.

Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race.

PubMed

Bowdish, G E; Cordell, W H; Bock, H C; Vukov, L F

1992-10-01

Emergency physicians often plan and provide on-site medical care for mass gatherings. Most of the mass gathering literature is descriptive. Only a few studies have looked at factors such as crowd size, event characteristics, or weather in predicting numbers and types of patients at mass gatherings. We used regression analysis to relate patient volume on Race Day at the Indianapolis Motor Speedway to weather conditions and race characteristics. Race Day weather data for the years 1983 to 1989 were obtained from the National Oceanic and Atmospheric Administration. Data regarding patients treated on 1983 to 1989 Race Days were obtained from the facility hospital (Hannah Emergency Medical Center) data base. Regression analysis was performed using weather factors and race characteristics as independent variables and number of patients seen as the dependent variable. Data from 1990 were used to test the validity of the model. There was a significant relationship between dew point (which is calculated from temperature and humidity) and patient load (P less than .01). Dew point, however, failed to predict patient load during the 1990 race. No relationships could be established between humidity, sunshine, wind, or race characteristics and number of patients. Although higher dew point was associated with higher patient load during the 1983 to 1989 races, dew point was a poor predictor of patient load during the 1990 race. Regression analysis may be useful in identifying relationships between event characteristics and patient load but is probably inadequate to explain the complexities of crowd behavior and too simplified to use as a prediction tool.

Self-consistent asset pricing models

NASA Astrophysics Data System (ADS)

Malevergne, Y.; Sornette, D.

2007-08-01

We discuss the foundations of factor or regression models in the light of the self-consistency condition that the market portfolio (and more generally the risk factors) is (are) constituted of the assets whose returns it is (they are) supposed to explain. As already reported in several articles, self-consistency implies correlations between the return disturbances. As a consequence, the alphas and betas of the factor model are unobservable. Self-consistency leads to renormalized betas with zero effective alphas, which are observable with standard OLS regressions. When the conditions derived from internal consistency are not met, the model is necessarily incomplete, which means that some sources of risk cannot be replicated (or hedged) by a portfolio of stocks traded on the market, even for infinite economies. Analytical derivations and numerical simulations show that, for arbitrary choices of the proxy which are different from the true market portfolio, a modified linear regression holds with a non-zero value αi at the origin between an asset i's return and the proxy's return. Self-consistency also introduces “orthogonality” and “normality” conditions linking the betas, alphas (as well as the residuals) and the weights of the proxy portfolio. Two diagnostics based on these orthogonality and normality conditions are implemented on a basket of 323 assets which have been components of the S&P500 in the period from January 1990 to February 2005. These two diagnostics show interesting departures from dynamical self-consistency starting about 2 years before the end of the Internet bubble. Assuming that the CAPM holds with the self-consistency condition, the OLS method automatically obeys the resulting orthogonality and normality conditions and therefore provides a simple way to self-consistently assess the parameters of the model by using proxy portfolios made only of the assets which are used in the CAPM regressions. Finally, the factor decomposition with the self-consistency condition derives a risk-factor decomposition in the multi-factor case which is identical to the principal component analysis (PCA), thus providing a direct link between model-driven and data-driven constructions of risk factors. This correspondence shows that PCA will therefore suffer from the same limitations as the CAPM and its multi-factor generalization, namely lack of out-of-sample explanatory power and predictability. In the multi-period context, the self-consistency conditions force the betas to be time-dependent with specific constraints.

Mixed conditional logistic regression for habitat selection studies.

PubMed

Duchesne, Thierry; Fortin, Daniel; Courbin, Nicolas

2010-05-01

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.

Two-dimensional advective transport in ground-water flow parameter estimation

USGS Publications Warehouse

Anderman, E.R.; Hill, M.C.; Poeter, E.P.

1996-01-01

Nonlinear regression is useful in ground-water flow parameter estimation, but problems of parameter insensitivity and correlation often exist given commonly available hydraulic-head and head-dependent flow (for example, stream and lake gain or loss) observations. To address this problem, advective-transport observations are added to the ground-water flow, parameter-estimation model MODFLOWP using particle-tracking methods. The resulting model is used to investigate the importance of advective-transport observations relative to head-dependent flow observations when either or both are used in conjunction with hydraulic-head observations in a simulation of the sewage-discharge plume at Otis Air Force Base, Cape Cod, Massachusetts, USA. The analysis procedure for evaluating the probable effect of new observations on the regression results consists of two steps: (1) parameter sensitivities and correlations calculated at initial parameter values are used to assess the model parameterization and expected relative contributions of different types of observations to the regression; and (2) optimal parameter values are estimated by nonlinear regression and evaluated. In the Cape Cod parameter-estimation model, advective-transport observations did not significantly increase the overall parameter sensitivity; however: (1) inclusion of advective-transport observations decreased parameter correlation enough for more unique parameter values to be estimated by the regression; (2) realistic uncertainties in advective-transport observations had a small effect on parameter estimates relative to the precision with which the parameters were estimated; and (3) the regression results and sensitivity analysis provided insight into the dynamics of the ground-water flow system, especially the importance of accurate boundary conditions. In this work, advective-transport observations improved the calibration of the model and the estimation of ground-water flow parameters, and use of regression and related techniques produced significant insight into the physical system.

An Analysis of the Number of Medical Malpractice Claims and Their Amounts

PubMed Central

Bonetti, Marco; Cirillo, Pasquale; Musile Tanzi, Paola; Trinchero, Elisabetta

2016-01-01

Starting from an extensive database, pooling 9 years of data from the top three insurance brokers in Italy, and containing 38125 reported claims due to alleged cases of medical malpractice, we use an inhomogeneous Poisson process to model the number of medical malpractice claims in Italy. The intensity of the process is allowed to vary over time, and it depends on a set of covariates, like the size of the hospital, the medical department and the complexity of the medical operations performed. We choose the combination medical department by hospital as the unit of analysis. Together with the number of claims, we also model the associated amounts paid by insurance companies, using a two-stage regression model. In particular, we use logistic regression for the probability that a claim is closed with a zero payment, whereas, conditionally on the fact that an amount is strictly positive, we make use of lognormal regression to model it as a function of several covariates. The model produces estimates and forecasts that are relevant to both insurance companies and hospitals, for quality assurance, service improvement and cost reduction. PMID:27077661

“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data

PubMed Central

Zhang, Min; Davidian, Marie

2008-01-01

Summary A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild “smoothness” conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a “parametric” representation, which makes likelihood-based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time-to-event studies. PMID:17970813

Multiple regression analysis of factors influencing dominant hand grip strength in an adult Malaysian population.

PubMed

Hossain, M G; Zyroul, R; Pereira, B P; Kamarul, T

2012-01-01

Grip strength is an important measure used to monitor the progression of a condition, and to evaluate outcomes of treatment. We assessed how various physical and social factors predict normal grip strength in an adult Malaysian population of mixed Asian ethnicity (254 men, 246 women). Grip strength was recorded using the Jamar dynamometer. The mean grip strength for the dominant hand was 29.8 kg for men and 17.6 kg for women. Multiple regression analysis demonstrated that the dominant hand grip strength was positively associated with height and body mass index, and negatively associated with age for both sexes. Dominant hand grip strength was related to work status for men (p < 0.05) but not for women. However, there was no difference in grip strength among ethnic groups.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions.

PubMed

Drouard, Vincent; Horaud, Radu; Deleforge, Antoine; Ba, Sileye; Evangelidis, Georgios

2017-03-01

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

NASA Astrophysics Data System (ADS)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Association between bullous pemphigoid and neurologic diseases: a case-control study.

PubMed

Casas-de-la-Asunción, E; Ruano-Ruiz, J; Rodríguez-Martín, A M; Vélez García-Nieto, A; Moreno-Giménez, J C

2014-11-01

In the past 10 years, bullous pemphigoid has been associated with other comorbidities and neurologic and psychiatric conditions in particular. Case series, small case-control studies, and large population-based studies in different Asian populations, mainland Europe, and the United Kingdom have confirmed this association. However, no data are available for the Spanish population. This was an observational, retrospective, case-control study with 1:2 matching. Fifty-four patients with bullous pemphigoid were selected. We compared the percentage of patients in each group with concurrent neurologic conditions, ischemic heart disease, diabetes, chronic obstructive pulmonary disease, and solid tumors using univariate logistic regression. An association model was constructed with conditional multiple logistic regression. The case group had a significantly higher percentage of patients with cerebrovascular accident and/or transient ischemic attack (odds ratio [OR], 3.06; 95% CI, 1.19-7.87], dementia (OR, 5.52; 95% CI, 2.19-13.93), and Parkinson disease (OR, 5; 95% CI, 1.57-15.94). A significantly higher percentage of cases had neurologic conditions (OR, 6.34; 95% CI, 2.89-13.91). Dementia and Parkinson disease were independently associated with bullous pemphigoid in the multivariate analysis. Patients with bullous pemphigoid have a higher frequency of neurologic conditions. Copyright © 2013 Elsevier España, S.L.U. and AEDV. All rights reserved.

Effects of Barometric Fluctuations on Well Water-Level Measurements and Aquifer Test Data

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

Spane, Frank A.

1999-12-16

This report examines the effects of barometric fluctuations on well water-level measurements and evaluates adjustment and removal methods for determining areal aquifer head conditions and aquifer test analysis. Two examples of Hanford Site unconfined aquifer tests are examined that demonstrate baro-metric response analysis and illustrate the predictive/removal capabilities of various methods for well water-level and aquifer total head values. Good predictive/removal characteristics were demonstrated with best corrective results provided by multiple-regression deconvolution methods.

Hospitalizations for primary care-sensitive conditions in Pelotas, Brazil: 1998 to 2012.

PubMed

Costa, Juvenal Soares Dias da; Teixeira, Ana Maria Ferreira Borges; Moraes, Mauricio; Strauch, Eliane Schneider; Silveira, Denise Silva da; Carret, Maria Laura Vidal; Fantinel, Everton

2017-01-01

To verify the hospitalization trend for primary care sensitive-conditions in Pelotas, Rio Grande do Sul, Brazil from 1998 to 2012. An ecological study compared hospitalizations rates of the city of Pelotas with the rest of state of Rio Grande do Sul. Analysis was conducted using direct standardization of rates, coefficients were stratified by sex and the Poisson regression was used. Hospitalizations for sensitive conditions decreased in Pelotas and Rio Grande do Sul. In Pelotas, a 63.8% decrease was detected in the period observed, and there was a 43.1% decrease in the state of Rio Grande do Sul. Poisson regression coefficients showed a decrease of 7% in Pelotas and of 4% in the rest of Rio Grande do Sul each year. During the study period, several changes were introduced in the Brazilian Unified Health System ("Sistema Único de Saúde") that may have influenced the results, including changes in administration, health funding, and a complete reworking of primary care through the creation of the Family Health Strategy program ("Estratégia Saúde da Família").

Heat rejection efficiency research of new energy automobile radiators

NASA Astrophysics Data System (ADS)

Ma, W. S.; Shen, W. X.; Zhang, L. W.

2018-03-01

The driving system of new energy vehicle has larger heat load than conventional engine. How to ensure the heat dissipation performance of the cooling system is the focus of the design of new energy vehicle thermal management system. In this paper, the heat dissipation efficiency of the radiator of the hybrid electric vehicle is taken as the research object, the heat dissipation efficiency of the radiator of the new energy vehicle is studied through the multi-working-condition enthalpy difference test. In this paper, the test method in the current standard QC/T 468-2010 “automobile radiator” is taken, but not limited to the test conditions specified in the standard, 5 types of automobile radiator are chosen, each of them is tested 20 times in simulated condition of different wind speed and engine inlet temperature. Finally, regression analysis is carried out for the test results, and regression equation describing the relationship of radiator heat dissipation heat dissipation efficiency air side flow rate cooling medium velocity and inlet air temperature is obtained, and the influence rule is systematically discussed.

Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design.

PubMed

Goldstein, Alisa M; Dondon, Marie-Gabrielle; Andrieu, Nadine

2006-08-01

A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene-environment (GxE) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case-control study for detecting interaction involving rare events. We now propose an unconditional analytic strategy to further increase the power for detecting gene-environment (GxE) interactions. This strategy allows the estimation of GxE interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased. Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting GxE interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small. Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case-control pairs are informative leading to more precise estimates of the odds ratios.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Supported employment for people with severe mental illness: systematic review and meta-analysis of the international evidence.

PubMed

Modini, Matthew; Tan, Leona; Brinchmann, Beate; Wang, Min-Jung; Killackey, Eoin; Glozier, Nicholas; Mykletun, Arnstein; Harvey, Samuel B

2016-07-01

Individual placement and support (IPS) is a vocational rehabilitation programme that was developed in the USA to improve employment outcomes for people with severe mental illness. Its ability to be generalised to other countries and its effectiveness in varying economic conditions remains to be ascertained. To investigate whether IPS is effective across international settings and in different economic conditions. A systematic review and meta-analysis of randomised controlled trials comparing IPS with traditional vocational services was undertaken; 17 studies, as well as 2 follow-up studies, were included. Meta-regressions were carried out to examine whether IPS effectiveness varied according to geographic location, unemployment rates or gross domestic product (GDP) growth. The overall pooled risk ratio for competitive employment using IPS compared with traditional vocational rehabilitation was 2.40 (95% CI 1.99-2.90). Meta-regressions indicated that neither geographic area nor unemployment rates affected the overall effectiveness of IPS. Even when a country's GDP growth was less than 2% IPS was significantly more effective than traditional vocational training, and its benefits remained evident over 2 years. Individual placement and support is an effective intervention across a variety of settings and economic conditions and is more than twice as likely to lead to competitive employment when compared with traditional vocational rehabilitation. © The Royal College of Psychiatrists 2016.

Estimating Available Fuel Weight Consumed by Prescribed Fires in the South

Treesearch

Walter A. Hough

1978-01-01

A method is proposed for estimating the weight of fuel burned (available fuel) by prescribed fires in southern pine stands. Weights of available fuel in litter alone and in litter plus understory materials can be estimated. Prediction equations were developed by regression analysis of data from a variety of locations and stand conditions. They are most reliable for...

The Design and Analysis of Salmonid Tagging Studies in the Columbia Basin; Volume XII; A Multinomial Model for Estimating Ocean Survival from Salmonid Coded Wire-Tag Data.

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

Ryding, Kristen E.; Skalski, John R.

1999-06-01

The purpose of this report is to illustrate the development of a stochastic model using coded wire-tag (CWT) release and age-at-return data, in order to regress first year ocean survival probabilities against coastal ocean conditions and climate covariates.

A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

NASA Astrophysics Data System (ADS)

Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

2017-02-01

Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function.

PubMed

Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

2017-02-01

Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

Occlusal wear and occlusal condition in a convenience sample of young adults.

PubMed

Van't Spijker, A; Kreulen, C M; Bronkhorst, E M; Creugers, N H J

2015-01-01

To study progression of tooth wear quantitatively in a convenient sample of young adults and to assess possible correlations with occlusal conditions. Twenty-eight dental students participated in a three-year follow up study on tooth wear. Visible wear facets on full arch gypsum casts were assessed using a flatbed scanner and measuring software. Regression analyses were used to assess possible associations between the registered occlusal conditions 'occlusal guidance scheme', 'vertical overbite', 'horizontal overbite', 'depth of sagittal curve', 'canine Angle class relation', 'history of orthodontic treatment', and 'self-reported grinding/clenching' (independent variables) and increase of wear facets (dependent variable). Mean increase in facet surface areas ranged from 1.2 mm2 (premolars, incisors) to 3.4 mm2 (molars); the relative increase ranged from 15% to 23%. Backward regression analysis showed no significant relation for 'group function', 'vertical overbite', 'depth of sagittal curve', 'history of orthodontic treatment' nor 'self-reported clenching. The final multiple linear regression model showed significant associations amongst 'anterior protected articulation' and 'horizontal overbite' and increase of facet surface areas. For all teeth combined, only 'anterior protected articulation' had a significant effect. 'Self reported grinding' did not have a significant effect (p>0.07). In this study 'anterior protected articulation' and 'horizontal overbite', were significantly associated with the progression of tooth wear. Self reported grinding was not significantly associated with progression of tooth wear. Occlusal conditions such as anterior protected articulation and horizontal overbite seem to have an effect on the progression of occlusal tooth wear in this convenient sample of young adults. Copyright © 2014 Elsevier Ltd. All rights reserved.

Uncertainty and sensitivity analysis for two-phase flow in the vicinity of the repository in the 1996 performance assessment for the Waste Isolation Pilot Plant: Undisturbed conditions

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

HELTON,JON CRAIG; BEAN,J.E.; ECONOMY,K.

2000-05-19

Uncertainty and sensitivity analysis results obtained in the 1996 performance assessment for the Waste Isolation Pilot Plant are presented for two-phase flow the vicinity of the repository under undisturbed conditions. Techniques based on Latin hypercube sampling, examination of scatterplots, stepwise regression analysis, partial correlation analysis and rank transformation are used to investigate brine inflow, gas generation repository pressure, brine saturation and brine and gas outflow. Of the variables under study, repository pressure is potentially the most important due to its influence on spallings and direct brine releases, with the uncertainty in its value being dominated by the extent to whichmore » the microbial degradation of cellulose takes place, the rate at which the corrosion of steel takes place, and the amount of brine that drains from the surrounding disturbed rock zone into the repository.« less

Reference-Free Removal of EEG-fMRI Ballistocardiogram Artifacts with Harmonic Regression

PubMed Central

Krishnaswamy, Pavitra; Bonmassar, Giorgio; Poulsen, Catherine; Pierce, Eric T; Purdon, Patrick L.; Brown, Emery N.

2016-01-01

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI. PMID:26151100

Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors.

PubMed

Baek, Hyun Jae; Kim, Ko Keun; Kim, Jung Soo; Lee, Boreom; Park, Kwang Suk

2010-02-01

A new method of blood pressure (BP) estimation using multiple regression with pulse arrival time (PAT) and two confounding factors was evaluated in clinical and unconstrained monitoring situations. For the first analysis with clinical data, electrocardiogram (ECG), photoplethysmogram (PPG) and invasive BP signals were obtained by a conventional patient monitoring device during surgery. In the second analysis, ECG, PPG and non-invasive BP were measured using systems developed to obtain data under conditions in which the subject was not constrained. To enhance the performance of BP estimation methods, heart rate (HR) and arterial stiffness were considered as confounding factors in regression analysis. The PAT and HR were easily extracted from ECG and PPG signals. For arterial stiffness, the duration from the maximum derivative point to the maximum of the dicrotic notch in the PPG signal, a parameter called TDB, was employed. In two experiments that normally cause BP variation, the correlation between measured BP and the estimated BP was investigated. Multiple-regression analysis with the two confounding factors improved correlation coefficients for diastolic blood pressure and systolic blood pressure to acceptable confidence levels, compared to existing methods that consider PAT only. In addition, reproducibility for the proposed method was determined using constructed test sets. Our results demonstrate that non-invasive, non-intrusive BP estimation can be obtained using methods that can be applied in both clinical and daily healthcare situations.

Strengths use as a secret of happiness: Another dimension of visually impaired individuals' psychological state.

PubMed

Matsuguma, Shinichiro; Kawashima, Motoko; Negishi, Kazuno; Sano, Fumiya; Mimura, Masaru; Tsubota, Kazuo

2018-01-01

It is well recognized that visual impairments (VI) worsen individuals' mental condition. However, little is known about the positive aspects including subjective happiness, positive emotions, and strengths. Therefore, the purpose of this study was to investigate the positive aspects of persons with VI including their subjective happiness, positive emotions, and strengths use. Positive aspects of persons with VI were measured using the Subjective Happiness Scale (SHS), the Scale of Positive and Negative Experience-Balance (SPANE-B), and the Strengths Use Scale (SUS). A cross-sectional analysis was utilized to examine personal information in a Tokyo sample (N = 44). We used a simple regression analysis and found significant relationships between the SHS or SPANE-B and SUS; on the contrary, VI-related variables were not correlated with them. A multiple regression analysis confirmed that SUS was a significant factor associated with both the SHS and SPANE-B. Strengths use might be a possible protective factor from the negative effects of VI.

Boosted regression tree, table, and figure data

EPA Pesticide Factsheets

Spreadsheets are included here to support the manuscript Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition. This dataset is associated with the following publication:Golden , H., C. Lane , A. Prues, and E. D'Amico. Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition. JAWRA. American Water Resources Association, Middleburg, VA, USA, 52(5): 1251-1274, (2016).

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high degree of multicollinearity, possibly illustrating the co-evolution of climatic and physiographic conditions. Given the ineffectiveness of many variables used here, future work should develop new variables that target specific processes associated with percentile flows.

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

PubMed

Veerkamp, R F; Koenen, E P; De Jong, G

2001-10-01

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

Spatial analysis of ambulance response times related to prehospital cardiac arrests in the city-state of Singapore.

PubMed

Earnest, Arul; Hock Ong, Marcus Eng; Shahidah, Nur; Min Ng, Wen; Foo, Chuanyang; Nott, David John

2012-01-01

The main objective of this study was to establish the spatial variation in ambulance response times for out-of-hospital cardiac arrests (OHCAs) in the city-state of Singapore. The secondary objective involved studying the relationships between various covariates, such as traffic condition and time and day of collapse, and ambulance response times. The study design was observational and ecological in nature. Data on OHCAs were collected from a nationally representative database for the period October 2001 to October 2004. We used the conditional autoregressive (CAR) model to analyze the data. Within the Bayesian framework of analysis, we used a Weibull regression model that took into account spatial random effects. The regression model was used to study the independent effects of each covariate. Our results showed that there was spatial heterogeneity in the ambulance response times in Singapore. Generally, areas in the far outskirts (suburbs), such as Boon Lay (in the west) and Sembawang (in the north), fared badly in terms of ambulance response times. This improved when adjusted for key covariates, including distance from the nearest fire station. Ambulance response time was also associated with better traffic conditions, weekend OHCAs, distance from the nearest fire station, and OHCAs occurring during nonpeak driving hours. For instance, the hazard ratio for good ambulance response time was 2.35 (95% credible interval [CI] 1.97-2.81) when traffic conditions were light and 1.72 (95% CI 1.51-1.97) when traffic conditions were moderate, as compared with heavy traffic. We found a clear spatial gradient for ambulance response times, with far-outlying areas' exhibiting poorer response times. Our study highlights the utility of this novel approach, which may be helpful for planning emergency medical services and public emergency responses.

Effects of macroeconomic trends on social security spending due to sickness and disability.

PubMed

Khan, Jahangir; Gerdtham, Ulf-G; Jansson, Bjarne

2004-11-01

We analyzed the relationship between macroeconomic conditions, measured as unemployment rate and social security spending, from 4 social security schemes and total spending due to sickness and disability. We obtained aggregated panel data from 13 Organization for Economic Cooperation and Development member countries for 1980-1996. We used regression analysis and fixed effect models to examine spending on sickness benefits, disability pensions, occupational-injury benefits, survivor's pensions, and total spending. A decline in unemployment increased sickness benefits spending and reduced disability pension spending. These effects reversed direction after 4 years of unemployment. Inclusion of mortality rate as an additional variable in the analysis did not affect the findings. Macroeconomic conditions influence some reimbursements from social security schemes but not total spending.

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

Symptoms of musculoskeletal disorders among ammunition factory workers in Turkey.

PubMed

Pinar, Tevfik; Cakmak, Z Aytul; Saygun, Meral; Akdur, Recep; Ulu, Nuriye; Keles, Isik; Saylam, Hamdi Saim

2013-01-01

The aim of this study was to assess the prevalence of symptoms of work-related musculoskeletal disorders (MSDs) and to determine the risk factors among ammunition factory workers in Turkey. This cross-sectional study was performed on 955 ammunition factory workers. Potential risk factors were investigated with a questionnaire and multivariate logistic regression analysis was performed. During the previous year, 39.3% of ammunition workers experienced symptoms of work-related MSDs. Logistic regression analysis showed smoking (odds ratio [OR] = 1.372), chronic diseases (OR = 1.795), body mass index (BMI; overweight) (OR = 1.631), working year (OR = 1.509), cold temperature (OR = 1.838), and work load (OR = 2.210) were significant independent risk factors for the development of symptoms of MSDs. It was found that both work-related conditions and personal and environmental factors are important for the development of occupational MSDs.

Melanin and blood concentration in human skin studied by multiple regression analysis: experiments

NASA Astrophysics Data System (ADS)

Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

2001-09-01

Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.

Prediction of human core body temperature using non-invasive measurement methods.

PubMed

Niedermann, Reto; Wyss, Eva; Annaheim, Simon; Psikuta, Agnes; Davey, Sarah; Rossi, René Michel

2014-01-01

The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.

The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.

Partial Least Squares Regression Models for the Analysis of Kinase Signaling.

PubMed

Bourgeois, Danielle L; Kreeger, Pamela K

2017-01-01

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions.

PubMed

Ono, Daiki; Bamba, Takeshi; Oku, Yuichi; Yonetani, Tsutomu; Fukusaki, Eiichiro

2011-09-01

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture. Copyright © 2011 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

[Risk factors for patent ductus arteriosus in early preterm infants: a case-control study].

PubMed

Du, Jin-Feng; Liu, Tian-Tian; Wu, Hui

2016-01-01

To investigate the risk factors for the occurrence of patent ductus arteriosus (PDA) and to provide a clinical basis for reducing the occurrence of PDA in early preterm infants. A total of 136 early preterm infants (gestational age≤32 weeks) who were hospitalized between January 2013 and December 2014 and diagnosed with hemodynamicalhy significant PDA (hs-PDA) were enrolled as the case group. Based on the matched case-control principle, 136 early preterm infants without hs-PDA were selected among those who were hospitalized within the same period at a ratio of 1:1 and enrolled as the control group. The two groups were matched for sex and gestational age. The basic information of neonates and maternal conditions during the pregnancy and perinatal periods were collected. Logistic regression analysis was performed to identify the risk factors for the development of PDA. Univariate analysis showed that neonatal infectious diseases, neonatal respiratory distress syndrome, decreased platelet count within 24 hours after birth, and low birth weight were associated with the development of hs-PDA (P<0.05). Multivariate conditional logistic regression analysis revealed that neonatal infectious diseases (OR=2.368) and decreased platelet count within 24 hours after birth (OR=0.996) were independent risk factors for hs-PDA. Neonatal infectious diseases and decreased platelet count within 24 hours after birth increase the risk of hs-PDA in early preterm infants.

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 deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Dietary consumption patterns and laryngeal cancer risk.

PubMed

Vlastarakos, Petros V; Vassileiou, Andrianna; Delicha, Evie; Kikidis, Dimitrios; Protopapas, Dimosthenis; Nikolopoulos, Thomas P

2016-06-01

We conducted a case-control study to investigate the effect of diet on laryngeal carcinogenesis. Our study population was made up of 140 participants-70 patients with laryngeal cancer (LC) and 70 controls with a non-neoplastic condition that was unrelated to diet, smoking, or alcohol. A food-frequency questionnaire determined the mean consumption of 113 different items during the 3 years prior to symptom onset. Total energy intake and cooking mode were also noted. The relative risk, odds ratio (OR), and 95% confidence interval (CI) were estimated by multiple logistic regression analysis. We found that the total energy intake was significantly higher in the LC group (p < 0.001), and that the difference remained statistically significant after logistic regression analysis (p < 0.001; OR: 118.70). Notably, meat consumption was higher in the LC group (p < 0.001), and the difference remained significant after logistic regression analysis (p = 0.029; OR: 1.16). LC patients also consumed significantly more fried food (p = 0.036); this difference also remained significant in the logistic regression model (p = 0.026; OR: 5.45). The LC group also consumed significantly more seafood (p = 0.012); the difference persisted after logistic regression analysis (p = 0.009; OR: 2.48), with the consumption of shrimp proving detrimental (p = 0.049; OR: 2.18). Finally, the intake of zinc was significantly higher in the LC group before and after logistic regression analysis (p = 0.034 and p = 0.011; OR: 30.15, respectively). Cereal consumption (including pastas) was also higher among the LC patients (p = 0.043), with logistic regression analysis showing that their negative effect was possibly associated with the sauces and dressings that traditionally accompany pasta dishes (p = 0.006; OR: 4.78). Conversely, a higher consumption of dairy products was found in controls (p < 0.05); logistic regression analysis showed that calcium appeared to be protective at the micronutrient level (p < 0.001; OR: 0.27). We found no difference in the overall consumption of fruits and vegetables between the LC patients and controls; however, the LC patients did have a greater consumption of cooked tomatoes and cooked root vegetables (p = 0.039 for both), and the controls had more consumption of leeks (p = 0.042) and, among controls younger than 65 years, cooked beans (p = 0.037). Lemon (p = 0.037), squeezed fruit juice (p = 0.032), and watermelon (p = 0.018) were also more frequently consumed by the controls. Other differences at the micronutrient level included greater consumption by the LC patients of retinol (p = 0.044), polyunsaturated fats (p = 0.041), and linoleic acid (p = 0.008); LC patients younger than 65 years also had greater intake of riboflavin (p = 0.045). We conclude that the differences in dietary consumption patterns between LC patients and controls indicate a possible role for lifestyle modifications involving nutritional factors as a means of decreasing the risk of laryngeal cancer.

Building factorial regression models to explain and predict nitrate concentrations in groundwater under agricultural land

NASA Astrophysics Data System (ADS)

Stigter, T. Y.; Ribeiro, L.; Dill, A. M. M. Carvalho

2008-07-01

SummaryFactorial regression models, based on correspondence analysis, are built to explain the high nitrate concentrations in groundwater beneath an agricultural area in the south of Portugal, exceeding 300 mg/l, as a function of chemical variables, electrical conductivity (EC), land use and hydrogeological setting. Two important advantages of the proposed methodology are that qualitative parameters can be involved in the regression analysis and that multicollinearity is avoided. Regression is performed on eigenvectors extracted from the data similarity matrix, the first of which clearly reveals the impact of agricultural practices and hydrogeological setting on the groundwater chemistry of the study area. Significant correlation exists between response variable NO3- and explanatory variables Ca 2+, Cl -, SO42-, depth to water, aquifer media and land use. Substituting Cl - by the EC results in the most accurate regression model for nitrate, when disregarding the four largest outliers (model A). When built solely on land use and hydrogeological setting, the regression model (model B) is less accurate but more interesting from a practical viewpoint, as it is based on easily obtainable data and can be used to predict nitrate concentrations in groundwater in other areas with similar conditions. This is particularly useful for conservative contaminants, where risk and vulnerability assessment methods, based on assumed rather than established correlations, generally produce erroneous results. Another purpose of the models can be to predict the future evolution of nitrate concentrations under influence of changes in land use or fertilization practices, which occur in compliance with policies such as the Nitrates Directive. Model B predicts a 40% decrease in nitrate concentrations in groundwater of the study area, when horticulture is replaced by other land use with much lower fertilization and irrigation rates.

Risk Factors for Drug Abuse among Nepalese Samples Selected from a Town of Eastern Nepal

ERIC Educational Resources Information Center

Niraula, Surya Raj; Chhetry, Devendra Bahadur; Singh, Girish Kumar; Nagesh, S.; Shyangwa, Pramod Mohan

2009-01-01

The study focuses on the serious issue related to the adolescents' and adults' behavior and health. It aims to identify the risk factors for drug abuse from samples taken from a town of Eastern Nepal. This is a matched case-control study. The conditional logistic regression method was adopted for data analysis. The diagnosis cut off was determined…

High Frequency Data Acquisition System for Modelling the Impact of Visitors on the Thermo-Hygrometric Conditions of Archaeological Sites: A Casa di Diana (Ostia Antica, Italy) Case Study.

PubMed

Merello, Paloma; García-Diego, Fernando-Juan; Beltrán, Pedro; Scatigno, Claudia

2018-01-25

The characterization of the microclimatic conditions is fundamental for the preventive conservation of archaeological sites. In this context, the identification of the factors that influence the thermo-hygrometric equilibrium is key to determine the causes of cultural heritage deterioration. In this work, a characterization of the thermo-hygrometric conditions of Casa di Diana (Ostia Antica, Italy) is carried out analyzing the data of temperature and relative humidity recorded by a system of sensors with high monitoring frequency. Sensors are installed in parallel, calibrated and synchronized with a microcontroller. A data set of 793,620 data, arranged in a matrix with 66,135 rows and 12 columns, was used. Furthermore, the influence of human impact (visitors) is evaluated through a multiple linear regression model and a logistic regression model. The visitors do not affect the environmental humidity as it is very high and constant all the year. The results show a significant influence of the visitors in the upset of the thermal balance. When a tourist guide takes place, the probability that the hourly temperature variation reaches values higher than its monthly average is 10.64 times higher than it remains equal or less to its monthly average. The analysis of the regression residuals shows the influence of outdoor climatic variables in the thermal balance, such as solar radiation or ventilation.

High Frequency Data Acquisition System for Modelling the Impact of Visitors on the Thermo-Hygrometric Conditions of Archaeological Sites: A Casa di Diana (Ostia Antica, Italy) Case Study

PubMed Central

Merello, Paloma; García-Diego, Fernando-Juan; Beltrán, Pedro; Scatigno, Claudia

2018-01-01

The characterization of the microclimatic conditions is fundamental for the preventive conservation of archaeological sites. In this context, the identification of the factors that influence the thermo-hygrometric equilibrium is key to determine the causes of cultural heritage deterioration. In this work, a characterization of the thermo-hygrometric conditions of Casa di Diana (Ostia Antica, Italy) is carried out analyzing the data of temperature and relative humidity recorded by a system of sensors with high monitoring frequency. Sensors are installed in parallel, calibrated and synchronized with a microcontroller. A data set of 793,620 data, arranged in a matrix with 66,135 rows and 12 columns, was used. Furthermore, the influence of human impact (visitors) is evaluated through a multiple linear regression model and a logistic regression model. The visitors do not affect the environmental humidity as it is very high and constant all the year. The results show a significant influence of the visitors in the upset of the thermal balance. When a tourist guide takes place, the probability that the hourly temperature variation reaches values higher than its monthly average is 10.64 times higher than it remains equal or less to its monthly average. The analysis of the regression residuals shows the influence of outdoor climatic variables in the thermal balance, such as solar radiation or ventilation. PMID:29370142

Factors Affecting Regression-Discontinuity.

ERIC Educational Resources Information Center

Schumacker, Randall E.

The regression-discontinuity approach to evaluating educational programs is reviewed, and regression-discontinuity post-program mean differences under various conditions are discussed. The regression-discontinuity design is used to determine whether post-program differences exist between an experimental program and a control group. The difference…

Characterisation of volatile profile and sensory analysis of fresh-cut "Radicchio di Chioggia" stored in air or modified atmosphere.

PubMed

Cozzolino, Rosaria; Martignetti, Antonella; Pellicano, Mario Paolo; Stocchero, Matteo; Cefola, Maria; Pace, Bernardo; De Giulio, Beatrice

2016-02-01

The volatile profile of two hybrids of "Radicchio di Chioggia", Corelli and Botticelli, stored in air or passive modified atmosphere (MAP) during 12 days of cold storage, was monitored by solid phase micro-extraction (SPME) GC-MS. Botticelli samples were also subjected to sensory analysis. Totally, 61 volatile organic compounds (VOCs) were identified in the headspace of radicchio samples. Principal component analysis (PCA) showed that fresh product possessed a metabolic content similar to that of the MAP samples after 5 and 8 days of storage. Projection to latent structures by partial least squares (PLS) regression analysis showed the volatiles content of the samples varied depending only on the packaging conditions. Specifically, 12 metabolites describing the time evolution and explaining the effects of the different storage conditions were highlighted. Finally, a PCA analysis revealed that VOCs profile significantly correlated with sensory attributes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sensitivity Analysis of the Integrated Medical Model for ISS Programs

NASA Technical Reports Server (NTRS)

Goodenow, D. A.; Myers, J. G.; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Young, M.

2016-01-01

Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Standardized Rank Regression Coefficient (SRRC) are methods of conducting sensitivity analysis on nonlinear simulation models like the Integrated Medical Model (IMM). The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The partial part is so named because adjustments are made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. In SRRC, standardized regression-based coefficients measure the sensitivity of each input, adjusted for all the other inputs, on each output. Because the relative ranking of each of the inputs and outputs is used, as opposed to the values themselves, both methods accommodate the nonlinear relationship of the underlying model. As part of the IMM v4.0 validation study, simulations are available that predict 33 person-missions on ISS and 111 person-missions on STS. These simulated data predictions feed the sensitivity analysis procedures. The inputs to the sensitivity procedures include the number occurrences of each of the one hundred IMM medical conditions generated over the simulations and the associated IMM outputs: total quality time lost (QTL), number of evacuations (EVAC), and number of loss of crew lives (LOCL). The IMM team will report the results of using PRCC and SRRC on IMM v4.0 predictions of the ISS and STS missions created as part of the external validation study. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty could drive changes in overall model output uncertainty. These efforts are an integral part of the overall verification, validation, and credibility review of IMM v4.0.

Technical Assistance and Changes in Nutrition and Physical Activity Practices in the National Early Care and Education Learning Collaboratives Project, 2015-2016.

PubMed

Chiappone, Alethea; Smith, Teresa M; Estabrooks, Paul A; Rasmussen, Cristy Geno; Blaser, Casey; Yaroch, Amy L

2018-04-26

The National Early Care and Education Learning Collaboratives Project (ECELC) aims to improve best practices in early care and education (ECE) programs in topic areas of the Nutrition and Physical Activity Self-Assessment in Child Care (NAP SACC). Technical assistance is a component of the ECELC, yet its effect on outcomes is unclear. Beyond dose and duration of technical assistance, limited research exists on characteristics of technical assistance that contribute to outcomes. The objective of this study was to identify and describe technical assistance characteristics and explore associations with NAP SACC outcomes. We collected data from 10 collaboratives comprising 84 ECE programs in 2 states in 2015-2016. The objective of technical assistance was to support programs in improving best practices. Technical assistance was provided to programs via on-site, telephone, or email and was tailored to program needs. We used a mixed-methods design to examine associations between technical assistance and NAP SACC outcomes. We used multiple regression analysis to assess quantitative data and qualitative comparative analysis to determine necessary and sufficient technical assistance conditions supporting NAP SACC outcomes. We also conducted a document review to describe technical assistance that referred conditions identified by the qualitative comparative analysis. Regression analyses detected an inverse relationship between changes in NAP SACC scores and hours of technical assistance. No clear pattern emerged in the qualitative comparative analysis, leaving no necessary and sufficient conditions. However, the qualitative comparative analysis identified feedback as a potentially important component of technical assistance, whereas resource sharing and frequent email were characteristics that seemed to reduce the likelihood of improved outcomes. Email and resource sharing were considered primarily general information rather than tailored technical assistance. Technical assistance may be used in programs and made adaptable to program needs. The inclusion and evaluation of technical assistance, especially tailored approaches, is warranted for environmental interventions, including ECE settings.

Effectiveness of the New Hampshire stream-gaging network in providing regional streamflow information

USGS Publications Warehouse

Olson, Scott A.

2003-01-01

The stream-gaging network in New Hampshire was analyzed for its effectiveness in providing regional information on peak-flood flow, mean-flow, and low-flow frequency. The data available for analysis were from stream-gaging stations in New Hampshire and selected stations in adjacent States. The principles of generalized-least-squares regression analysis were applied to develop regional regression equations that relate streamflow-frequency characteristics to watershed characteristics. Regression equations were developed for (1) the instantaneous peak flow with a 100-year recurrence interval, (2) the mean-annual flow, and (3) the 7-day, 10-year low flow. Active and discontinued stream-gaging stations with 10 or more years of flow data were used to develop the regression equations. Each stream-gaging station in the network was evaluated and ranked on the basis of how much the data from that station contributed to the cost-weighted sampling-error component of the regression equation. The potential effect of data from proposed and new stream-gaging stations on the sampling error also was evaluated. The stream-gaging network was evaluated for conditions in water year 2000 and for estimated conditions under various network strategies if an additional 5 years and 20 years of streamflow data were collected. The effectiveness of the stream-gaging network in providing regional streamflow information could be improved for all three flow characteristics with the collection of additional flow data, both temporally and spatially. With additional years of data collection, the greatest reduction in the average sampling error of the regional regression equations was found for the peak- and low-flow characteristics. In general, additional data collection at stream-gaging stations with unregulated flow, relatively short-term record (less than 20 years), and drainage areas smaller than 45 square miles contributed the largest cost-weighted reduction to the average sampling error of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active stations, the reactivation of discontinued stations, or the activation of new stations to maximize the regional information content provided by the stream-gaging network. Final decisions regarding altering the New Hampshire stream-gaging network would require the consideration of the many uses of the streamflow data serving local, State, and Federal interests.

Performance characteristics of LOX-H2, tangential-entry, swirl-coaxial, rocket injectors

NASA Technical Reports Server (NTRS)

Howell, Doug; Petersen, Eric; Clark, Jim

1993-01-01

Development of a high performing swirl-coaxial injector requires an understanding of fundamental performance characteristics. This paper addresses the findings of studies on cold flow atomic characterizations which provided information on the influence of fluid properties and element operating conditions on the produced droplet sprays. These findings are applied to actual rocket conditions. The performance characteristics of swirl-coaxial injection elements under multi-element hot-fire conditions were obtained by analysis of combustion performance data from three separate test series. The injection elements are described and test results are analyzed using multi-variable linear regression. A direct comparison of test results indicated that reduced fuel injection velocity improved injection element performance through improved propellant mixing.

Including information about comorbidity in estimates of disease burden: Results from the WHO World Mental Health Surveys

PubMed Central

Alonso, Jordi; Vilagut, Gemma; Chatterji, Somnath; Heeringa, Steven; Schoenbaum, Michael; Üstün, T. Bedirhan; Rojas-Farreras, Sonia; Angermeyer, Matthias; Bromet, Evelyn; Bruffaerts, Ronny; de Girolamo, Giovanni; Gureje, Oye; Haro, Josep Maria; Karam, Aimee N.; Kovess, Viviane; Levinson, Daphna; Liu, Zhaorui; Mora, Maria Elena Medina; Ormel, J.; Posada-Villa, Jose; Uda, Hidenori; Kessler, Ronald C.

2010-01-01

Background The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring comorbidity. A methodology that addresses this problem is proposed and illustrated here with data from the WHO World Mental Health Surveys. Although the analysis is based on self-reports about one’s own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing comorbid condition profiles. Methods Face-to-face interviews in 13 countries (six developing, nine developed; n = 31,067; response rate = 69.6%) assessed 10 classes of chronic physical and 9 of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for comorbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects. Results The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia, and major depression were rated most severe. Adjustment for comorbidity reduced condition-specific estimates with substantial between-condition variation (.24–.70 ratios of condition-specific estimates with and without adjustment for comorbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity. Conclusions Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for comorbidity. These adjustments substantially influence condition-specific ratings. PMID:20553636

Comparison of dental implant stabilities by impact response and resonance frequencies using artificial bone.

PubMed

Kim, Dae-Seung; Lee, Woo-Jin; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Kim, Tae-Il; Yi, Won-Jin

2014-06-01

We compared implant stability as determined by the peak frequency from the impact response with the implant stability quotient (ISQ) by resonance frequency analysis (RFA) in various artificial bone conditions. The clinical bone conditions were simulated using an artificial bone material with different cortical thicknesses and trabecular densities. The artificial bone material was solid, rigid polyurethane. The polyurethane foam of 0.8g/cm(3) density was used for the cortical bone layer, and that of 0.08, 0.16, 0.24, 0.32, and 0.48g/cm(3) densities for the trabecular bone layer. The cortical bone material of 4 different thicknesses (1.4, 1.6, 1.8, and 2.0mm) was attached to the trabecular bone with varying density. Two types of dental implants (10 and 13mm lengths of 4.0mm diameter) were placed into the artificial bone blocks. An inductive sensor was used to measure the vibration caused by tapping the adapter-implant assembly. The peak frequency of the power spectrum of the impact response was used as the criterion for implant stability. The ISQ value was also measured for the same conditions. The stability, as measured by peak frequency (SPF) and ISQ value, increased as the trabecular density and the cortical density increased in linear regression analysis. The SPF and ISQ values were highly correlated with each other when the trabecular bone density and cortical bone thickness changed (Pearson correlation=0.90, p<0.01). The linear regression of the SPF with the cortical bone thickness showed higher goodness of fit (R(2) measure) than the ISQ value with the cortical bone thickness. The SPF could differentiate implantation conditions as many as the ISQ value when the trabecular bone density and the cortical density changed. However, the ISQ value was not consistent with the general stability tendency in some conditions. The SPF showed better consistency and differentiability with implant stability than the ISQ value by resonance frequency analysis in the various implantation conditions. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Influence of context in social participation of people with disabilities in Brazil.

PubMed

Silva, Fabiana C M; Sampaio, Rosana F; Ferreira, Fabiane R; Camargos, Vitor P; Neves, Jorge A

2013-10-01

To identify environmental and personal factors associated with social participation in adults with various diseases/health conditions residing in the urban areas of the Belo Horizonte Metropolitan Region, Minas Gerais, Brazil. Individual characteristics, social participation, and perception of environmental barriers of 226 patients treated at a public rehabilitation referral service were evaluated. Regression analyses with hierarchical entry of data were performed to verify the association of personal and environmental factors with social participation. More years of schooling, being engaged in the labor market, and consuming alcohol are conditions that increase the social participation of patients. Natural environment, transportation, access to health services, and social capital are perceived as the most important barriers to participation. Based on the linear regression analysis, the adjusted coefficient (R²(adj)) of the full model was 0.42 (P = 0.000). The results of this study may contribute to the planning and implementation of interventions and public policies at the individual and contextual level that are considered appropriate for reducing barriers and facilitate full participation.

Heart rate-left ventricular ejection time relations - Variations during postural change and cardiovascular challenges

NASA Technical Reports Server (NTRS)

Lance, V. Q.; Spodick, D. H.

1976-01-01

Experiments were conducted on healthy human subjects to determine HR-LVET (Heart Rate-Left Ventricular Ejection Time) regression relations in different postures, including tilt, and during isometric exercise. The subjects were tested in the resting state in supine and sitting positions, during isometric handgrip in supine and sitting positions and during 70 deg headup tilt. The recordings included a bipolar electrocardiogram and a right external carotid pulse curve. Comparison of the HR-LVET relation for the conditions under analysis revealed differences among the respective regression equations, which can be explained by the well-established differences in stroke volume and ejection rate among these states. These differences appear to account for the fact that under conditions in which stroke volume variations should be the major determinant, slopes will be similar but intercepts will vary. Since substantial differences among intercepts are observed, caution should be exercised whenever the intercept factor is used to predict LVET for HR.

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.

PubMed

Ferrari, Alberto

2017-01-01

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.

Assessing preferences of beach users for certain aspects of weather and ocean conditions: case studies from Australia

NASA Astrophysics Data System (ADS)

Zhang, Fan; Wang, Xiao Hua

2013-05-01

Three well-known Australian beaches, Surfers Paradise Beach (Gold Coast), Narrowneck Beach (Gold Coast) and Bondi Beach (Sydney), were selected for analysis of beach user preferences for certain weather and ocean conditions. Regression methods were used to determine how the numbers of visitors to these beaches are affected by these conditions. Actual visitor numbers were counted at three times during the day over several months at each beach with the aid of web cameras. The corresponding weather and ocean conditions were obtained from the Australian Bureau of Meteorology and local government agencies. Weekly and seasonal factors were also considered. The conditions preferred by beach users, as found in this study, are: no precipitation, higher temperatures, light-to-moderate wind speed (less than 30 km/h) and low wave height (up to 1.25 m). This study, the first to provide an analysis of beach user preferences for both weather and ocean conditions, shows that ocean conditions play a significant role in explaining the demand for beach recreation in Australia. It is therefore necessary for tourism management authorities or local governments to provide accurate and timely weather and ocean information to local, domestic and international beach users.

Assessing preferences of beach users for certain aspects of weather and ocean conditions: case studies from Australia.

PubMed

Zhang, Fan; Wang, Xiao Hua

2013-05-01

Three well-known Australian beaches, Surfers Paradise Beach (Gold Coast), Narrowneck Beach (Gold Coast) and Bondi Beach (Sydney), were selected for analysis of beach user preferences for certain weather and ocean conditions. Regression methods were used to determine how the numbers of visitors to these beaches are affected by these conditions. Actual visitor numbers were counted at three times during the day over several months at each beach with the aid of web cameras. The corresponding weather and ocean conditions were obtained from the Australian Bureau of Meteorology and local government agencies. Weekly and seasonal factors were also considered. The conditions preferred by beach users, as found in this study, are: no precipitation, higher temperatures, light-to-moderate wind speed (less than 30 km/h) and low wave height (up to 1.25 m). This study, the first to provide an analysis of beach user preferences for both weather and ocean conditions, shows that ocean conditions play a significant role in explaining the demand for beach recreation in Australia. It is therefore necessary for tourism management authorities or local governments to provide accurate and timely weather and ocean information to local, domestic and international beach users.

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

EPA Science Inventory

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...

A Semiparametric Change-Point Regression Model for Longitudinal Observations.

PubMed

Xing, Haipeng; Ying, Zhiliang

2012-12-01

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a semiparametric change-point regression model, in which the error process (stochastic component) is nonparametric and the baseline mean function (functional part) is completely unspecified, the observation times are allowed to be subject-specific, and the number, locations and magnitudes of change-points are unknown and need to be estimated. We further develop an estimation procedure which combines the recent advance in semiparametric analysis based on counting process argument and multiple change-points inference, and discuss its large sample properties, including consistency and asymptotic normality, under suitable regularity conditions. Simulation results show that the proposed methods work well under a variety of scenarios. An application to a real data set is also given.

Screening and clustering of sparse regressions with finite non-Gaussian mixtures.

PubMed

Zhang, Jian

2017-06-01

This article proposes a method to address the problem that can arise when covariates in a regression setting are not Gaussian, which may give rise to approximately mixture-distributed errors, or when a true mixture of regressions produced the data. The method begins with non-Gaussian mixture-based marginal variable screening, followed by fitting a full but relatively smaller mixture regression model to the selected data with help of a new penalization scheme. Under certain regularity conditions, the new screening procedure is shown to possess a sure screening property even when the population is heterogeneous. We further prove that there exists an elbow point in the associated scree plot which results in a consistent estimator of the set of active covariates in the model. By simulations, we demonstrate that the new procedure can substantially improve the performance of the existing procedures in the content of variable screening and data clustering. By applying the proposed procedure to motif data analysis in molecular biology, we demonstrate that the new method holds promise in practice. © 2016, The International Biometric Society.

Social motivation in Qatari schools and their relation to school achievement.

PubMed

Nasser, Ramzi

2014-10-01

This study assessed the relation between school-social motivation and student academic achievement. A factor analysis was performed on a set of school-social items selected a priori from three measures of school motivation: the Inventory of School Motivation, the General Achievement Goals Orientation Scale, and the Facilitating Conditions Scale. Three factors with fewer items represented Global Motivation, Peer Help, and Social Power. Hierarchical regression analysis showed social motivation measures were weak predictors of achievement scores in the various content areas. Findings are discussed in the context of Qatari education and culture.

Box-Behnken design for investigation of microwave-assisted extraction of patchouli oil

NASA Astrophysics Data System (ADS)

Kusuma, Heri Septya; Mahfud, Mahfud

2015-12-01

Microwave-assisted extraction (MAE) technique was employed to extract the essential oil from patchouli (Pogostemon cablin). The optimal conditions for microwave-assisted extraction of patchouli oil were determined by response surface methodology. A Box-Behnken design (BBD) was applied to evaluate the effects of three independent variables (microwave power (A: 400-800 W), plant material to solvent ratio (B: 0.10-0.20 g mL-1) and extraction time (C: 20-60 min)) on the extraction yield of patchouli oil. The correlation analysis of the mathematical-regression model indicated that quadratic polynomial model could be employed to optimize the microwave extraction of patchouli oil. The optimal extraction conditions of patchouli oil was microwave power 634.024 W, plant material to solvent ratio 0.147648 g ml-1 and extraction time 51.6174 min. The maximum patchouli oil yield was 2.80516% under these optimal conditions. Under the extraction condition, the experimental values agreed with the predicted results by analysis of variance. It indicated high fitness of the model used and the success of response surface methodology for optimizing and reflect the expected extraction condition.

The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care.

PubMed

Taljaard, Monica; McKenzie, Joanne E; Ramsay, Craig R; Grimshaw, Jeremy M

2014-06-19

An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. In segmented regression analysis, the change in intercept and/or slope from pre- to post-intervention is estimated and used to test causal hypotheses about the intervention. We illustrate segmented regression using data from a previously published study that evaluated the effectiveness of a collaborative intervention to improve quality in pre-hospital ambulance care for acute myocardial infarction (AMI) and stroke. In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression, we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out. Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.

Regression tree modeling of forest NPP using site conditions and climate variables across eastern USA

NASA Astrophysics Data System (ADS)

Kwon, Y.

2013-12-01

As evidence of global warming continue to increase, being able to predict forest response to climate changes, such as expected rise of temperature and precipitation, will be vital for maintaining the sustainability and productivity of forests. To map forest species redistribution by climate change scenario has been successful, however, most species redistribution maps lack mechanistic understanding to explain why trees grow under the novel conditions of chaining climate. Distributional map is only capable of predicting under the equilibrium assumption that the communities would exist following a prolonged period under the new climate. In this context, forest NPP as a surrogate for growth rate, the most important facet that determines stand dynamics, can lead to valid prediction on the transition stage to new vegetation-climate equilibrium as it represents changes in structure of forest reflecting site conditions and climate factors. The objective of this study is to develop forest growth map using regression tree analysis by extracting large-scale non-linear structures from both field-based FIA and remotely sensed MODIS data set. The major issue addressed in this approach is non-linear spatial patterns of forest attributes. Forest inventory data showed complex spatial patterns that reflect environmental states and processes that originate at different spatial scales. At broad scales, non-linear spatial trends in forest attributes and mixture of continuous and discrete types of environmental variables make traditional statistical (multivariate regression) and geostatistical (kriging) models inefficient. It calls into question some traditional underlying assumptions of spatial trends that uncritically accepted in forest data. To solve the controversy surrounding the suitability of forest data, regression tree analysis are performed using Software See5 and Cubist. Four publicly available data sets were obtained: First, field-based Forest Inventory and Analysis (USDA, Forest Service) data set for the 31 eastern most United States. Second, 8-day composite of MODIS Land Cover, FPAR, LAI and GPP/NPP data were obtained from Jan 2001 to Dec 2004 (total 182 composite) and each product were filtered by pixel-level quality assurance data to select best quality pixels. Third, 30-year averaged climate data were collected from National Oceanic and Atmospheric Administration (NOAA) and five climatic variables were obtained: Monthly temperature, precipitation, annual heating and cooling days, and annual frost-free days. Forth, topographic data were obtained from digital elevation model (1km by 1km). This research will provide a better understanding of large-scale forest responses to environmental factors that will be beneficial for the development of important forest management applications.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Toward the International Classification of Functioning, Disability and Health (ICF) Rehabilitation Set: A Minimal Generic Set of Domains for Rehabilitation as a Health Strategy.

PubMed

Prodinger, Birgit; Cieza, Alarcos; Oberhauser, Cornelia; Bickenbach, Jerome; Üstün, Tevfik Bedirhan; Chatterji, Somnath; Stucki, Gerold

2016-06-01

To develop a comprehensive set of the International Classification of Functioning, Disability and Health (ICF) categories as a minimal standard for reporting and assessing functioning and disability in clinical populations along the continuum of care. The specific aims were to specify the domains of functioning recommended for an ICF Rehabilitation Set and to identify a minimal set of environmental factors (EFs) to be used alongside the ICF Rehabilitation Set when describing disability across individuals and populations with various health conditions. Secondary analysis of existing data sets using regression methods (Random Forests and Group Lasso regression) and expert consultations. Along the continuum of care, including acute, early postacute, and long-term and community rehabilitation settings. Persons (N=9863) with various health conditions participated in primary studies. The number of respondents for whom the dependent variable data were available and used in this analysis was 9264. Not applicable. For regression analyses, self-reported general health was used as a dependent variable. The ICF categories from the functioning component and the EF component were used as independent variables for the development of the ICF Rehabilitation Set and the minimal set of EFs, respectively. Thirty ICF categories to be complemented with 12 EFs were identified as relevant to the identified ICF sets. The ICF Rehabilitation Set constitutes of 9 ICF categories from the component body functions and 21 from the component activities and participation. The minimal set of EFs contains 12 categories spanning all chapters of the EF component of the ICF. The identified sets proposed serve as minimal generic sets of aspects of functioning in clinical populations for reporting data within and across heath conditions, time, clinical settings including rehabilitation, and countries. These sets present a reference framework for harmonizing existing information on disability across general and clinical populations. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Healthcare Expenditures Associated with Depression Among Individuals with Osteoarthritis: Post-Regression Linear Decomposition Approach.

PubMed

Agarwal, Parul; Sambamoorthi, Usha

2015-12-01

Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p < 0.001) compared to those without depression. Post-regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.

Exposure to the dental environment and prevalence of respiratory illness in dental student populations.

PubMed

Scannapieco, Frank A; Ho, Alex W; DiTolla, Maris; Chen, Casey; Dentino, Andrew R

2004-03-01

To determine if the prevalence of respiratory disease among dental students and dental residents varies with their exposure to the clinical dental environment. A detailed questionnaire was administered to 817 students at 3 dental schools. The questionnaire sought information concerning demographic characteristics, school year, exposure to the dental environment and dental procedures, and history of respiratory disease. The data obtained were subjected to bivariate and multiple logistic regression analysis. Respondents reported experiencing the following respiratory conditions during the previous year: asthma (26 cases), bronchitis (11 cases), chronic lung disease (6 cases), pneumonia (5 cases) and streptococcal pharyngitis (50 cases). Bivariate statistical analyses indicated no significant associations between the prevalence of any of the respiratory conditions and year in dental school, except for asthma, for which there was a significantly higher prevalence at 1 school compared to the other 2 schools. When all cases of respiratory disease were combined as a composite variable and subjected to multivariate logistic regression analysis controlling for age, sex, race, dental school, smoking history and alcohol consumption, no statistically significant association was observed between respiratory condition and year in dental school or exposure to the dental environment as a dental patient. No association was found between the prevalence of respiratory disease and a student's year in dental school or previous exposure to the dental environment as a patient. These results suggest that exposure to the dental environment does not increase the risk for respiratory infection in healthy dental health care workers.

Relationship between accumulated heat stress during the dry period, body condition score, and reproduction parameters of Holstein cows in tropical conditions.

PubMed

Avendaño-Reyes, Leonel; Fuquay, John W; Moore, Reuben B; Liu, Zhanglin; Clark, Bruce L; Vierhout, C

2010-02-01

To estimate the relationship between heat stress during the last 60 days prepartum, body condition score and certain reproductive traits in the subsequent lactation of Holstein cows, 564 multiparous cows and 290 primiparous cows from four dairy herds were used in a hot, humid region. Maximum prepartum degree days were estimated to quantify the degree of heat stress. Multiple regressions analyses and logistic regression analysis were performed to determine the effect of prepartum heat stress and body condition change on reproductive parameters, which were obtained from DHIA forms at the end of the lactation. Multiparous and primiparous cows which gained body condition score from calving to 60 d postpartum exhibited 28 and 27 fewer days open (P < 0.05), respectively, than cows not gaining. There was no effect (P > 0.05) of heat stress measurement on days open or services per conception in either multiparous or primiparous cows. During hotter months of calving, multiparous cows showed higher services per conception and primiparous cows showed higher days open and services per conception (P < 0.05). Maximum prepartum degree-days were positively associated (P < 0.05) with calving difficulty score. Multiparous cows with high body condition score at calving were 1.47 times more likely to present a very difficult calving than cows that calved in October (P < 0.05). Collectively, these results suggest that reproductive performance was not affected by cumulative prepartum heat stress although it was associated with very difficult calving score.

Caudal Regression Syndrome with Partial Agenesis of the Corpus callosum and Partial Lobar Holoprosencephaly

PubMed Central

Hashami, Hilal Al; Bataclan, Maria F; Mathew, Mariam; Krishnan, Lalitha

2010-01-01

Caudal regression syndrome is a rare fetal condition of diabetic pregnancy. Although the exact mechanism is not known, hyperglycaemia during embryogenesis seems to act as a teratogen. Independently, caudal regression syndrome (CRS), agenesis of the corpus callosum (ACC) and partial lobar holoprosencephaly (HPE) have been reported in infants of diabetic mothers. To our knowledge, a combination of all these three conditions has not been reported so far. PMID:21509087

Caudal Regression Syndrome with Partial Agenesis of the Corpus callosum and Partial Lobar Holoprosencephaly: Case report.

PubMed

Hashami, Hilal Al; Bataclan, Maria F; Mathew, Mariam; Krishnan, Lalitha

2010-04-01

Caudal regression syndrome is a rare fetal condition of diabetic pregnancy. Although the exact mechanism is not known, hyperglycaemia during embryogenesis seems to act as a teratogen. Independently, caudal regression syndrome (CRS), agenesis of the corpus callosum (ACC) and partial lobar holoprosencephaly (HPE) have been reported in infants of diabetic mothers. To our knowledge, a combination of all these three conditions has not been reported so far.

Environmental factors and flow paths related to Escherichia coli concentrations at two beaches on Lake St. Clair, Michigan, 2002–2005

USGS Publications Warehouse

Holtschlag, David J.; Shively, Dawn; Whitman, Richard L.; Haack, Sheridan K.; Fogarty, Lisa R.

2008-01-01

Regression analyses and hydrodynamic modeling were used to identify environmental factors and flow paths associated with Escherichia coli (E. coli) concentrations at Memorial and Metropolitan Beaches on Lake St. Clair in Macomb County, Mich. Lake St. Clair is part of the binational waterway between the United States and Canada that connects Lake Huron with Lake Erie in the Great Lakes Basin. Linear regression, regression-tree, and logistic regression models were developed from E. coli concentration and ancillary environmental data. Linear regression models on log10 E. coli concentrations indicated that rainfall prior to sampling, water temperature, and turbidity were positively associated with bacteria concentrations at both beaches. Flow from Clinton River, changes in water levels, wind conditions, and log10 E. coli concentrations 2 days before or after the target bacteria concentrations were statistically significant at one or both beaches. In addition, various interaction terms were significant at Memorial Beach. Linear regression models for both beaches explained only about 30 percent of the variability in log10 E. coli concentrations. Regression-tree models were developed from data from both Memorial and Metropolitan Beaches but were found to have limited predictive capability in this study. The results indicate that too few observations were available to develop reliable regression-tree models. Linear logistic models were developed to estimate the probability of E. coli concentrations exceeding 300 most probable number (MPN) per 100 milliliters (mL). Rainfall amounts before bacteria sampling were positively associated with exceedance probabilities at both beaches. Flow of Clinton River, turbidity, and log10 E. coli concentrations measured before or after the target E. coli measurements were related to exceedances at one or both beaches. The linear logistic models were effective in estimating bacteria exceedances at both beaches. A receiver operating characteristic (ROC) analysis was used to determine cut points for maximizing the true positive rate prediction while minimizing the false positive rate. A two-dimensional hydrodynamic model was developed to simulate horizontal current patterns on Lake St. Clair in response to wind, flow, and water-level conditions at model boundaries. Simulated velocity fields were used to track hypothetical massless particles backward in time from the beaches along flow paths toward source areas. Reverse particle tracking for idealized steady-state conditions shows changes in expected flow paths and traveltimes with wind speeds and directions from 24 sectors. The results indicate that three to four sets of contiguous wind sectors have similar effects on flow paths in the vicinity of the beaches. In addition, reverse particle tracking was used for transient conditions to identify expected flow paths for 10 E. coli sampling events in 2004. These results demonstrate the ability to track hypothetical particles from the beaches, backward in time, to likely source areas. This ability, coupled with a greater frequency of bacteria sampling, may provide insight into changes in bacteria concentrations between source and sink areas.

Matched samples logistic regression in case-control studies with missing values: when to break the matches.

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

Simulated data sets are used to evaluate conditional and unconditional maximum likelihood estimation in an individual case-control design with continuous covariates when there are different rates of excluded cases and different levels of other design parameters. The effectiveness of the estimation procedures is measured by method bias, variance of the estimators, root mean square error (RMSE) for logistic regression and the percentage of explained variation. Conditional estimation leads to higher RMSE than unconditional estimation in the presence of missing observations, especially for 1:1 matching. The RMSE is higher for the smaller stratum size, especially for the 1:1 matching. The percentage of explained variation appears to be insensitive to missing data, but is generally higher for the conditional estimation than for the unconditional estimation. It is particularly good for the 1:2 matching design. For minimizing RMSE, a high matching ratio is recommended; in this case, conditional and unconditional logistic regression models yield comparable levels of effectiveness. For maximizing the percentage of explained variation, the 1:2 matching design with the conditional logistic regression model is recommended.

Job satisfaction among recent graduates of schools of nursing.

PubMed

Munro, B H

1983-01-01

Using a randomly selected national sample and multiple regression analysis, the correlates of job satisfaction among recent graduates of nursing programs were investigated. Factor analysis was used to test the validity of Herzberg's theory of job satisfaction/dissatisfaction. For these 329 employed RNs, responsibility (the importance and challenge of the work) was the most important determinant of job satisfaction, and working conditions was the second strongest predictor. Graduates from diploma, associate degree, and baccalaureate programs did not differ in terms of job satisfaction. Support was given for the validity of Herzberg's dual-factor theory in relation to all five motivators included in the analysis (achievement, work itself, responsibility, advancement, and growth) and for the hygiene salary. The validity of four other hygienes (supervision, working conditions, status, and security) was not established. The results imply that administrators need to appeal to nurses' needs for important challenging jobs and opportunities to grow and develop professionally.

Relationship between self-esteem and living conditions among stroke survivors at home.

PubMed

Shida, Junko; Sugawara, Kyoko; Goto, Junko; Sekito, Yoshiko

2014-10-01

To clarify the relationship between self-esteem of stroke survivors at home and their living conditions. Study participants were stroke survivors who lived at home and commuted to one of two medical facilities in the Tohoku region of Japan. Stroke survivors were recruited for the present study when they came to the hospital for a routine visit. The researcher or research assistant explained the study objective and methods to the stroke survivor, and the questionnaire survey was conducted. Survey contents included the Japanese version of the Rosenberg Self-Esteem Scale (RSE) and questions designed to assess living conditions. A total of 65 participants with complete RSE data were included in the analysis. The mean (standard deviation) age of participants was 70.9 years (± 11.1), with a mean RSE score of 32.12 (± 8.32). Only a minor decrease in participant self-esteem was observed, even after having experienced a stroke. Factors associated with self-esteem, including "independent bathing" (standardized partial regression coefficient, β = 0.405, P < 0.001), "being needed by family members" (β = 0.389, P < 0.001), "independent grooming" (β = 0.292, P = 0.009), and "sleep satisfaction" (β = 0.237, P = 0.017), were analyzed by stepwise multiple regression analysis. The multiple correlation coefficient adjusted for the degrees of freedom was 0.738 (P < 0.001). Our analysis revealed that the maintenance of activities of daily living, and the presence of a suitable environment that enhances physical function recovery and promotes activity and participation, are necessary to improve self-esteem in stroke survivors living at home. © 2013 The Authors. Japan Journal of Nursing Science © 2013 Japan Academy of Nursing Science.

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

PubMed

Sun, Hokeun; Wang, Shuang

2013-05-30

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.

Comparing the Relationship Between Age and Length of Disability Across Common Chronic Conditions

PubMed Central

Jetha, Arif; Besen, Elyssa; Smith, Peter M.

2016-01-01

Objective: The aim of this study was to compare the association between age and disability length across common chronic conditions. Methods: Analysis of 39,915 nonwork-related disability claims with a diagnosis of arthritis, diabetes, hypertension, coronary artery disease, depression, low back pain, chronic pulmonary disease, or cancer. Ordinary least squares regression models examined age-length of disability association across chronic conditions. Results: Arthritis (76.6 days), depression (63.2 days), and cancer (64.9 days) were associated with longest mean disability lengths; hypertension was related to shortest disability lengths (41.5 days). Across chronic conditions, older age was significantly associated with longer work disability. The age–length of disability association was most significant for chronic pulmonary disease and cancer. The relationship between age and length of work disability was linear among most chronic conditions. Conclusions: Work disability prevention strategies should consider both employee age and chronic condition diagnosis. PMID:27164446

Use of multiple regression models in the study of sandhopper orientation under natural conditions

NASA Astrophysics Data System (ADS)

Marchetti, Giovanni M.; Scapini, Felicita

2003-10-01

In sandhoppers (Amphipoda; Talitridae), typical dwellers of the supralittoral zone of sandy beaches, orientation with respect to the sun and landscape vision is adapted to the local direction of the shoreline. Variation of this behavioural adaptation can be related to the characteristics of the beach. Measures of orientation with respect to the shoreline direction can thus be made as a tool to assess beach stability versus changeability, once the sources of variation are correctly interpreted. Orientation of animals can be studied by statistical analysis of directions taken after release in nature. In this paper some new tools for exploring directional data are reviewed, with special emphasis on non-parametric smoothers and regression models. Results from a large study concerning one species of sandhoppers, Talitrus saltator (Montagu), from an exposed sandy beach in northeastern Tunisia are presented. Seasonal differences in orientation behaviour were shown with a higher scatter in autumn with respect to spring. The higher scatter shown in autumn depended both on intrinsic (sex) and external (climatic conditions and landscape visibility) factors and was related to the tendency of this species to migrate towards the dune anticipating winter conditions.

Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-β-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis.

PubMed

Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O

2017-03-05

The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (K b ), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated K b and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10 -7 M for anthracene and 3.48×10 -8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized light. Published by Elsevier B.V.

[Optimization of ultrasonic-assisted extraction of total flavonoids from leaves of the Artocarpus heterophyllus by response surface methodology].

PubMed

Wang, Hong-wu; Liu, Yan-qing; Wang, Yuan-hong

2011-07-01

To investigate the ultrasonic-assisted extract on of total flavonoids from leaves of the Artocarpus heterophyllus. Investigated the effects of ethanol concentration, extraction time, and liquid-solid ratio on flavonoids yield. A 17-run response surface design involving three factors at three levels was generated by the Design-Expert software and experimental data obtained were subjected to quadratic regression analysis to create a mathematical model describing flavonoids extraction. The optimum ultrasonic assisted extraction conditions were: ethanol volume fraction 69.4% and liquid-solid ratio of 22.6:1 for 32 min. Under these optimized conditions, the yield of flavonoids was 7.55 mg/g. The Box-Behnken design and response surface analysis can well optimize the ultrasonic-assisted extraction of total flavonoids from Artocarpus heterophyllus.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Predictors of Age of Diagnosis for Children with Autism Spectrum Disorder: The Role of a Consistent Source of Medical Care, Race, and Condition Severity

ERIC Educational Resources Information Center

Emerson, Natacha D.; Morrell, Holly E. R.; Neece, Cameron

2016-01-01

Having a consistent source of medical care may facilitate diagnosis of autism spectrum disorders (ASD). This study examined predictors of age of ASD diagnosis using data from the 2011-2012 National Survey of Children's Health. Using multiple linear regression analysis, age of diagnosis was predicted by race, ASD severity, having a consistent…

Tidal Influence on Water Quality of Kapuas Kecil River Downstream

NASA Astrophysics Data System (ADS)

Purnaini, Rizki; Sudarmadji; Purwono, Suryo

2018-02-01

The Kapuas Kecil River is strongly influenced by tidal, in the dry season the intrusion of surface water is often a problem for the WTP because it causes the change of raw water quality to be processed. The purpose of this study was to examine the effect of sea tides on water quality of the Kapuas Kecil River. The study was conducted in Kapuas River downstream along ± 30 km from the upper boundary to the estuary. Water sampling is carried out during the dry and rainy season, when the tidal conditions at 7 (seven) locations of the monitoring station. Descriptive analysis methods and regression-correlation statistics are used to determine the effect of tides on water quality in Kapuas River downstream. In general, the water quality of the Kapuas Kecil River has exceeded the criteria of first class water quality, ie water that can be used for drinking water. The status of water quality of the Kapuas Kecil River based on the pollution index calculation shows the condition of the river is "mild to medium pollutants". The result of multiple linear regression analysis got the value of coefficient of determination (adjusted R square) = 0,760, which in whole show that independent variable (tidal and distance) influence to dependent variable (value of TDS) equal to 76%.

[Prevalence and risk factors of postpartum depression in Tianhe District of Guangzhou].

PubMed

Deng, Aiwen; Jiang, Tingting; Luo, Yingping; Xiong, Ribo

2014-01-01

To investigate the prevalence and risk factors of postpartum depression (PPD) in Tianhe district of Guangzhou. A total of 1428 postpartum women in 3 hospitals in Tianhe District of Guangzhou were screened with Edinburg Postnatal Depression Scale (EPDS), Social Support Rating Scale (SSRS) and a self-designed questionnaire of PPD-related factors during the period from May to September, 2013. The prevalence of PPD was 20.03% in these women. Unconditional logistic regression analysis showed a significant correlation of PPD with education, delivery mode, only daughter, relationship between mother-in-law and daughter-in-law, newborn gender satisfaction and housing condition (P<0.05). Multivariate logistic regression analysis identified education, delivery mode, only daughter, relationship between mother-in-law and daughter-in-law, and newborn gender satisfaction as the risk factors for PPD, and housing condition was negatively correlated with the incidence of PPD with an OR value of 0.900. Compared with healthy postpartum women, the patients with PPD exhibited significantly reduced total score of social support rating scale, score of objective support, score of subjective support, and social utilization degree. The prevalence of PPD is high in Tianhe District of Guangzhou, and health education and psychosocial intervention should be offered to prevent PPD.

Determination of Uncertainties for the New SSME Model

NASA Technical Reports Server (NTRS)

Coleman, Hugh W.; Hawk, Clark W.

1996-01-01

This report discusses the uncertainty analysis performed in support of a new test analysis and performance prediction model for the Space Shuttle Main Engine. The new model utilizes uncertainty estimates for experimental data and for the analytical model to obtain the most plausible operating condition for the engine system. This report discusses the development of the data sets and uncertainty estimates to be used in the development of the new model. It also presents the application of uncertainty analysis to analytical models and the uncertainty analysis for the conservation of mass and energy balance relations is presented. A new methodology for the assessment of the uncertainty associated with linear regressions is presented.

Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

NASA Astrophysics Data System (ADS)

Mirbaha, Babak; Saffarzadeh, Mahmoud; AmirHossein Beheshty, Seyed; Aniran, MirMoosa; Yazdani, Mirbahador; Shirini, Bahram

2017-10-01

Analysis of vehicle speed with different weather condition and traffic characteristics is very effective in traffic planning. Since the weather condition and traffic characteristics vary every day, the prediction of average speed can be useful in traffic management plans. In this study, traffic and weather data for a two-lane highway located in Northwest of Iran were selected for analysis. After merging traffic and weather data, the linear regression model was calibrated for speed prediction using STATA12.1 Statistical and Data Analysis software. Variables like vehicle flow, percentage of heavy vehicles, vehicle flow in opposing lane, percentage of heavy vehicles in opposing lane, rainfall (mm), snowfall and maximum daily wind speed more than 13m/s were found to be significant variables in the model. Results showed that variables of vehicle flow and heavy vehicle percent acquired the positive coefficient that shows, by increasing these variables the average vehicle speed in every weather condition will also increase. Vehicle flow in opposing lane, percentage of heavy vehicle in opposing lane, rainfall amount (mm), snowfall and maximum daily wind speed more than 13m/s acquired the negative coefficient that shows by increasing these variables, the average vehicle speed will decrease.

Wetland environmental conditions associated with the risk of avian cholera outbreaks and the abundance of Pasteurella multocida

USGS Publications Warehouse

Blanchong, Julie A.; Samuel, Michael D.; Goldberg, Diana R.; Shadduck, Daniel J.; Creekmore, L.H.

2006-01-01

Avian cholera is a significant infectious disease affecting waterfowl across North America and occurs worldwide among various avian species. Despite the importance of this disease, little is known about the factors that cause avian cholera outbreaks and what management strategies might be used to reduce disease mortality. Previous studies indicated that wetland water conditions may affect survival and transmission of Pasteurella multocida, the agent that causes avian cholera. These studies hypothesized that water conditions affect the likelihood that avian cholera outbreaks will occur in specific wetlands. To test these predictions, we collected data from avian cholera outbreak and non-outbreak (control) wetlands throughout North America (wintera??spring 1995a??1996 to 1998a??1999) to evaluate whether water conditions were associated with outbreaks. Conditional logistic regression analysis on paired outbreak and non-outbreak wetlands indicated no significant association between water conditions and the risk of avian cholera outbreaks. For wetlands where avian cholera outbreaks occurred, linear regression showed that increased eutrophic nutrient concentrations (Potassium [K], nitrate [NO3], phosphorus [P], and phosphate [PO3]) were positively related to the abundance of P. multocida recovered from water and sediment samples. Wetland protein concentration and an El Ni??o event were also associated with P. multocida abundance. Our results indicate that wetland water conditions are not strongly associated with the risk of avian cholera outbreaks; however, some variables may play a role in the abundance of P. multocida bacteria and might be important in reducing the severity of avian cholera outbreaks.

Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

PubMed Central

Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.

2008-01-01

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742

Value of Information Analysis for Time-lapse Seismic Data by Simulation-Regression

NASA Astrophysics Data System (ADS)

Dutta, G.; Mukerji, T.; Eidsvik, J.

2016-12-01

A novel method to estimate the Value of Information (VOI) of time-lapse seismic data in the context of reservoir development is proposed. VOI is a decision analytic metric quantifying the incremental value that would be created by collecting information prior to making a decision under uncertainty. The VOI has to be computed before collecting the information and can be used to justify its collection. Previous work on estimating the VOI of geophysical data has involved explicit approximation of the posterior distribution of reservoir properties given the data and then evaluating the prospect values for that posterior distribution of reservoir properties. Here, we propose to directly estimate the prospect values given the data by building a statistical relationship between them using regression. Various regression techniques such as Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and k-Nearest Neighbors (k-NN) are used to estimate the VOI, and the results compared. For a univariate Gaussian case, the VOI obtained from simulation-regression has been shown to be close to the analytical solution. Estimating VOI by simulation-regression is much less computationally expensive since the posterior distribution of reservoir properties given each possible dataset need not be modeled and the prospect values need not be evaluated for each such posterior distribution of reservoir properties. This method is flexible, since it does not require rigid model specification of posterior but rather fits conditional expectations non-parametrically from samples of values and data.

What is new about covered interest parity condition in the European Union? Evidence from fractal cross-correlation regressions

NASA Astrophysics Data System (ADS)

Ferreira, Paulo; Kristoufek, Ladislav

2017-11-01

We analyse the covered interest parity (CIP) using two novel regression frameworks based on cross-correlation analysis (detrended cross-correlation analysis and detrending moving-average cross-correlation analysis), which allow for studying the relationships at different scales and work well under non-stationarity and heavy tails. CIP is a measure of capital mobility commonly used to analyse financial integration, which remains an interesting feature of study in the context of the European Union. The importance of this features is related to the fact that the adoption of a common currency is associated with some benefits for countries, but also involves some risks such as the loss of economic instruments to face possible asymmetric shocks. While studying the Eurozone members could explain some problems in the common currency, studying the non-Euro countries is important to analyse if they are fit to take the possible benefits. Our results point to the CIP verification mainly in the Central European countries while in the remaining countries, the verification of the parity is only residual.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

NASA Astrophysics Data System (ADS)

Braga, Jez Willian Batista; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Rufini, Iolanda Aparecida; Santos, Dário, Jr.; Krug, Francisco José

2010-01-01

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.

Assessment of relevant factors and relationships concerning human dermal exposure to pesticides in greenhouse applications.

PubMed

Martínez Vidal, Jose L; Egea González, Francisco J; Garrido Frenich, Antonia; Martínez Galera, María; Aguilera, Pedro A; López Carrique, Enrique

2002-08-01

Principal component analysis (PCA) was applied to the gas chromatographic data obtained from 23 different greenhouse trials. This was used to establish which factors, including application technique (very small, small, medium and large drop-size), crop characteristics (short/tall, thin/dense) and pattern application of the operator (walking towards or away from the treated area) are relevant to the dermal exposure levels of greenhouse applicators. The results showed that the highest exposure by pesticides during field applications in greenhouses, in the climatic conditions and in the crop conditions typical of a southern European country, occurs on the lower legs and front thighs of the applicators. Similar results were obtained by hierarchical cluster analysis (HCA). Drop-size seems to be very important in determining total exposure, while height and density of crops have little influence on total exposure under the conditions of the present study. No pesticide type is a major factor in total exposure. The application of multiple regression analysis (MRA) allowed assessment of the relationships between the pesticide exposure of the less affected parts of the body with the most affected parts.

Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

PubMed Central

Cui, Hengjian; Li, Runze

2014-01-01

This work is concerned with marginal sure independence feature screening for ultra-high dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use conditional distribution function to construct a new index for feature screening. In this paper, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of O(nκ) with some κ ≥ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life data sets. PMID:26392643

A meta-analysis of sex differences in human brain structure☆

PubMed Central

Ruigrok, Amber N.V.; Salimi-Khorshidi, Gholamreza; Lai, Meng-Chuan; Baron-Cohen, Simon; Lombardo, Michael V.; Tait, Roger J.; Suckling, John

2014-01-01

The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18–59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions. PMID:24374381

Dynamic elementary mode modelling of non-steady state flux data.

PubMed

Folch-Fortuny, Abel; Teusink, Bas; Hoefsloot, Huub C J; Smilde, Age K; Ferrer, Alberto

2018-06-18

A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

Orthodontic bracket bonding without previous adhesive priming: A meta-regression analysis.

PubMed

Altmann, Aline Segatto Pires; Degrazia, Felipe Weidenbach; Celeste, Roger Keller; Leitune, Vicente Castelo Branco; Samuel, Susana Maria Werner; Collares, Fabrício Mezzomo

2016-05-01

To determine the consensus among studies that adhesive resin application improves the bond strength of orthodontic brackets and the association of methodological variables on the influence of bond strength outcome. In vitro studies were selected to answer whether adhesive resin application increases the immediate shear bond strength of metal orthodontic brackets bonded with a photo-cured orthodontic adhesive. Studies included were those comparing a group having adhesive resin to a group without adhesive resin with the primary outcome measurement shear bond strength in MPa. A systematic electronic search was performed in PubMed and Scopus databases. Nine studies were included in the analysis. Based on the pooled data and due to a high heterogeneity among studies (I(2)  =  93.3), a meta-regression analysis was conducted. The analysis demonstrated that five experimental conditions explained 86.1% of heterogeneity and four of them had significantly affected in vitro shear bond testing. The shear bond strength of metal brackets was not significantly affected when bonded with adhesive resin, when compared to those without adhesive resin. The adhesive resin application can be set aside during metal bracket bonding to enamel regardless of the type of orthodontic adhesive used.

Demographic and psychosocial risk factors for preterm delivery in an active duty pregnant population.

PubMed

Evans, M A; Rosen, L N

2000-01-01

The effects of work climate, pregnancy transitions stress, maternal medical conditions, health risk behaviors, psychological health, and demographic characteristics were examined among 269 pregnant military women. The study found that single and separated/divorced military women were at greater risk for preterm delivery than married women. Unmarried participants were more likely to belong to ethnic minorities, were lower ranking, less educated, and reported a greater number of medical conditions than married participants. Psychosocial variables distinguished the three marital status groups--married, single, and separated/divorced--but none of these variables was related to preterm delivery. In a logistic regression analysis, marital status was a more significant predictor of preterm delivery than were medical conditions.

Sensitivity Analysis of Weather Variables on Offsite Consequence Analysis Tools in South Korea and the United States.

PubMed

Kim, Min-Uk; Moon, Kyong Whan; Sohn, Jong-Ryeul; Byeon, Sang-Hoon

2018-05-18

We studied sensitive weather variables for consequence analysis, in the case of chemical leaks on the user side of offsite consequence analysis (OCA) tools. We used OCA tools Korea Offsite Risk Assessment (KORA) and Areal Location of Hazardous Atmospheres (ALOHA) in South Korea and the United States, respectively. The chemicals used for this analysis were 28% ammonia (NH₃), 35% hydrogen chloride (HCl), 50% hydrofluoric acid (HF), and 69% nitric acid (HNO₃). The accident scenarios were based on leakage accidents in storage tanks. The weather variables were air temperature, wind speed, humidity, and atmospheric stability. Sensitivity analysis was performed using the Statistical Package for the Social Sciences (SPSS) program for dummy regression analysis. Sensitivity analysis showed that impact distance was not sensitive to humidity. Impact distance was most sensitive to atmospheric stability, and was also more sensitive to air temperature than wind speed, according to both the KORA and ALOHA tools. Moreover, the weather variables were more sensitive in rural conditions than in urban conditions, with the ALOHA tool being more influenced by weather variables than the KORA tool. Therefore, if using the ALOHA tool instead of the KORA tool in rural conditions, users should be careful not to cause any differences in impact distance due to input errors of weather variables, with the most sensitive one being atmospheric stability.

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals

PubMed Central

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-01-01

Abstract We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support. From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs. The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs. The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses. PMID:29489648

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals.

PubMed

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-03-01

We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support.From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs.The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs.The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses.

Changes in dark chocolate volatiles during storage.

PubMed

Nightingale, Lia M; Cadwallader, Keith R; Engeseth, Nicki J

2012-05-09

Chocolate storage is critical to the quality of the final product. Inadequate storage, especially with temperature fluctuations, may lead to a change in crystal structure, which may eventually cause fat bloom. Bloom is the main cause of quality loss in the chocolate industry. The impact of various storage conditions on the flavor quality of dark chocolate was determined. Dark chocolate was stored in different conditions leading to either fat or sugar bloom and analyzed at 0, 4, and 8 weeks of storage. Changes in chocolate flavor were determined by volatile analysis and descriptive sensory evaluation. Results were analyzed by analysis of variance (ANOVA), cluster analysis, principal component analysis (PCA), and linear partial least-squares regression analysis (PLS). Volatile concentration and loss were significantly affected by storage conditions. Chocolates stored at high temperature were the most visually and texturally compromised, but volatile concentrations were affected the least, whereas samples stored at ambient, frozen, and high relative humidity conditions had significant volatile loss during storage. It was determined that high-temperature storage caused a change in crystal state due to the polymorphic shift to form VI, leading to an increase in sample hardness. Decreased solid fat content (SFC) during high-temperature storage increased instrumentally determined volatile retention, although no difference was detected in chocolate flavor during sensory analysis, possibly due to instrumental and sensory sampling techniques. When all instrumental and sensory data had been taken into account, the storage condition that had the least impact on texture, surface roughness, grain size, lipid polymorphism, fat bloom formation, volatile concentrations, and sensory attributes was storage at constant temperature and 75% relative humidity.

Missing Rings in Pinus halepensis – The Missing Link to Relate the Tree-Ring Record to Extreme Climatic Events

PubMed Central

Novak, Klemen; de Luis, Martin; Saz, Miguel A.; Longares, Luis A.; Serrano-Notivoli, Roberto; Raventós, Josep; Čufar, Katarina; Gričar, Jožica; Di Filippo, Alfredo; Piovesan, Gianluca; Rathgeber, Cyrille B. K.; Papadopoulos, Andreas; Smith, Kevin T.

2016-01-01

Climate predictions for the Mediterranean Basin include increased temperatures, decreased precipitation, and increased frequency of extreme climatic events (ECE). These conditions are associated with decreased tree growth and increased vulnerability to pests and diseases. The anatomy of tree rings responds to these environmental conditions. Quantitatively, the width of a tree ring is largely determined by the rate and duration of cell division by the vascular cambium. In the Mediterranean climate, this division may occur throughout almost the entire year. Alternatively, cell division may cease during relatively cool and dry winters, only to resume in the same calendar year with milder temperatures and increased availability of water. Under particularly adverse conditions, no xylem may be produced in parts of the stem, resulting in a missing ring (MR). A dendrochronological network of Pinus halepensis was used to determine the relationship of MR to ECE. The network consisted of 113 sites, 1,509 trees, 2,593 cores, and 225,428 tree rings throughout the distribution range of the species. A total of 4,150 MR were identified. Binomial logistic regression analysis determined that MR frequency increased with increased cambial age. Spatial analysis indicated that the geographic areas of south-eastern Spain and northern Algeria contained the greatest frequency of MR. Dendroclimatic regression analysis indicated a non-linear relationship of MR to total monthly precipitation and mean temperature. MR are strongly associated with the combination of monthly mean temperature from previous October till current February and total precipitation from previous September till current May. They are likely to occur with total precipitation lower than 50 mm and temperatures higher than 5°C. This conclusion is global and can be applied to every site across the distribution area. Rather than simply being a complication for dendrochronology, MR formation is a fundamental response of trees to adverse environmental conditions. The demonstrated relationship of MR formation to ECE across this dendrochronological network in the Mediterranean basin shows the potential of MR analysis to reconstruct the history of past climatic extremes and to predict future forest dynamics in a changing climate. PMID:27303421

Missing Rings in Pinus halepensis - The Missing Link to Relate the Tree-Ring Record to Extreme Climatic Events.

PubMed

Novak, Klemen; de Luis, Martin; Saz, Miguel A; Longares, Luis A; Serrano-Notivoli, Roberto; Raventós, Josep; Čufar, Katarina; Gričar, Jožica; Di Filippo, Alfredo; Piovesan, Gianluca; Rathgeber, Cyrille B K; Papadopoulos, Andreas; Smith, Kevin T

2016-01-01

Climate predictions for the Mediterranean Basin include increased temperatures, decreased precipitation, and increased frequency of extreme climatic events (ECE). These conditions are associated with decreased tree growth and increased vulnerability to pests and diseases. The anatomy of tree rings responds to these environmental conditions. Quantitatively, the width of a tree ring is largely determined by the rate and duration of cell division by the vascular cambium. In the Mediterranean climate, this division may occur throughout almost the entire year. Alternatively, cell division may cease during relatively cool and dry winters, only to resume in the same calendar year with milder temperatures and increased availability of water. Under particularly adverse conditions, no xylem may be produced in parts of the stem, resulting in a missing ring (MR). A dendrochronological network of Pinus halepensis was used to determine the relationship of MR to ECE. The network consisted of 113 sites, 1,509 trees, 2,593 cores, and 225,428 tree rings throughout the distribution range of the species. A total of 4,150 MR were identified. Binomial logistic regression analysis determined that MR frequency increased with increased cambial age. Spatial analysis indicated that the geographic areas of south-eastern Spain and northern Algeria contained the greatest frequency of MR. Dendroclimatic regression analysis indicated a non-linear relationship of MR to total monthly precipitation and mean temperature. MR are strongly associated with the combination of monthly mean temperature from previous October till current February and total precipitation from previous September till current May. They are likely to occur with total precipitation lower than 50 mm and temperatures higher than 5°C. This conclusion is global and can be applied to every site across the distribution area. Rather than simply being a complication for dendrochronology, MR formation is a fundamental response of trees to adverse environmental conditions. The demonstrated relationship of MR formation to ECE across this dendrochronological network in the Mediterranean basin shows the potential of MR analysis to reconstruct the history of past climatic extremes and to predict future forest dynamics in a changing climate.

Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

Regression away from the mean: Theory and examples.

PubMed

Schwarz, Wolf; Reike, Dennis

2018-02-01

Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs. © 2017 The British Psychological Society.

Regression: The Apple Does Not Fall Far From the Tree.

PubMed

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

Comparison of vertical E × B drift velocities and ground-based magnetometer observations of DELTA H in the low latitude under geomagnetically disturbed conditions

NASA Astrophysics Data System (ADS)

Prabhu, M.; Unnikrishnan, K.

2018-04-01

In the present work, we analyzed the daytime vertical E × B drift velocities obtained from Jicamarca Unattended Long-term Ionosphere Atmosphere (JULIA) radar and ΔH component of geomagnetic field measured as the difference between the magnitudes of the horizontal (H) components between two magnetometers deployed at two different locations Jicamarca, and Piura in Peru for 22 geomagnetically disturbed events in which either SC has occurred or Dstmax < -50 nT during the period 2006-2011. The ΔH component of geomagnetic field is measured as the differences in the magnitudes of horizontal H component between magnetometer placed directly on the magnetic equator and one displaced 6-9° away. It will provide a direct measure of the daytime electrojet current, due to the eastward electric field. This will in turn gives the magnitude of vertical E × B drift velocity in the F region. A positive correlation exists between peak values of daytime vertical E × B drift velocity and peak value of ΔH for the three consecutive days of the events. It was observed that 45% of the events have daytime vertical E × B drift velocity peak in the magnitude range 10-20 m/s and 20-30 m/s and 20% have peak ΔH in the magnitude range 50-60 nT and 80-90 nT. It was observed that the time of occurrence of the peak value of both the vertical E × B drift velocity and the ΔH have a maximum (40%) probability in the same time range 11:00-13:00 LT. We also investigated the correlation between E × B drift velocity and Dst index and the correlation between delta H and Dst index. A strong positive correlation is found between E × B drift and Dst index as well as between delta H and Dst Index. Three different techniques of data analysis - linear, polynomial (order 2), and polynomial (order 3) regression analysis were considered. The regression parameters in all the three cases were calculated using the Least Square Method (LSM), using the daytime vertical E × B drift velocity and ΔH. A formula was developed which indicates the relationship between daytime vertical E × B drift velocity and ΔH, for the disturbed periods. The E × B drift velocity was then evaluated using the formulae thus found for the three regression analysis and validated for the 'disturbed periods' of 3 selected events. The E × B drift velocities estimated by the three regression analysis have a fairly good agreement with JULIA radar observed values under different seasons and solar activity conditions. Root Mean Square (RMS) errors calculated for each case suggest that polynomial (order 3) regression analysis provides a better agreement with the observations from among the three.

Solid precipitation measurement intercomparison in Bismarck, North Dakota, from 1988 through 1997

USGS Publications Warehouse

Ryberg, Karen R.; Emerson, Douglas G.; Macek-Rowland, Kathleen M.

2009-01-01

A solid precipitation measurement intercomparison was recommended by the World Meteorological Organization (WMO) and was initiated after approval by the ninth session of the Commission for Instruments and Methods of Observation. The goal of the intercomparison was to assess national methods of measuring solid precipitation against methods whose accuracy and reliability were known. A field study was started in Bismarck, N. Dak., during the 1988-89 winter as part of the intercomparison. The last official field season of the WMO intercomparison was 1992-93; however, the Bismarck site continued to operate through the winter of 1996-97. Precipitation events at Bismarck were categorized as snow, mixed, or rain on the basis of descriptive notes recorded as part of the solid precipitation intercomparison. The rain events were not further analyzed in this study. Catch ratios (CRs) - the ratio of the precipitation catch at each gage to the true precipitation measurement (the corrected double fence intercomparison reference) - were calculated. Then, regression analysis was used to develop equations that model the snow and mixed precipitation CRs at each gage as functions of wind speed and temperature. Wind speed at the gages, functions of temperature, and upper air conditions (wind speed and air temperature at 700 millibars pressure) were used as possible explanatory variables in the multiple regression analysis done for this study. The CRs were modeled by using multiple regression analysis for the Tretyakov gage, national shielded gage, national unshielded gage, AeroChem gage, national gage with double fence, and national gage with Wyoming windshield. As in earlier studies by the WMO, wind speed and air temperature were found to influence the CR of the Tretyakov gage. However, in this study, the temperature variable represented the average upper air temperature over the duration of the event. The WMO did not use upper air conditions in its analysis. The national shielded and unshielded gages where found to be influenced by functions of wind speed only, as in other studies, but the upper air wind speed was used as an explanatory variable in this study. The AeroChem gage was not used in the WMO intercomparison study for 1987-93. The AeroChem gage had a highly varied CR at Bismarck, and a number of variables related to wind speed and temperature were used in the model for the CR. Despite extensive efforts to find a model for the national gage with double fence, no statistically significant regression model was found at the 0.05 level of statistical significance. The national gage with Wyoming windshield had a CR modeled by temperature and wind speed variables, and the regression relation had the highest coefficient of determination (R2 = 0.572) and adjusted coefficient of multiple determination (R2a = 0.476) of all of the models identified for any gage. Three of the gage CRs evaluated could be compared with those in the WMO intercomparison study for 1987-93. The WMO intercomparison had the advantage of a much larger dataset than this study. However, the data in this study represented a longer time period. Snow precipitation catch is highly varied depending on the equipment used and the weather conditions. Much of the variation is not accounted for in the WMO equations or in the equations developed in this study, particularly for unshielded gages. Extensive attempts at regression analysis were made with the mixed precipitation data, but it was concluded that the sample sizes were not large enough to model the CRs. However, the data could be used to test the WMO intercomparison equations. The mixed precipitation equations for the Tretyakov and national shielded gages are similar to those for snow in that they are more likely to underestimate precipitation when observed amounts were small and overestimate precipitation when observed amounts were relatively large. Mixed precipitation is underestimated by the WMO adjustment and t

A generalized right truncated bivariate Poisson regression model with applications to health data.

PubMed

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

A generalized right truncated bivariate Poisson regression model with applications to health data

PubMed Central

Islam, M. Ataharul; Chowdhury, Rafiqul I.

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model. PMID:28586344

No evidence of reaction time slowing in autism spectrum disorder.

PubMed

Ferraro, F Richard

2016-01-01

A total of 32 studies comprising 238 simple reaction time and choice reaction time conditions were examined in individuals with autism spectrum disorder (n = 964) and controls (n = 1032). A Brinley plot/multiple regression analysis was performed on mean reaction times, regressing autism spectrum disorder performance onto the control performance as a way to examine any generalized simple reaction time/choice reaction time slowing exhibited by the autism spectrum disorder group. The resulting regression equation was Y (autism spectrum disorder) = 0.99 × (control) + 87.93, which accounted for 92.3% of the variance. These results suggest that there are little if any simple reaction time/choice reaction time slowing in this sample of individual with autism spectrum disorder, in comparison with controls. While many cognitive and information processing domains are compromised in autism spectrum disorder, it appears that simple reaction time/choice reaction time remain relatively unaffected in autism spectrum disorder. © The Author(s) 2014.

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package.

PubMed

Reid, Stephen; Tibshirani, Rob

2014-07-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso [Formula: see text] and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by.

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package

PubMed Central

Reid, Stephen; Tibshirani, Rob

2014-01-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso (ℓ1) and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by. PMID:26257587

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Nonparametric instrumental regression with non-convex constraints

NASA Astrophysics Data System (ADS)

Grasmair, M.; Scherzer, O.; Vanhems, A.

2013-03-01

This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.

Assessment of Response Surface Models using Independent Confirmation Point Analysis

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2010-01-01

This paper highlights various advantages that confirmation-point residuals have over conventional model design-point residuals in assessing the adequacy of a response surface model fitted by regression techniques to a sample of experimental data. Particular advantages are highlighted for the case of design matrices that may be ill-conditioned for a given sample of data. The impact of both aleatory and epistemological uncertainty in response model adequacy assessments is considered.

[Risk factors for asthma in children in Hefei, China].

PubMed

Xiong, Mei; Ni, Chen; Pan, Jia-Hua; Wang, Qiang; Zheng, Li-Lin

2013-05-01

To investigate the risk factors for asthma in children in Hefei, China and to provide a strategy for asthma control in this region. A total of 400 children with a confirmed diagnosis of asthma, as well as 400 children of comparable age, sex, living environment, and family background, who had no respiratory diseases, were selected for a case-control study. A survey questionnaire survey was completed for all children. The obtained data were subjected to univariate and multivariate logistic regression analysis to determine the risk factors for asthma. The logistic regression analysis showed that a family history of allergy, allergic rhinitis, infantile eczema, no breastfeeding, air-conditioning and passive smoking were the risk factors for asthma in children, with odds ratios of 9.63, 7.56, 4.58, 2.16, 1.73, and 1.55 respectively. In order to reduce the incidence of asthma, we should advocate breast feeding, promote outdoor activities, keep ventilation natural, prevent passive smoking and cure allergic rhinitis.

The relationship between turbidity of mouth-rinsed water and oral health status.

PubMed

Takeuchi, Susumu; Ueno, Masayuki; Takehara, Sachiko; Pham, Thuy Anh Vu; Hakuta, Chiyoko; Morishima, Seiji; Shinada, Kayoko; Kawaguchi, Yoko

2013-01-01

The purpose of this study was to examine the relationship between turbidity of mouth rinsed water and oral health status such as dental and periodontal conditions, oral hygiene status, flow rate of saliva and oral bacteria. Subjects were 165 patients who visited the Dental Hospital, Tokyo Medical and Dental University. Oral health status, including dental and periodontal conditions, oral hygiene status and flow rate of saliva, was clinically examined. The turbidity was measured with a turbidimeter. Quantification of Fusobacterium spp, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola and total bacteria levels was performed using real-time PCR. The Pearson correlation and multiple regression analysis were used to explore the associations between the turbidity and oral health parameters. The turbidity showed significant correlations with the number of decayed teeth and deep pockets, the plaque index, extent of tongue coating and Fusobacterium spp, P. gingivalis, T. forsythia, T. denticola and total bacteria levels. In a multiple regression model, the turbidity was negatively associated with the flow rate of saliva and positively associated with the total number of bacteria (p < 0.001). Current findings suggested that turbidity of mouth rinsed water could be used as an indicator to evaluate oral health condition and the amount of bacteria in the oral cavity. In addition, the turbiditimeter appeared as a simple and objective device for screening abnormality of oral health condition at chair side as well as community-based research.

Explaining and modeling the concentration and loading of Escherichia coli in a stream-A case study.

PubMed

Wang, Chaozi; Schneider, Rebecca L; Parlange, Jean-Yves; Dahlke, Helen E; Walter, M Todd

2018-09-01

Escherichia coli (E. coli) level in streams is a public health indicator. Therefore, being able to explain why E. coli levels are sometimes high and sometimes low is important. Using citizen science data from Fall Creek in central NY we found that complementarily using principal component analysis (PCA) and partial least squares (PLS) regression provided insights into the drivers of E. coli and a mechanism for predicting E. coli levels, respectively. We found that stormwater, temperature/season and shallow subsurface flow are the three dominant processes driving the fate and transport of E. coli. PLS regression modeling provided very good predictions under stormwater conditions (R 2  = 0.85 for log (E. coli concentration) and R 2  = 0.90 for log (E. coli loading)); predictions under baseflow conditions were less robust. But, in our case, both E. coli concentration and E. coli loading were significantly higher under stormwater condition, so it is probably more important to predict high-flow E. coli hazards than low-flow conditions. Besides previously reported good indicators of in-stream E. coli level, nitrate-/nitrite-nitrogen and soluble reactive phosphorus were also found to be good indicators of in-stream E. coli levels. These findings suggest management practices to reduce E. coli concentrations and loads in-streams and, eventually, reduce the risk of waterborne disease outbreak. Copyright © 2018. Published by Elsevier B.V.

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

Risk estimation using probability machines.

PubMed

Dasgupta, Abhijit; Szymczak, Silke; Moore, Jason H; Bailey-Wilson, Joan E; Malley, James D

2014-03-01

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.

Visible spectroscopy as a tool for the assessment of storage conditions of fresh pork packaged in modified atmosphere.

PubMed

Spanos, Dimitrios; Christensen, Mette; Tørngren, Mari Ann; Baron, Caroline P

2016-03-01

The storage conditions of fresh meat are known to impact its colour and microbial shelf life. In the present study, visible spectroscopy was evaluated as a method to assess meat storage conditions and its optimisation. Fresh pork steaks (longissimus thoracis et lumborum and semimembranosus) were placed in modified atmosphere packaging using gas mixtures containing 0, 40, 50, and 80% oxygen, and stored with or without light for up to 9days. Principal component analysis of visible reflectance spectra (400-700nm) showed that the colour of the different meat cuts was affected by presence of oxygen, illumination, and storage time. Differences in the oxygen levels did not contribute to the observed variance. Predictive models based on partial least squares regression-discriminant analysis exhibited high potency in the classification of the storage parameters of meat cuts packaged in modified atmosphere. The study demonstrates the applicability of visible spectroscopy as a tool to assess the storage conditions of meat cuts packaged in modified atmosphere. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

PubMed

Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

2012-01-01

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Estimating Blade Section Airloads from Blade Leading-Edge Pressure Measurements

NASA Technical Reports Server (NTRS)

vanAken, Johannes M.

2003-01-01

The Tilt-Rotor Aeroacoustic Model (TRAM) test in the Duitse-Nederlandse Wind (DNW) Tunnel acquired blade pressure data for forward flight test conditions of a tiltrotor in helicopter mode. Chordwise pressure data at seven radial locations were integrated to obtain the blade section normal force. The present investigation evaluates the use of linear regression analysis and of neural networks in estimating the blade section normal force coefficient from a limited number of blade leading-edge pressure measurements and representative operating conditions. These network models are subsequently used to estimate the airloads at intermediate radial locations where only blade pressure measurements at the 3.5% chordwise stations are available.

Meta-Milgram: An Empirical Synthesis of the Obedience Experiments

PubMed Central

Haslam, Nick; Loughnan, Steve; Perry, Gina

2014-01-01

Milgram's famous experiment contained 23 small-sample conditions that elicited striking variations in obedient responding. A synthesis of these diverse conditions could clarify the factors that influence obedience in the Milgram paradigm. We assembled data from the 21 conditions (N = 740) in which obedience involved progression to maximum voltage (overall rate 43.6%) and coded these conditions on 14 properties pertaining to the learner, the teacher, the experimenter, the learner-teacher relation, the experimenter-teacher relation, and the experimental setting. Logistic regression analysis indicated that eight factors influenced the likelihood that teachers continued to the 450 volt shock: the experimenter's directiveness, legitimacy, and consistency; group pressure on the teacher to disobey; the indirectness, proximity, and intimacy of the relation between teacher and learner; and the distance between the teacher and the experimenter. Implications are discussed. PMID:24705407

A regression technique for evaluation and quantification for water quality parameters from remote sensing data

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.

1979-01-01

The objective of this paper is to define optical physics and/or environmental conditions under which the linear multiple-regression should be applicable. An investigation of the signal-response equations is conducted and the concept is tested by application to actual remote sensing data from a laboratory experiment performed under controlled conditions. Investigation of the signal-response equations shows that the exact solution for a number of optical physics conditions is of the same form as a linearized multiple-regression equation, even if nonlinear contributions from surface reflections, atmospheric constituents, or other water pollutants are included. Limitations on achieving this type of solution are defined.

Body sway, aim point fluctuation and performance in rifle shooters: inter- and intra-individual analysis.

PubMed

Ball, Kevin A; Best, Russell J; Wrigley, Tim V

2003-07-01

In this study, we examined the relationships between body sway, aim point fluctuation and performance in rifle shooting on an inter- and intra-individual basis. Six elite shooters performed 20 shots under competition conditions. For each shot, body sway parameters and four aim point fluctuation parameters were quantified for the time periods 5 s to shot, 3 s to shot and 1 s to shot. Three parameters were used to indicate performance. An AMTI LG6-4 force plate was used to measure body sway parameters, while a SCATT shooting analysis system was used to measure aim point fluctuation and shooting performance. Multiple regression analysis indicated that body sway was related to performance for four shooters. Also, body sway was related to aim point fluctuation for all shooters. These relationships were specific to the individual, with the strength of association, parameters of importance and time period of importance different for different shooters. Correlation analysis of significant regressions indicated that, as body sway increased, performance decreased and aim point fluctuation increased for most relationships. We conclude that body sway and aim point fluctuation are important in elite rifle shooting and performance errors are highly individual-specific at this standard. Individual analysis should be a priority when examining elite sports performance.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

NASA Technical Reports Server (NTRS)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

[Study on depressive disorder and related factors in surgical inpatients].

PubMed

Ge, Hong-min; Liu, Lan-fen; Han, Jian-bo

2008-03-01

To investigate the prevalence and possible influencing factors of depressive disorder in surgical inpatients. Two hundred and sixty-six surgical inpatients meeting the inclusion criteria were first screened with the self rating depression scale (SDS), and then the subjects screened positive and 20% of those screened negative were evaluated with Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) as a gold standard for diagnosis of depressive disorder. Possible influencing factors were also analyzed by experienced psychiatrists. The standard score of SDS in the surgical inpatients were significantly higher than those in the Chinese norm, and the incidence of depressive disorder in the surgical inpatients was 37.2%. Unvaried analysis showed that depressive disorder were associated with gender, education, economic condition, variety of diseases, hospitalization duration, and treatment methods. Logistic regression analysis revealed that gender, economic condition, treatment methods and previous history were the main influencing factors. The incidence of depressive disorder in the surgical inpatients is high, and it is mainly influenced by gender, economic condition, treatment methods and previous history.

Modeling and optimization of red currants vacuum drying process by response surface methodology (RSM).

PubMed

Šumić, Zdravko; Vakula, Anita; Tepić, Aleksandra; Čakarević, Jelena; Vitas, Jasmina; Pavlić, Branimir

2016-07-15

Fresh red currants were dried by vacuum drying process under different drying conditions. Box-Behnken experimental design with response surface methodology was used for optimization of drying process in terms of physical (moisture content, water activity, total color change, firmness and rehydratation power) and chemical (total phenols, total flavonoids, monomeric anthocyanins and ascorbic acid content and antioxidant activity) properties of dried samples. Temperature (48-78 °C), pressure (30-330 mbar) and drying time (8-16 h) were investigated as independent variables. Experimental results were fitted to a second-order polynomial model where regression analysis and analysis of variance were used to determine model fitness and optimal drying conditions. The optimal conditions of simultaneously optimized responses were temperature of 70.2 °C, pressure of 39 mbar and drying time of 8 h. It could be concluded that vacuum drying provides samples with good physico-chemical properties, similar to lyophilized sample and better than conventionally dried sample. Copyright © 2016 Elsevier Ltd. All rights reserved.

Contrast sensitivity to spatial gratings in moderate and dim light conditions in patients with diabetes in the absence of diabetic retinopathy.

PubMed

Safi, Sare; Rahimi, Anoushiravan; Raeesi, Afsaneh; Safi, Hamid; Aghazadeh Amiri, Mohammad; Malek, Mojtaba; Yaseri, Mehdi; Haeri, Mohammad; Middleton, Frank A; Solessio, Eduardo; Ahmadieh, Hamid

2017-01-01

To evaluate the ability of contrast sensitivity (CS) to discriminate loss of visual function in diabetic subjects with no clinical signs of retinopathy relative to that of normal subjects. In this prospective cross-sectional study, we measured CS in 46 diabetic subjects with a mean age of 48±6 years, a best-corrected visual acuity of 20/20 and no signs of diabetic retinopathy. The CS in these subjects was compared with CS measurements in 46 normal control subjects at four spatial frequencies (3, 6, 12, 18 cycles per degree) under moderate (500 lux) and dim (less than 2 lux) background light conditions. CS was approximately 0.16 log units lower in patients with diabetes relative to controls both in moderate and in dim background light conditions. Logistic regression classification and receiver operating characteristic curve analysis indicated that CS analysis using two light conditions was more accurate (0.78) overall compared with CS analysis using only a single illumination condition (accuracy values were 0.67 and 0.70 in moderate and dim light conditions, respectively). Our results showed that patients with diabetes without clinical signs of retinopathy exhibit a uniform loss in CS at all spatial frequencies tested. Measuring the loss in CS at two spatial frequencies (3 and 6 cycles per degree) and two light conditions (moderate and dim) is sufficiently robust to classify diabetic subjects with no retinopathy versus control subjects.

Image analysis software for following progression of peripheral neuropathy

NASA Astrophysics Data System (ADS)

Epplin-Zapf, Thomas; Miller, Clayton; Larkin, Sean; Hermesmeyer, Eduardo; Macy, Jenny; Pellegrini, Marco; Luccarelli, Saverio; Staurenghi, Giovanni; Holmes, Timothy

2009-02-01

A relationship has been reported by several research groups [1 - 4] between the density and shapes of nerve fibers in the cornea and the existence and severity of peripheral neuropathy. Peripheral neuropathy is a complication of several prevalent diseases or conditions, which include diabetes, HIV, prolonged alcohol overconsumption and aging. A common clinical technique for confirming the condition is intramuscular electromyography (EMG), which is invasive, so a noninvasive technique like the one proposed here carries important potential advantages for the physician and patient. A software program that automatically detects the nerve fibers, counts them and measures their shapes is being developed and tested. Tests were carried out with a database of subjects with levels of severity of diabetic neuropathy as determined by EMG testing. Results from this testing, that include a linear regression analysis are shown.

Numerical analysis and experimental studies on solenoid common rail diesel injector with worn control valve

NASA Astrophysics Data System (ADS)

Krivtsov, S. N.; Yakimov, I. V.; Ozornin, S. P.

2018-03-01

A mathematical model of a solenoid common rail fuel injector was developed. Its difference from existing models is control valve wear simulation. A common rail injector of 0445110376 Series (Cummins ISf 2.8 Diesel engine) produced by Bosch Company was used as a research object. Injector parameters (fuel delivery and back leakage) were determined by calculation and experimental methods. GT-Suite model average R2 is 0.93 which means that it predicts the injection rate shape very accurately (nominal and marginal technical conditions of an injector). Numerical analysis and experimental studies showed that control valve wear increases back leakage and fuel delivery (especially at 160 MPa). The regression models for determining fuel delivery and back leakage effects on fuel pressure and energizing time were developed (for nominal and marginal technical conditions).

A spatial analysis of social and economic determinants of tuberculosis in Brazil.

PubMed

Harling, Guy; Castro, Marcia C

2014-01-01

We investigated the spatial distribution, and social and economic correlates, of tuberculosis in Brazil between 2002 and 2009 using municipality-level age/sex-standardized tuberculosis notification data. Rates were very strongly spatially autocorrelated, being notably high in urban areas on the eastern seaboard and in the west of the country. Non-spatial ecological regression analyses found higher rates associated with urbanicity, population density, poor economic conditions, household crowding, non-white population and worse health and healthcare indicators. These associations remained in spatial conditional autoregressive models, although the effect of poverty appeared partially confounded by urbanicity, race and spatial autocorrelation, and partially mediated by household crowding. Our analysis highlights both the multiple relationships between socioeconomic factors and tuberculosis in Brazil, and the importance of accounting for spatial factors in analysing socioeconomic determinants of tuberculosis. © 2013 Published by Elsevier Ltd.

Germplasm-regression-combined (GRC) marker-trait association identification in plant breeding: a challenge for plant biotechnological breeding under soil water deficit conditions.

PubMed

Ruan, Cheng-Jiang; Xu, Xue-Xuan; Shao, Hong-Bo; Jaleel, Cheruth Abdul

2010-09-01

In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL) identification and marker assisted selection (MAS). However, results have been modest. This has been due to several factors including absence of tight linkage QTL, non-availability of mapping populations, and substantial time needed to develop such populations. To overcome these limitations, and as an alternative to planned populations, molecular marker-trait associations have been identified by the combination between germplasm and the regression technique. In the present preview, the authors (1) survey the successful applications of germplasm-regression-combined (GRC) molecular marker-trait association identification in plants; (2) describe how to do the GRC analysis and its differences from mapping QTL based on a linkage map reconstructed from the planned populations; (3) consider the factors that affect the GRC association identification, including selections of optimal germplasm and molecular markers and testing of identification efficiency of markers associated with traits; and (4) finally discuss the future prospects of GRC marker-trait association analysis used in plant MAS/QTL breeding programs, especially in long-juvenile woody plants when no other genetic information such as linkage maps and QTL are available.

Methods of estimating or accounting for neighborhood associations with health using complex survey data.

PubMed

Brumback, Babette A; Cai, Zhuangyu; Dailey, Amy B

2014-05-15

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.

Small bowel lymphangiectasia and angiodysplasia: a positive association; novel clinical marker or shared pathophysiology?

PubMed

Macdonald, Jonathan; Porter, Victoria; Scott, Neil W; McNamara, Deirdre

2010-10-01

Small bowel angiodysplasia accounts for 30 to 40% of cases of obscure gastrointestinal bleeding and is associated with significant morbidity and mortality. Identifying lesions can be difficult. Small bowel capsule endoscopy (SBCE) is a significant advance on earlier diagnostic techniques. The cause of angiodysplasia is unknown and the natural history poorly understood. Many lesions are thought to arise from a degenerative process associated with ageing, local vascular anomalies, and tissue hypoxia. Nonpathologic lymphangiectasias are commonly seen throughout the small bowel and are considered a normal finding. To determine whether there is an association between lymphangiectasias, angiodysplasia, and atherosclerosis related conditions. Relevant information was collected from a dedicated SBCE database. Logistic regression analysis was used to examine associations between angiodysplasia, lymphangiectasia, patient demographics, and comorbidity. In all, 180 patients underwent SBCE during the study period, 46 (25%) had angiodysplasia and 47 (26%) lymphangiectasia. Lymphangiectasia were seen in 24 (52%) of 46 with angiodysplasia, in 16 (19%) of 84 with obscure gastrointestinal bleeding without angiodysplasia and in 7 (14%) of 50 without gastrointestinal bleeding. Logistic regression analysis confirmed a strong positive association between angiodysplasia and lymphangiectasia; odds ratio 4.42, P<0.003. Angiodysplasias were also associated with increasing age; odds ratio 1.1. There was no correlation with any other patient characteristic. Lymphangiectasia are strongly associated with the presence of small intestinal angiodysplasia and may represent a useful clinical marker for this condition. Angiodysplasia are also associated with increasing age. Conditions associated with systemic atherosclerosis did not increase the risk of angiodysplasia.

Conditional Density Estimation with HMM Based Support Vector Machines

NASA Astrophysics Data System (ADS)

Hu, Fasheng; Liu, Zhenqiu; Jia, Chunxin; Chen, Dechang

Conditional density estimation is very important in financial engineer, risk management, and other engineering computing problem. However, most regression models have a latent assumption that the probability density is a Gaussian distribution, which is not necessarily true in many real life applications. In this paper, we give a framework to estimate or predict the conditional density mixture dynamically. Through combining the Input-Output HMM with SVM regression together and building a SVM model in each state of the HMM, we can estimate a conditional density mixture instead of a single gaussian. With each SVM in each node, this model can be applied for not only regression but classifications as well. We applied this model to denoise the ECG data. The proposed method has the potential to apply to other time series such as stock market return predictions.

Recovery and concordance in a secure forensic psychiatry hospital - the self rated DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales.

PubMed

Davoren, Mary; Hennessy, Sarah; Conway, Catherine; Marrinan, Seamus; Gill, Pauline; Kennedy, Harry G

2015-03-28

Detention in a secure forensic psychiatric hospital may inhibit engagement and recovery. Having validated the clinician rated DUNDRUM-3 (programme completion) and DUNDRUM-4 (recovery) in a forensic hospital, we set out to draft and validate scales measuring the same programme completion and recovery items that patients could use to self-rate. Based on previous work, we hypothesised that self-rating scores might be predictors of objective progress including conditional discharge. We hypothesised also that the difference between patients' and clinicians' ratings of progress in treatment and other factors relevant to readiness for discharge (concordance) would diminish as patients neared discharge. We hypothesised also that this difference in matched scores would predict objective progress including conditional discharge. In a prospective naturalistic observational cohort study in a forensic hospital, we examined whether scores on the self-rated DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales or differences between clinician and patient ratings on the same scales (concordance) would predict moves between levels of therapeutic security and conditional discharge over the next twelve months. Both scales stratified along the recovery pathway of the hospital, but clinician ratings matched the level of therapeutic security more accurately than self ratings. The clinician rated scales predicted moves to less secure units and to more secure units and predicted conditional discharge but the self-rated scores did not. The difference between clinician and self-rated scores (concordance) predicted positive and negative moves and conditional discharge, but this was not always an independent predictor as shown by regression analysis. In regression analysis the DUNDRUM-3 predicted moves to less secure places though the HCR-20 C & R score dominated the model. Moves back to more secure places were predicted by lack of concordance on the DUNDRUM-4. Conditional discharge was predicted predominantly by the DUNDRUM-3. Patients accurately self-rate relative to other patients however their absolute ratings were consistently lower (better) than clinicians' ratings and were less accurate predictors of outcomes including conditional discharge. Quantifying concordance is a useful part of the recovery process and predicts outcomes but self-ratings are not accurate predictors.

Genetic variation of the androgen receptor and risk of myocardial infarction and ischemic stroke in women.

PubMed

Rexrode, Kathryn M; Ridker, Paul M; Hegener, Hillary H; Buring, Julie E; Manson, JoAnn E; Zee, Robert Y L

2008-05-01

Androgen receptors (AR) are expressed in endothelial cells and vascular smooth-muscle cells. Some studies suggest an association between AR gene variation and risk of cardiovascular disease (CVD) in men; however, the relationship has not been examined in women. Six haplotype block-tagging single nucleotide polymorphisms (rs962458, rs6152, rs1204038, rs2361634, rs1337080, rs1337082), as well as the cysteine, adenine, guanine (CAG) microsatellite in exon 1, of the AR gene were evaluated among 300 white postmenopausal women who developed CVD (158 myocardial infarctions and 142 ischemic strokes) and an equal number of matched controls within the Women's Health Study. Genotype distributions were similar between cases and controls, and genotypes were not significantly related to risk of CVD, myocardial infarctions or ischemic stroke in conditional logistic regression models. Seven common haplotypes were observed, but distributions did not differ between cases and controls nor were significant associations observed in logistic regression analysis. The median CAG repeat length was 21. In conditional logistic regression, there was no association between the number of alleles with CAG repeat length >or=21 (or >or=22) and risk of CVD, myocardial infarctions or ischemic stroke. No association between AR genetic variation, as measured by haplotype-tagging single nucleotide polymorphisms and CAG repeat number, and risk of CVD was observed in women.

Linear regression analysis of Hospital Episode Statistics predicts a large increase in demand for elective hand surgery in England.

PubMed

Bebbington, Emily; Furniss, Dominic

2015-02-01

We integrated two factors, demographic population shifts and changes in prevalence of disease, to predict future trends in demand for hand surgery in England, to facilitate workforce planning. We analysed Hospital Episode Statistics data for Dupuytren's disease, carpal tunnel syndrome, cubital tunnel syndrome, and trigger finger from 1998 to 2011. Using linear regression, we estimated trends in both diagnosis and surgery until 2030. We integrated this regression with age specific population data from the Office for National Statistics in order to estimate how this will contribute to a change in workload over time. There has been a significant increase in both absolute numbers of diagnoses and surgery for all four conditions. Combined with future population data, we calculate that the total operative burden for these four conditions will increase from 87,582 in 2011 to 170,166 (95% confidence interval 144,517-195,353) in 2030. The prevalence of these diseases in the ageing population, and increasing prevalence of predisposing factors such as obesity and diabetes, may account for the predicted increase in workload. The most cost effective treatments must be sought, which requires high quality clinical trials. Our methodology can be applied to other sub-specialties to help anticipate the need for future service provision. Copyright © 2014 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

PubMed

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

Acupuncture for musculoskeletal pain: A meta-analysis and meta-regression of sham-controlled randomized clinical trials

PubMed Central

Yuan, Qi-ling; Wang, Peng; Liu, Liang; Sun, Fu; Cai, Yong-song; Wu, Wen-tao; Ye, Mao-lin; Ma, Jiang-tao; Xu, Bang-bang; Zhang, Yin-gang

2016-01-01

The aims of this systematic review were to study the analgesic effect of real acupuncture and to explore whether sham acupuncture (SA) type is related to the estimated effect of real acupuncture for musculoskeletal pain. Five databases were searched. The outcome was pain or disability immediately (≤1 week) following an intervention. Standardized mean differences (SMDs) with 95% confidence intervals were calculated. Meta-regression was used to explore possible sources of heterogeneity. Sixty-three studies (6382 individuals) were included. Eight condition types were included. The pooled effect size was moderate for pain relief (59 trials, 4980 individuals, SMD −0.61, 95% CI −0.76 to −0.47; P < 0.001) and large for disability improvement (31 trials, 4876 individuals, −0.77, −1.05 to −0.49; P < 0.001). In a univariate meta-regression model, sham needle location and/or depth could explain most or all heterogeneities for some conditions (e.g., shoulder pain, low back pain, osteoarthritis, myofascial pain, and fibromyalgia); however, the interactions between subgroups via these covariates were not significant (P < 0.05). Our review provided low-quality evidence that real acupuncture has a moderate effect (approximate 12-point reduction on the 100-mm visual analogue scale) on musculoskeletal pain. SA type did not appear to be related to the estimated effect of real acupuncture. PMID:27471137

Subjective study of preferred listening conditions in Italian Catholic churches

NASA Astrophysics Data System (ADS)

Martellotta, Francesco

2008-10-01

The paper describes the results of research aimed at investigating the preferred subjective listening conditions inside churches. The effect of different musical motifs (spanning Gregorian chants to symphonic music) was investigated and regression analysis was performed in order to point out the relationship between subjective ratings and acoustical parameters. In order to present realistic listening conditions to the subjects a small subset of nine churches was selected among a larger set of acoustic data collected in several Italian churches during a widespread on-site survey. The subset represented different architectural styles and shapes, and was characterized by average listening conditions. For each church a single source-receiver combination with fixed relative positions was chosen. Measured binaural impulse responses were cross-talk cancelled and then convolved with five anechoic motifs. Paired comparisons were finally performed, asking a trained panel of subjects their preference. Factor analysis pointed out a substantially common underlying pattern characterizing subjective responses. The results show that preferred listening conditions vary as a function of the musical motif, depending on early decay time for choral music and on a combination of initial time delay and lateral energy for instrumental music.

[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).

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command.

PubMed

Daza, Eric J; Hudgens, Michael G; Herring, Amy H

Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241-258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.

Occupational exposures and non-Hodgkin's lymphoma: Canadian case-control study.

PubMed

Karunanayake, Chandima P; McDuffie, Helen H; Dosman, James A; Spinelli, John J; Pahwa, Punam

2008-08-07

The objective was to study the association between Non-Hodgkin's Lymphoma (NHL) and occupational exposures related to long held occupations among males in six provinces of Canada. A population based case-control study was conducted from 1991 to 1994. Males with newly diagnosed NHL (ICD-10) were stratified by province of residence and age group. A total of 513 incident cases and 1506 population based controls were included in the analysis. Conditional logistic regression was conducted to fit statistical models. Based on conditional logistic regression modeling, the following factors independently increased the risk of NHL: farmer and machinist as long held occupations; constant exposure to diesel exhaust fumes; constant exposure to ionizing radiation (radium); and personal history of another cancer. Men who had worked for 20 years or more as farmer and machinist were the most likely to develop NHL. An increased risk of developing NHL is associated with the following: long held occupations of faer and machinist; exposure to diesel fumes; and exposure to ionizing radiation (radium). The risk of NHL increased with the duration of employment as a farmer or machinist.

Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command

PubMed Central

Hudgens, Michael G.; Herring, Amy H.

2017-01-01

Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study. PMID:29755297

Conditional Monte Carlo randomization tests for regression models.

PubMed

Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

2014-08-15

We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

Machine vision and appearance based learning

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-03-01

Smart algorithms are used in Machine vision to organize or extract high-level information from the available data. The resulted high-level understanding the content of images received from certain visual sensing system and belonged to an appearance space can be only a key first step in solving various specific tasks such as mobile robot navigation in uncertain environments, road detection in autonomous driving systems, etc. Appearance-based learning has become very popular in the field of machine vision. In general, the appearance of a scene is a function of the scene content, the lighting conditions, and the camera position. Mobile robots localization problem in machine learning framework via appearance space analysis is considered. This problem is reduced to certain regression on an appearance manifold problem, and newly regression on manifolds methods are used for its solution.

Creep-Rupture Data Analysis - Engineering Application of Regression Techniques. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Rummler, D. R.

1976-01-01

The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions1[W][OA

PubMed Central

Sulpice, Ronan; Nikoloski, Zoran; Tschoep, Hendrik; Antonio, Carla; Kleessen, Sabrina; Larhlimi, Abdelhalim; Selbig, Joachim; Ishihara, Hirofumi; Gibon, Yves; Fernie, Alisdair R.; Stitt, Mark

2013-01-01

Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27–0.58 and 0.21–0.51 and P values in the range of <0.001–<0.13 and <0.001–<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks. PMID:23515278

Speech-language pathologist job satisfaction in school versus medical settings.

PubMed

Kalkhoff, Nicole L; Collins, Dana R

2012-04-01

The goal of this study was to determine if job satisfaction differs between speech-language pathologists (SLPs) working in school settings and SLPs working in medical settings. The Job Satisfaction Survey (JSS) by Spector (1997) was sent via electronic mail to 250 SLPs in each of the 2 settings. Job satisfaction scores were computed from subscale category ratings and were compared between the 2 settings. Subscale category ratings for pay, promotion, supervision, benefits, contingent rewards, operating conditions, coworkers, nature of work, and communication were analyzed for differences between and within settings. Age, caseload size, and years-at-position were analyzed by linear regression to determine whether these factors might predict SLPs' job satisfaction. The survey had a response rate of 19.6% (N = 98 participants). Although SLPs in both settings were generally satisfied with their jobs, SLPs in medical settings had significantly higher total job satisfaction scores. Respondents from both settings had similar satisfaction ratings for subscale categories, with nature of work receiving the highest rating and operating conditions and promotion the lowest. Results of the linear regression analysis for age, caseload size, and years-at-position were not significant. Further research should evaluate important aspects of job satisfaction in both settings, especially nature of work operating conditions, and promotion.

Do acid volatile sulfides (AVS) influence the accumulation of sediment-bound metals to benthic invertebrates under natural field conditions?

PubMed

De Jonge, Maarten; Dreesen, Freja; De Paepe, Josefina; Blust, Ronny; Bervoets, Lieven

2009-06-15

The present study evaluates the influence of acid volatile sulfides (AVS) on accumulation of sediment-bound metals in benthic invertebrates under natural field conditions. Natural sediments, pore water, surface water, and two species of widespread benthic invertebrates (Chironomus gr. thummi and Tubifex tubifex) were collected from 17 historical polluted Flemish lowland rivers and measured for metal concentrations. Different sediment characteristics were determined (AVS, organic matter, clay content) and multiple regression was used to study their relationship with accumulated metals in the invertebrates. Physical and chemical analysis of the field samples indicated low metal concentrations in the water and pore water, but very high metal concentrations in the sediment and the invertebrates, especially for Pb (5.99 micromol/ g). In general, metal accumulation in chironomids and tubificid worms was most strongly correlated with total metal concentrations in the sediment and sediment metal concentrations normalized for organic matter and clay content. Following the results of the linear regression model, AVS did not turn out to be a significant variable in describing variation in metal accumulation. Our study clearly demonstrates that, in addition to the results gained from experiments under lab conditions, benthic invertebrates can accumulate metals from unspiked field sediments even when there's an excess of AVS.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Causes of death and competing risk analysis of the associated factors for non-small cell lung cancer using the Surveillance, Epidemiology, and End Results database.

PubMed

Wei, Shenhai; Tian, Jintao; Song, Xiaoping; Wu, Bingqun; Liu, Limin

2018-01-01

To investigate the probability of death (POD) from any causes by time after diagnosis of non-small cell lung cancer (NSCLC) and the factors associated with survival for NSCLC patients. A total of 202,914 patients with NSCLC from 2004 to 2013 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The overall survival (OS) and lung cancer-specific survival (LCSS) were calculated and POD from any causes at different time periods after diagnosis was explored. The predictive factors for OS, LCSS and survival from non-lung cancer deaths were investigated using multivariate analysis with Cox proportional hazards regression and competing risk regression analysis. The 5- and 10-year OS were 20.4% and 11.5%, accordingly that for LCSS were 25.5% and 18.4%, respectively. Lung cancer contributed 88.3% (n = 128,402) of the deaths. The POD from lung cancer decreased with time after diagnosis. In multivariate analysis, advanced age and advanced stage of NSCLC were associated with decreased OS and LCSS. Comparing to no surgery, any kind of resection conferred lower risk of death from lung cancer and higher risk of dying from non-lung cancer conditions except lobectomy or bilobectomy, which was associated with lower risk of death from both lung cancer and non-lung cancer conditions. Most of the patients with NSCLC died from lung cancer. Rational surveillance and treatment policies should be made for them. Early stage and lobectomy or bilobectomy were associated with improved OS and LCSS. It is reasonable to focus on early detection and optimal surgical treatment for NSCLC.

Serious conditions for ED elderly fall patients: a secondary analysis of the Basel Non-Specific Complaints study.

PubMed

Liu, Shan W; Sri-On, Jiraporn; Tirrell, Gregory Philip; Nickel, Christian; Bingisser, Roland

2016-08-01

Falls among older adults are a public health problem and are multifactorial. We sought to determine whether falls predict more serious conditions in older adult patients presenting to the emergency department (ED) with a "nonspecific complaint" (NSC). A secondary objective was to examine what factors predicted serious conditions among older adult patients with a fall. This study was a secondary analysis of a prospective delayed-type cross-sectional diagnostic study that included a 30-day follow-up. We included patients 65 years and older who presented to the ED from May 2007 and July 2011 with a NSC and had an Emergency Severity Index score of 2 or 3. We then compared the serious conditions among older adults who presented to the ED with a fall with those who did not fall in a cohort of patients with NSC. We had 1111 patients enrolled in our study; 518 (47%) of them had fallen. We found that 310 (60%) of elderly fall patients vs 349 (59%) of nonfall patients had a 30-day serious condition (P=.74). In multiple logistic regression analysis, falls did not predict serious conditions or 30-day mortality among all NSC patients. Among fall patients, male sex, diuretic use, and generalized weakness predicted serious conditions. Fall patients share many features with nonfall NSC patient. However, falls did not increase the risk of serious conditions. Falls in the elderly could be considered under the broader entity of NSC. Copyright © 2016 Elsevier Inc. All rights reserved.

Technical Assistance and Changes in Nutrition and Physical Activity Practices in the National Early Care and Education Learning Collaboratives Project, 2015–2016

PubMed Central

Smith, Teresa M.; Estabrooks, Paul A.; Rasmussen, Cristy Geno; Blaser, Casey; Yaroch, Amy L

2018-01-01

Purpose and Objectives The National Early Care and Education Learning Collaboratives Project (ECELC) aims to improve best practices in early care and education (ECE) programs in topic areas of the Nutrition and Physical Activity Self-Assessment in Child Care (NAP SACC). Technical assistance is a component of the ECELC, yet its effect on outcomes is unclear. Beyond dose and duration of technical assistance, limited research exists on characteristics of technical assistance that contribute to outcomes. The objective of this study was to identify and describe technical assistance characteristics and explore associations with NAP SACC outcomes. Intervention Approach We collected data from 10 collaboratives comprising 84 ECE programs in 2 states in 2015–2016. The objective of technical assistance was to support programs in improving best practices. Technical assistance was provided to programs via on-site, telephone, or email and was tailored to program needs. Evaluation Methods We used a mixed-methods design to examine associations between technical assistance and NAP SACC outcomes. We used multiple regression analysis to assess quantitative data and qualitative comparative analysis to determine necessary and sufficient technical assistance conditions supporting NAP SACC outcomes. We also conducted a document review to describe technical assistance that referred conditions identified by the qualitative comparative analysis. Results Regression analyses detected an inverse relationship between changes in NAP SACC scores and hours of technical assistance. No clear pattern emerged in the qualitative comparative analysis, leaving no necessary and sufficient conditions. However, the qualitative comparative analysis identified feedback as a potentially important component of technical assistance, whereas resource sharing and frequent email were characteristics that seemed to reduce the likelihood of improved outcomes. Email and resource sharing were considered primarily general information rather than tailored technical assistance. Implications for Public Health Technical assistance may be used in programs and made adaptable to program needs. The inclusion and evaluation of technical assistance, especially tailored approaches, is warranted for environmental interventions, including ECE settings. PMID:29704371

Evaluation of risk factors for perforated peptic ulcer.

PubMed

Yamamoto, Kazuki; Takahashi, Osamu; Arioka, Hiroko; Kobayashi, Daiki

2018-02-15

The aim of this study was to evaluate the prediction factors for perforated peptic ulcer (PPU). At St. Luke's International Hospital in Tokyo, Japan, a case control study was performed between August 2004 and March 2016. All patients diagnosed with PPU were included. As control subjects, patients with age, sex and date of CT scan corresponding to those of the PPU subjects were included in the study at a proportion of 2 controls for every PPU subject. All data such as past medical histories, physical findings, and laboratory data were collected through chart reviews. Univariate analyses and multivariate analyses with logistic regression were conducted, and receiver operating characteristic curves (ROCs) were calculated to show validity. Sensitivity analyses were performed to confirm results using a stepwise method and conditional logistic regression. A total of 408 patients were included in this study; 136 were a group of patients with PPU, and 272 were a control group. Univariate analysis showed statistical significance in many categories. Four different models of multivariate analyses were conducted, and significant differences were found for muscular defense and a history of peptic ulcer disease (PUD) in all models. The conditional forced-entry analysis of muscular defense showed an odds ratio (OR) of 23.8 (95% confidence interval [CI]: 5.70-100.0), and the analysis of PUD history showed an OR of 6.40 (95% CI: 1.13-36.2). The sensitivity analysis showed consistent results, with an OR of 23.8-366.2 for muscular defense and an OR of 3.67-7.81 for PUD history. The area under the curve (AUC) of all models was high enough to confirm the results. However, anticoagulants, known risk factors for PUD, did not increase the risk for PPU in our study. The conditional forced-entry analysis of anticoagulant use showed an OR of 0.85 (95% CI: 0.03-22.3). The evaluation of prediction factors and development of a prediction rule for PPU may help our decision making in performing a CT scan for patients with acute abdominal pain.

Multiple Regression with Varying Levels of Correlation among Predictors: Monte Carlo Sampling from Normal and Non-Normal Populations.

ERIC Educational Resources Information Center

Vasu, Ellen Storey

1978-01-01

The effects of the violation of the assumption of normality in the conditional distributions of the dependent variable, coupled with the condition of multicollinearity upon the outcome of testing the hypothesis that the regression coefficient equals zero, are investigated via a Monte Carlo study. (Author/JKS)

Assessment of the first radiances received from the VSSR Atmospheric Sounder (VAS) instrument

NASA Technical Reports Server (NTRS)

Chesters, D.; Uccellini, L. W.; Montgomery, H.; Mostek, A.; Robinson, W.

1981-01-01

The first orderly, calibrated radiances from the VAS-D instrument on the GOES-4 satellite are examined for: image quality, radiometric precision, radiation transfer verification at clear air radiosonde sites, regression retrieval accuracy, and mesoscale analysis features. Postlaunch problems involving calibration and data processing irregularities of scientific or operational significance are included. The radiances provide good visual and relative radiometric data for empirically conditioned retrievals of mesoscale temperature and moisture fields in clear air.

Associations between self-reported working conditions and registered health and safety results.

PubMed

Høivik, Dordi; Baste, Valborg; Brandsdal, Einar; Moen, Bente E

2007-02-01

To investigate the association between self-reported working conditions and registered health and safety results in a petroleum company in Norway. We analyzed data from company surveys of working and organizational conditions in 2003 and 2004 and data from the company's files of sickness absence, personal injuries, serious incidents, and undesirable incidents in 2003 and 2004 as well as personal injuries from 2000 to 2004 using Pearson's correlation analysis and multiple linear regression analyses. Good perception of confidence in management in 2003 and 2004 was significantly negatively correlated with the number of personal injuries from 2000 to 2004. Management style and trust in the manager are important factors for predicting personal injuries. The company's working and organizational survey might be used as an indicator for injury risk.

Do positive schizotypal symptoms predict false perceptual experiences in nonclinical populations?

PubMed

Tsakanikos, Elias; Reed, Phil

2005-12-01

We examined whether positive schizotypy (i.e., reports of hallucinatory and delusional-like experiences) in nonclinical participants could predict false perceptual experiences during detection of fast-moving words beyond a possible response bias. The participants (N = 160) were assigned to one of two conditions: they were asked either to make presence/absence judgments (loose criterion) or to read aloud every detected word (strict criterion). Regression analysis showed that high levels of positive schizotypy predicted false alarms in the loose condition and false perceptions of words in the strict condition. The obtained effects were independent of detection accuracy, task order, impulsivity, and social desirability. We discuss the results in the context of information processing biases linked to the positive symptomatology of schizophrenia. Clinical and theoretical implications are also considered.

Comparison of DMSP SSM/I and Landsat 7 ETM+ Sea Ice Concentrations During Summer Melt

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

As part of NASA's EOS Aqua sea ice validation program for the Advanced Microwave Scanning Radiometer (AMSR-E), Landsat 7 Enhanced Thematic Mapper (ETM+) images were acquired to develop a sea ice concentration data set with which to validate AMSR-E sea ice concentration retrievals. The standard AMSR-E Arctic sea ice concentration product will be obtained with the enhanced NASA Team (NT2) algorithm. The goal of this study is to assess the accuracy to which the NT2 algorithm, using DMSP Special Sensor Microwave Imager radiances, retrieves sea ice concentrations under summer melt conditions. Melt ponds are currently the largest source of error in the determination of Arctic sea ice concentrations with satellite passive microwave sensors. To accomplish this goal, Landsat 7 ETM+ images of Baffin Bay were acquired under clear sky conditions on the 26th and 27th of June 2000 and used to generate high-resolution sea ice concentration maps with which to compare the NT2 retrievals. Based on a linear regression analysis of 116 25-km samples, we find that overall the NT2 retrievals agree well with the Landsat concentrations. The regression analysis yields a correlation coefficient of 0.98. In areas of high melt ponding, the NT2 retrievals underestimate the sea ice concentrations by about 12% compared to the Landsat values.

Incidence and risk factors of acute kidney injury after esophageal cancer surgery: A nested case-control study.

PubMed

Wang, Wen; Wang, Tong; Feng, Xiaoshuang; Sun, Li

2017-03-01

Acute kidney injury (AKI) has been increasingly recognized as a common and serious postoperative complication. Although many studies have been conducted to investigate postoperative AKI after thoracic surgery, little is known about AKI after esophageal surgery. Thus, we conducted this study to determine the incidence and identify risk factors of postoperative AKI after esophageal cancer surgery. A retrospective nested case-control study of patients undergoing elective esophageal cancer surgery between July 2013 and July 2016 in a single tertiary specialized cancer hospital was performed. The primary outcome was development of AKI. Conditional logistic regression analysis was performed to identify independent risk factors for AKI. Of 2094 patients, 51 (2.4%) developed postoperative AKI after esophageal cancer surgery. In multivariate conditional logistic regression analysis, four risk factors for AKI after esophageal surgery for cancer were identified: preoperative serum creatinine level (OR 1.040; 95% CI 1.012-1.069), duration of surgery (OR 1.009; 95% CI 1.005-1.014), smoking history (OR 3.029; 95% CI 1.092-8.399) and hypertension (OR 6.422; 95% CI 2.736-15.070). Postoperative AKI occurred in 2.4% of patients after esophageal surgery for cancer. Preoperative serum creatinine level, duration of surgery, smoking history and hypertension were independent risk factors for postoperative AKI. Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Prevalence of hyperphagia in Alzheimer's disease: a meta-analysis.

PubMed

Shea, Yat-Fung; Lee, Shui-Ching; Chu, Leung-Wing

2018-02-06

Unlike other behavioural and psychological symptoms of dementia, hyperphagia is less recognized among patients with Alzheimer's disease (AD). The prevalence of hyperphagia varies among studies, but there has been no systematic review or meta-analysis. An extensive search on the literature on hyperphagia in AD published between 1 January 1980 and 30 October 2017 was conducted. Data on the prevalence were retrieved. Meta-analysis with a random effect model was performed to determine the pooled estimate of prevalence. Meta-regression analysis was performed based on study characteristics, population demographics, or condition information. Results from 20 studies were extracted. Twenty-six reported cases of hyperphagia were identified. The mean age of onset was 70.7 ± 8.9 years, with a male predominance (68.4%). Hyperphagia occurred in all stages of AD. Only eight studies reported the prevalence of hyperphagia. Meta-analysis showed a pooled prevalence of hyperphagia of 18.6%. Publication bias may have been present. Meta-regression showed that ethnicity accounted for the variance among studies (coefficient: -1.247 (95% confidence interval: -1.978 to -0.516), R 2 analogue: 0.77, P < 0.001). Hyperphagia occurs in all stages of AD. In this meta-analysis of eight published studies, the prevalence of hyperphagia was 18.6%. In view of the possible publication bias, a large-scale study on hyperphagia is recommended in the future. © 2018 Japanese Psychogeriatric Society.

Relating vegetation condition to grazing management systems in the central Keiskamma catchment, Eastern Cape Province, South Africa

NASA Astrophysics Data System (ADS)

Kakembo, Vincent; Ndou, Naledzani

2017-04-01

An investigation of the temporal changes in vegetation condition across the communal villages of the central Keiskamma catchment, Eastern Cape Province, in relation to local grazing management systems was conducted. Landsat TM images of 1984 and 1999, in conjunction with SPOT imagery of 2011 were used to assess the spatial trends in vegetation. Information regarding the functionality of local grazing management structures was obtained through structured interviews. Vegetation condition was related to grazing management systems using the logistic regression in Idrisi Selva remote sensing software. Analysis of vegetation condition trends revealed a consistent deterioration of vegetation condition in villages with weak grazing management systems. A statistically significant correlation between vegetation condition and grazing management systems was identified. High levels of vegetation degradation were associated with villages that did not adhere to sound grazing management practices. The introduction of another layer governance in the form of elected municipal committees weakened traditional village management structures. Strengthening traditional management committees should be the point of departure for vegetation restoration.

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

PubMed

Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Respite service use among caregivers of older people: comparative analysis of family dementia caregivers with musculoskeletal and circulatory system disorder caregivers.

PubMed

Vecchio, Nerina; Fitzgerald, Janna A; Radford, Katrina; Kurrle, Susan

2018-01-01

To identify the main drivers of the use of respite services and the need for respite services among caregivers of people experiencing dementia relative to family caregivers of people with other health conditions. Based on nationally representative secondary data regression analysis was used to test the association between selected health conditions and the utilisation of and need for respite services. For a person living with dementia the odds of using respite care are higher than for a person with either a musculoskeletal or circulatory condition. Family caregivers of people living with dementia report the odds of the need for more respite as 5.3 times higher than for family caregivers of people with musculoskeletal conditions and 7.7 times higher than for family caregivers of people with circulatory conditions. The main reason for never using respite services is largely driven by the type of health condition, age of care recipient, existence of a spouse, and level of disability. Respite services that cater to the specific needs of families experiencing dementia at home should become a higher priority within the aged care sector. Alternative models of respite care that focus on prevention and early intervention would be cost effective.

Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis

NASA Astrophysics Data System (ADS)

Sakata, Ayaka; Xu, Yingying

2018-03-01

We analyse a linear regression problem with nonconvex regularization called smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis for Gaussian random data. We propose an approximate message passing (AMP) algorithm considering nonconvex regularization, namely SCAD-AMP, and analytically show that the stability condition corresponds to the de Almeida-Thouless condition in spin glass literature. Through asymptotic analysis, we show the correspondence between the density evolution of SCAD-AMP and the replica symmetric (RS) solution. Numerical experiments confirm that for a sufficiently large system size, SCAD-AMP achieves the optimal performance predicted by the replica method. Through replica analysis, a phase transition between replica symmetric and replica symmetry breaking (RSB) region is found in the parameter space of SCAD. The appearance of the RS region for a nonconvex penalty is a significant advantage that indicates the region of smooth landscape of the optimization problem. Furthermore, we analytically show that the statistical representation performance of the SCAD penalty is better than that of \

Quantitative Analysis of Driving Factors of Grassland Degradation: A Case Study in Xilin River Basin, Inner Mongolia

PubMed Central

Xie, Yichun; Sha, Zongyao

2012-01-01

Current literature suggests that grassland degradation occurs in areas with poor soil conditions or noticeable environmental changes and is often a result of overgrazing or human disturbances. However, these views are questioned in our analyses. Based on the analysis of satellite vegetation maps from 1984, 1998, and 2004 for the Xilin River Basin, Inner Mongolia, China, and binary logistic regression (BLR) analysis, we observe the following: (1) grassland degradation is positively correlated with the growth density of climax communities; (2) our findings do not support a common notion that a decrease of biological productivity is a direct indicator of grassland degradation; (3) a causal relationship between grazing intensity and grassland degradation was not found; (4) degradation severity increased steadily towards roads but showed different trends near human settlements. This study found complex relationships between vegetation degradation and various microhabitat conditions, for example, elevation, slope, aspect, and proximity to water. PMID:22619613

Lean production tools and decision latitude enable conditions for innovative learning in organizations: a multilevel analysis.

PubMed

Fagerlind Ståhl, Anna-Carin; Gustavsson, Maria; Karlsson, Nadine; Johansson, Gun; Ekberg, Kerstin

2015-03-01

The effect of lean production on conditions for learning is debated. This study aimed to investigate how tools inspired by lean production (standardization, resource reduction, visual monitoring, housekeeping, value flow analysis) were associated with an innovative learning climate and with collective dispersion of ideas in organizations, and whether decision latitude contributed to these associations. A questionnaire was sent out to employees in public, private, production and service organizations (n = 4442). Multilevel linear regression analyses were used. Use of lean tools and decision latitude were positively associated with an innovative learning climate and collective dispersion of ideas. A low degree of decision latitude was a modifier in the association to collective dispersion of ideas. Lean tools can enable shared understanding and collective spreading of ideas, needed for the development of work processes, especially when decision latitude is low. Value flow analysis played a pivotal role in the associations. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Low-flow, base-flow, and mean-flow regression equations for Pennsylvania streams

USGS Publications Warehouse

Stuckey, Marla H.

2006-01-01

Low-flow, base-flow, and mean-flow characteristics are an important part of assessing water resources in a watershed. These streamflow characteristics can be used by watershed planners and regulators to determine water availability, water-use allocations, assimilative capacities of streams, and aquatic-habitat needs. Streamflow characteristics are commonly predicted by use of regression equations when a nearby streamflow-gaging station is not available. Regression equations for predicting low-flow, base-flow, and mean-flow characteristics for Pennsylvania streams were developed from data collected at 293 continuous- and partial-record streamflow-gaging stations with flow unaffected by upstream regulation, diversion, or mining. Continuous-record stations used in the regression analysis had 9 years or more of data, and partial-record stations used had seven or more measurements collected during base-flow conditions. The state was divided into five low-flow regions and regional regression equations were developed for the 7-day, 10-year; 7-day, 2-year; 30-day, 10-year; 30-day, 2-year; and 90-day, 10-year low flows using generalized least-squares regression. Statewide regression equations were developed for the 10-year, 25-year, and 50-year base flows using generalized least-squares regression. Statewide regression equations were developed for harmonic mean and mean annual flow using weighted least-squares regression. Basin characteristics found to be significant explanatory variables at the 95-percent confidence level for one or more regression equations were drainage area, basin slope, thickness of soil, stream density, mean annual precipitation, mean elevation, and the percentage of glaciation, carbonate bedrock, forested area, and urban area within a basin. Standard errors of prediction ranged from 33 to 66 percent for the n-day, T-year low flows; 21 to 23 percent for the base flows; and 12 to 38 percent for the mean annual flow and harmonic mean, respectively. The regression equations are not valid in watersheds with upstream regulation, diversions, or mining activities. Watersheds with karst features need close examination as to the applicability of the regression-equation results.

Monthly streamflow forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2017-04-01

Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an important water way and water resource in Western Europe. The present study investigates the predictability of monthly mean streamflow at lead times of zero, one, and two months with the focus on potential benefits by the integration of seasonal climate predictions. Specifically, we use seasonal predictions of precipitation and surface air temperature released by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression analysis. In order to disentangle forecast uncertainty, the 'Reverse Ensemble Streamflow Prediction' framework is adapted here to the context of regression: By using appropriate subsets of predictors the regression model is constrained to either the initial conditions, the meteorological forcing, or both. An operational application is mimicked by equipping the model with the seasonal climate predictions provided by ECMWF. Finally, to mitigate the spatial aggregation of the meteorological fields the model is also applied at the subcatchment scale, and the resulting predictions are combined afterwards. The hindcast experiment is carried out for the period 1982-2011 in cross validation mode at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero lead time the integration of seasonal climate predictions decreases the mean absolute error by 5 to 10 percentage compared to forecasts which are solely based on initial conditions. This reduction most likely is induced by the seasonal prediction of precipitation and not air temperature. The study is completed by bench marking the regression model with runoff simulations from ECMWFs seasonal forecast system. By simply using basin averages followed by a linear bias correction, these runoff simulations translate well to monthly streamflow. Though the regression model is only slightly outperformed, we argue that runoff out of the land surface component of seasonal climate forecasting systems is an interesting option when it comes to seasonal streamflow forecasting in large river basins.

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

PubMed

Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

Impact of wearing fixed orthodontic appliances on quality of life among adolescents: Case-control study.

PubMed

Costa, Andréa A; Serra-Negra, Júnia M; Bendo, Cristiane B; Pordeus, Isabela A; Paiva, Saul M

2016-01-01

To investigate the impact of wearing a fixed orthodontic appliance on oral health-related quality of life (OHRQoL) among adolescents. A case-control study (1 ∶ 2) was carried out with a population-based randomized sample of 327 adolescents aged 11 to 14 years enrolled at public and private schools in the City of Brumadinho, southeast of Brazil. The case group (n  =  109) was made up of adolescents with a high negative impact on OHRQoL, and the control group (n  =  218) was made up of adolescents with a low negative impact. The outcome variable was the impact on OHRQoL measured by the Brazilian version of the Child Perceptions Questionnaire (CPQ 11-14) - Impact Short Form (ISF:16). The main independent variable was wearing fixed orthodontic appliances. Malocclusion and the type of school were identified as possible confounding variables. Bivariate and multiple conditional logistic regressions were employed in the statistical analysis. A multiple conditional logistic regression model demonstrated that adolescents wearing fixed orthodontic appliances had a 4.88-fold greater chance of presenting high negative impact on OHRQoL (95% CI: 2.93-8.13; P < .001) than those who did not wear fixed orthodontic appliances. A bivariate conditional logistic regression demonstrated that malocclusion was significantly associated with OHRQoL (P  =  .017), whereas no statistically significant association was found between the type of school and OHRQoL (P  =  .108). Adolescents who wore fixed orthodontic appliances had a greater chance of reporting a negative impact on OHRQoL than those who did not wear such appliances.

Item parameters dissociate between expectation formats: a regression analysis of time-frequency decomposed EEG data

PubMed Central

Monsalve, Irene F.; Pérez, Alejandro; Molinaro, Nicola

2014-01-01

During language comprehension, semantic contextual information is used to generate expectations about upcoming items. This has been commonly studied through the N400 event-related potential (ERP), as a measure of facilitated lexical retrieval. However, the associative relationships in multi-word expressions (MWE) may enable the generation of a categorical expectation, leading to lexical retrieval before target word onset. Processing of the target word would thus reflect a target-identification mechanism, possibly indexed by a P3 ERP component. However, given their time overlap (200–500 ms post-stimulus onset), differentiating between N400/P3 ERP responses (averaged over multiple linguistically variable trials) is problematic. In the present study, we analyzed EEG data from a previous experiment, which compared ERP responses to highly expected words that were placed either in a MWE or a regular non-fixed compositional context, and to low predictability controls. We focused on oscillatory dynamics and regression analyses, in order to dissociate between the two contexts by modeling the electrophysiological response as a function of item-level parameters. A significant interaction between word position and condition was found in the regression model for power in a theta range (~7–9 Hz), providing evidence for the presence of qualitative differences between conditions. Power levels within this band were lower for MWE than compositional contexts when the target word appeared later on in the sentence, confirming that in the former lexical retrieval would have taken place before word onset. On the other hand, gamma-power (~50–70 Hz) was also modulated by predictability of the item in all conditions, which is interpreted as an index of a similar “matching” sub-step for both types of contexts, binding an expected representation and the external input. PMID:25161630

The impact of intrinsic and extrinsic factors on the job satisfaction of dentists.

PubMed

Goetz, K; Campbell, S M; Broge, B; Dörfer, C E; Brodowski, M; Szecsenyi, J

2012-10-01

The Two-Factor Theory of job satisfaction distinguishes between intrinsic-motivation (i.e. recognition, responsibility) and extrinsic-hygiene (i.e. job security, salary, working conditions) factors. The presence of intrinsic-motivation facilitates higher satisfaction and performance, whereas the absences of extrinsic factors help mitigate against dissatisfaction. The consideration of these factors and their impact on dentists' job satisfaction is essential for the recruitment and retention of dentists. The objective of the study is to assess the level of job satisfaction of German dentists and the factors that are associated with it. This cross-sectional study was based on a job satisfaction survey. Data were collected from 147 dentists working in 106 dental practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Organizational characteristics were measured with two items. Linear regression analyses were performed in which each of the nine items of the job satisfaction scale (excluding overall satisfaction) were handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction as the dependent outcome variable, the nine items of job satisfaction and the two items of organizational characteristics controlled for age and gender as predictors. The response rate was 95.0%. Dentists were satisfied with 'freedom of working method' and mostly dissatisfied with their 'income'. Both variables are extrinsic factors. The regression analyses identified five items that were significantly associated with each item of the job satisfaction scale: 'age', 'mean weekly working time', 'period in the practice', 'number of dentist's assistant' and 'working atmosphere'. Within the stepwise linear regression analysis the intrinsic factor 'opportunity to use abilities' (β = 0.687) showed the highest score of explained variance (R(2) = 0.468) regarding overall job satisfaction. With respect to the Two-Factor Theory of job satisfaction both components, intrinsic and extrinsic, are essential for dentists but the presence of intrinsic motivating factors like the opportunity to use abilities has most positive impact on job satisfaction. The findings of this study will be helpful for further activities to improve the working conditions of dentists and to ensure quality of care. © 2012 John Wiley & Sons A/S.

Development of a User Interface for a Regression Analysis Software Tool

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

Regression Analysis and the Sociological Imagination

ERIC Educational Resources Information Center

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly; Gay, Robert; Stachowiak, Susan

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter. In order to increase overall robustness, the vehicle also has an alternate method of triggering the drogue parachute deployment based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this velocity-based trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers excellent performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

GWAS with longitudinal phenotypes: performance of approximate procedures

PubMed Central

Sikorska, Karolina; Montazeri, Nahid Mostafavi; Uitterlinden, André; Rivadeneira, Fernando; Eilers, Paul HC; Lesaffre, Emmanuel

2015-01-01

Analysis of genome-wide association studies with longitudinal data using standard procedures, such as linear mixed model (LMM) fitting, leads to discouragingly long computation times. There is a need to speed up the computations significantly. In our previous work (Sikorska et al: Fast linear mixed model computations for genome-wide association studies with longitudinal data. Stat Med 2012; 32.1: 165–180), we proposed the conditional two-step (CTS) approach as a fast method providing an approximation to the P-value for the longitudinal single-nucleotide polymorphism (SNP) effect. In the first step a reduced conditional LMM is fit, omitting all the SNP terms. In the second step, the estimated random slopes are regressed on SNPs. The CTS has been applied to the bone mineral density data from the Rotterdam Study and proved to work very well even in unbalanced situations. In another article (Sikorska et al: GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. BMC Bioinformatics 2013; 14: 166), we suggested semi-parallel computations, greatly speeding up fitting many linear regressions. Combining CTS with fast linear regression reduces the computation time from several weeks to a few minutes on a single computer. Here, we explore further the properties of the CTS both analytically and by simulations. We investigate the performance of our proposal in comparison with a related but different approach, the two-step procedure. It is analytically shown that for the balanced case, under mild assumptions, the P-value provided by the CTS is the same as from the LMM. For unbalanced data and in realistic situations, simulations show that the CTS method does not inflate the type I error rate and implies only a minimal loss of power. PMID:25712081

The effects of climate change on harp seals (Pagophilus groenlandicus).

PubMed

Johnston, David W; Bowers, Matthew T; Friedlaender, Ari S; Lavigne, David M

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data.

The Effects of Climate Change on Harp Seals (Pagophilus groenlandicus)

PubMed Central

Johnston, David W.; Bowers, Matthew T.; Friedlaender, Ari S.; Lavigne, David M.

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data. PMID:22238591

Activity-Limiting Musculoskeletal Conditions in US Veterans Compared to Non-Veterans: Results from the 2013 National Health Interview Survey

PubMed Central

Hinojosa, Ramon

2016-01-01

Past military service is associated with health outcomes, both positive and negative. In this study we use the 2013 National Health Interview Survey to examine the constellation of conditions referred to as musculoskeletal disorders (MSDs) for Veterans and non-veterans with health conditions that limit their daily activities. Multivariate logistic regression analysis reveal that Veterans are more likely to report MSDs like neck and back problems, fracture bone and joint problems as an activity limiting problem compared to non-veterans. The relationship between age and reports of activity limiting MSDs is moderated by Veteran status. Veterans in this sample report more activity limiting MSDs at younger ages compared to non-veterans and fewer MSDs at older ages. This research contributes to our understanding of potentially limiting health conditions at earlier ages for Veterans. PMID:28005905

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Prevalence of cognitive and functional impairment in a community sample in Ribeirão Preto, Brazil.

PubMed

Lopes, Marcos A; Hototian, Sergio R; Bustamante, Sonia E Z; Azevedo, Dionísio; Tatsch, Mariana; Bazzarella, Mário C; Litvoc, Júlio; Bottino, Cássio M C

2007-08-01

This study aimed at estimating the prevalence of cognitive and functional impairment (CFI) in a community sample in Ribeirão Preto, Brazil, evaluating its distribution in relation to various socio-demographic and clinical factors. The population was a representative sample aged 60 and older, from three different socio-economic classes. Cluster sampling was applied. Instruments used to select CFI (a syndromic category that does not exclude dementia): 'Mini Mental State Examination' (MMSE), 'Fuld Object Memory Evaluation' (FOME), 'Informant Questionnaire on Cognitive Decline in the Elderly' (IQCODE), 'Bayer Activities of Daily Living Scale' (B-ADL) and clinical interviews. The data obtained were submitted to bivariate and logistic regression analysis. A sample of 1.145 elderly persons was evaluated, with a mean age of 70.9 years (60-100; DP: 7.7); 63.4% were female, and 52.8% had up to 4 years of schooling. CFI prevalence was 18.9% (n = 217). Following logistic regression analysis, higher age, low education, stroke, epilepsy and depression were associated with CFI. Female sex, widowhood, low social class and head trauma were associated with CFI only on bivariate analysis. CFI prevalence results were similar to those found by studies in Brazil, Puerto Rico and Malaysia. Cognitive and functional impairment is a rather heterogeneous condition which may be associated with various clinical conditions found in the elderly population. Due to its high prevalence and association with higher mortality and disability rates, this clinical syndrome should receive more attention on public health intervention planning.

Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation

PubMed Central

Ma, Fangfang; Jazmin, Lara J.; Young, Jamey D.; Allen, Doug K.

2014-01-01

Improving plant productivity is an important aim for metabolic engineering. There are few comprehensive methods that quantitatively describe leaf metabolism, although such information would be valuable for increasing photosynthetic capacity, enhancing biomass production, and rerouting carbon flux toward desirable end products. Isotopically nonstationary metabolic flux analysis (INST-MFA) has been previously applied to map carbon fluxes in photoautotrophic bacteria, which involves model-based regression of transient 13C-labeling patterns of intracellular metabolites. However, experimental and computational difficulties have hindered its application to terrestrial plant systems. We performed in vivo isotopic labeling of Arabidopsis thaliana rosettes with 13CO2 and estimated fluxes throughout leaf photosynthetic metabolism by INST-MFA. Plants grown at 200 µmol m-2s−1 light were compared with plants acclimated for 9 d at an irradiance of 500 µmol⋅m−2⋅s−1. Approximately 1,400 independent mass isotopomer measurements obtained from analysis of 37 metabolite fragment ions were regressed to estimate 136 total fluxes (54 free fluxes) under each condition. The results provide a comprehensive description of changes in carbon partitioning and overall photosynthetic flux after long-term developmental acclimation of leaves to high light. Despite a doubling in the carboxylation rate, the photorespiratory flux increased from 17 to 28% of net CO2 assimilation with high-light acclimation (Vc/Vo: 3.5:1 vs. 2.3:1, respectively). This study highlights the potential of 13C INST-MFA to describe emergent flux phenotypes that respond to environmental conditions or plant physiology and cannot be obtained by other complementary approaches. PMID:25368168

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

PubMed

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Analysis of hyperspectral field radiometric data for monitoring nitrogen concentration in rice crops

NASA Astrophysics Data System (ADS)

Stroppiana, D.; Boschetti, M.; Confalonieri, R.; Bocchi, S.; Brivio, P. A.

2005-10-01

Monitoring crop conditions and assessing nutrition requirements is fundamental for implementing sustainable agriculture. Rational nitrogen fertilization is of particular importance in rice crops in order to guarantee high production levels while minimising the impact on the environment. In fact, the typical flooded condition of rice fields can be a significant source of greenhouse gasses. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies for a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field Northern Italy, in order to evaluate the potential of field radiometric measurements for the prediction of rice nitrogen concentration. The results indicate that rice reflectance is influenced by nitrogen supply at certain wavelengths although N concentration cannot be accurately predicted based on the reflectance measured at a given wavelength. Regression analysis highlighted that the visible region of the spectrum is most sensitive to plant nitrogen concentration when reflectance measures are combined into a spectral index. An automated procedure allowed the analysis of all the possible combinations into a Normalized Difference Index (NDI) of the narrow spectral bands derived by spectral resampling of field measurements. The derived index appeared to be least influenced by plant biomass and Leaf Area Index (LAI) providing a useful approach to detect rice nutritional status. The validation of the regressive model showed that the model is able to predict rice N concentration (R2=0.55 [p<0.01] RRMSE=29.4; modelling efficiency close to the optimum value).

Effect of Shift Work on Sleep, Health, and Quality of Life of Health-care Workers.

PubMed

Nena, Evangelia; Katsaouni, Maria; Steiropoulos, Paschalis; Theodorou, Evangelos; Constantinidis, Theodoros C; Tripsianis, Grigorios

2018-01-01

Shift work is associated with sleep disruption, impaired quality of life, and is a risk factor for several health conditions. Aim of this study was to investigate the impact of shift work on sleep and quality of life of health-care workers (HCW). Tertiary University hospital in Greece. Cross-sectional study. Included were HCW, working either in an irregular shift system or exclusively in morning shifts. All participants answered the WHO-5 Well-Being Index (WHO-5) and a questionnaire on demographics and medical history. Shift workers filled the Shift Work Disorders Screening Questionnaire (SWDSQ). Descriptive statistics, Student's t -test, one-way analysis of variance (ANOVA), Pearson's r correlation coefficient, and multivariate stepwise linear regression analysis were applied. Included were 312 employees (87.9% females), 194 working in irregular shift system and 118 in morning shifts. Most shift-workers (58.2%) were somehow or totally dissatisfied with their sleep quality. Regression analysis revealed the following independent determinants for sleep impairment: parenthood ( P < 0.001), age 36-45 years ( P < 0.001), >3 night shifts/week ( P < 0.001), work >5 years in an irregular shift system ( P < 0.001). Diabetes mellitus was the most common medical condition reported by shift workers ( P = 0.008). Comparison between the two groups revealed a significantly impairment in WHO-5 total score, as well as in 4 of 5 of its items ( P < 0.001). Shift-work impairs quality of life, whereas its duration and frequency, along with age and family status of employees can have adverse effects on sleep.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss.

PubMed

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. © 2016 S. Karger GmbH, Freiburg.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715

Gas occurrence property in shales of Tuha basin northwest china

NASA Astrophysics Data System (ADS)

Chen, Jinlong; Huang, Zhilong

2017-04-01

Pore of rock under formation condition must be fulfilled by gas, oil, or water, so the volume of water and gas is equation to porous volume in shale gas. The occurrences states of gas are free gas, solution gas, and absorbed gas. Field analysis is used to obtain total gas content by improved lost gas recover method. Free gas content acquired by pore proportion of gas, which use measured pore volume minus water and oil saturation, convert gas content of standard condition by state equation. Water saturation obtain from core water content, oil saturation obtain from extract carbohydrate. Solution gas need gas solubility in oil and water to calculate solution gas content in standard condition. Absorbed gas, introduce Absorbed Gas Saturation ɛ, which acquire from isothermal adsorption volume vs field analysis gas content in many basins of published paper, need isothermal adsorption and Absorbed Gas Saturation to obtain absorbed gas content. All of the data build connect with logging value by regression equation. The gas content is 0.92-1.53 m3/t from field analysis, evaluate gas content is 1.33 m3/t average, free gas proportion is about 47%, absorbed gas counter for 49%, and solution gas is average 4%.

A meta-analysis of sex differences in human brain structure.

PubMed

Ruigrok, Amber N V; Salimi-Khorshidi, Gholamreza; Lai, Meng-Chuan; Baron-Cohen, Simon; Lombardo, Michael V; Tait, Roger J; Suckling, John

2014-02-01

The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18-59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

NASA Astrophysics Data System (ADS)

Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

2013-06-01

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

Uncertainty and sensitivity analysis for two-phase flow in the vicinity of the repository in the 1996 performance assessment for the Waste Isolation Pilot Plant: Disturbed conditions

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

HELTON,JON CRAIG; BEAN,J.E.; ECONOMY,K.

2000-05-22

Uncertainty and sensitivity analysis results obtained in the 1996 performance assessment (PA) for the Waste Isolation Pilot Plant (WIPP) are presented for two-phase flow in the vicinity of the repository under disturbed conditions resulting from drilling intrusions. Techniques based on Latin hypercube sampling, examination of scatterplots, stepwise regression analysis, partial correlation analysis and rank transformations are used to investigate brine inflow, gas generation repository pressure, brine saturation and brine and gas outflow. Of the variables under study, repository pressure and brine flow from the repository to the Culebra Dolomite are potentially the most important in PA for the WIPP. Subsequentmore » to a drilling intrusion repository pressure was dominated by borehole permeability and generally below the level (i.e., 8 MPa) that could potentially produce spallings and direct brine releases. Brine flow from the repository to the Culebra Dolomite tended to be small or nonexistent with its occurrence and size also dominated by borehole permeability.« less

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Effect of substituents on prediction of TLC retention of tetra-dentate Schiff bases and their Copper(II) and Nickel(II) complexes.

PubMed

Stevanović, Nikola R; Perušković, Danica S; Gašić, Uroš M; Antunović, Vesna R; Lolić, Aleksandar Đ; Baošić, Rada M

2017-03-01

The objectives of this study were to gain insights into structure-retention relationships and to propose the model to estimating their retention. Chromatographic investigation of series of 36 Schiff bases and their copper(II) and nickel(II) complexes was performed under both normal- and reverse-phase conditions. Chemical structures of the compounds were characterized by molecular descriptors which are calculated from the structure and related to the chromatographic retention parameters by multiple linear regression analysis. Effects of chelation on retention parameters of investigated compounds, under normal- and reverse-phase chromatographic conditions, were analyzed by principal component analysis, quantitative structure-retention relationship and quantitative structure-activity relationship models were developed on the basis of theoretical molecular descriptors, calculated exclusively from molecular structure, and parameters of retention and lipophilicity. Copyright © 2016 John Wiley & Sons, Ltd.

Hydrodynamic Stability of Multicomponent Droplet Gasification in Reduced Gravity

NASA Technical Reports Server (NTRS)

Aharon, I.; Shaw, B. D.

1995-01-01

This investigation addresses the problem of hydrodynamic stability of a two-component droplet undergoing spherically-symmetrical gasification. The droplet components are assumed to have characteristic liquid species diffusion times that are large relative to characteristic droplet surface regression times. The problem is formulated as a linear stability analysis, with a goal of predicting when spherically-symmetric droplet gasification can be expected to be hydrodynamically unstable from surface-tension gradients acting along the surface of a droplet which result from perturbations. It is found that for the conditions assumed in this paper (quasisteady gas phase, no initial droplet temperature gradients, diffusion-dominated gasification), surface tension gradients do not play a role in the stability characteristics. In addition, all perturbations are predicted to decay such that droplets were hydrodynamically stable. Conditions are identified, however, that deserve more analysis as they may lead to hydrodynamic instabilities driven by capillary effects.

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

Vehicle operation characteristic under different ramp entrance conditions in underground road: Analysis, simulation and modelling

NASA Astrophysics Data System (ADS)

Yao, Qiming; Liu, Shuo; Liu, Yang

2018-05-01

An experimental design was used to study the vehicle operation characteristics of different ramp entrance conditions in underground road. With driving simulator, the experimental scenarios include left or right ramp with first, second and third service level, respectively, to collect vehicle speed, acceleration, lateral displacement and location information at the ramp entrance section. By using paired t-test and ANOVA, the influence factors of vehicle operating characteristics are studied. The result shows that effects of ramp layout and mainline traffic environment on vehicle operation characteristics are significant. The regression model of vehicle traveling distance on acceleration lane is established. Suggestions are made for ramp entrance design of underground road.

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Contrast sensitivity to spatial gratings in moderate and dim light conditions in patients with diabetes in the absence of diabetic retinopathy

PubMed Central

Safi, Sare; Rahimi, Anoushiravan; Raeesi, Afsaneh; Safi, Hamid; Aghazadeh Amiri, Mohammad; Malek, Mojtaba; Yaseri, Mehdi; Haeri, Mohammad; Middleton, Frank A; Solessio, Eduardo; Ahmadieh, Hamid

2017-01-01

Objective To evaluate the ability of contrast sensitivity (CS) to discriminate loss of visual function in diabetic subjects with no clinical signs of retinopathy relative to that of normal subjects. Research design and methods In this prospective cross-sectional study, we measured CS in 46 diabetic subjects with a mean age of 48±6 years, a best-corrected visual acuity of 20/20 and no signs of diabetic retinopathy. The CS in these subjects was compared with CS measurements in 46 normal control subjects at four spatial frequencies (3, 6, 12, 18 cycles per degree) under moderate (500 lux) and dim (less than 2 lux) background light conditions. Results CS was approximately 0.16 log units lower in patients with diabetes relative to controls both in moderate and in dim background light conditions. Logistic regression classification and receiver operating characteristic curve analysis indicated that CS analysis using two light conditions was more accurate (0.78) overall compared with CS analysis using only a single illumination condition (accuracy values were 0.67 and 0.70 in moderate and dim light conditions, respectively). Conclusions Our results showed that patients with diabetes without clinical signs of retinopathy exhibit a uniform loss in CS at all spatial frequencies tested. Measuring the loss in CS at two spatial frequencies (3 and 6 cycles per degree) and two light conditions (moderate and dim) is sufficiently robust to classify diabetic subjects with no retinopathy versus control subjects. PMID:28878937

Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

PubMed

Bond, H S; Sullivan, S G; Cowling, B J

2016-06-01

Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

Effect of the statin therapy on biochemical laboratory tests--a chemometrics study.

PubMed

Durceková, Tatiana; Mocák, Ján; Boronová, Katarína; Balla, Ján

2011-01-05

Statins are the first-line choice for lowering total and LDL cholesterol levels and very important medicaments for reducing the risk of coronary artery disease. The aim of this study is therefore assessment of the results of biochemical tests characterizing the condition of 172 patients before and after administration of statins. For this purpose, several chemometric tools, namely principal component analysis, cluster analysis, discriminant analysis, logistic regression, KNN classification, ROC analysis, descriptive statistics and ANOVA were used. Mutual relations of 11 biochemical laboratory tests, the patient's age and gender were investigated in detail. Achieved results enable to evaluate the extent of the statin treatment in each individual case. They may also help in monitoring the dynamic progression of the disease. Copyright © 2010 Elsevier B.V. All rights reserved.

The Dissolution Behavior of Borosilicate Glasses in Far-From Equilibrium Conditions

DOE PAGES

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.; ...

2018-02-10

An area of agreement in the waste glass corrosion community is that, at far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this work is to study the effects of temperature and pH on the dissolution rate of three model nuclear waste glasses (SON68, ISG, AFCI). The dissolution rate data are then used to parameterize a kinetic rate model based on Transition State Theory that has been developed to model glass corrosion behavior in dilute conditions. To do this, experiments were conducted atmore » temperatures of 23, 40, 70, and 90 °C and pH(22 °C) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies though quantifiable differences exist. The glass dissolution rates were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), with which a robust uncertainty analysis is performed. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, a mathematical description of the effect of glass composition on the rate parameter values should be possible. This would allow for the possibility of calculating the forward dissolution rate of glass based solely on composition. In addition, the method of determination of parameter uncertainty and correlation provides a framework for other rate models that describe the dissolution rates of other amorphous and crystalline materials in a wide range of chemical conditions. As a result, the higher level of uncertainty analysis would provide a basis for comparison of different rate models and allow for a better means of quantifiably comparing the various models.« less

The dissolution behavior of borosilicate glasses in far-from equilibrium conditions

NASA Astrophysics Data System (ADS)

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.; Ryan, Joseph V.; Asmussen, R. Matthew

2018-04-01

An area of agreement in the waste glass corrosion community is that, at far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this work is to study the effects of temperature and pH on the dissolution rate of three model nuclear waste glasses (SON68, ISG, AFCI). The dissolution rate data are then used to parameterize a kinetic rate model based on Transition State Theory that has been developed to model glass corrosion behavior in dilute conditions. To do this, experiments were conducted at temperatures of 23, 40, 70, and 90 °C and pH (22 °C) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies though quantifiable differences exist. The glass dissolution rates were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), with which a robust uncertainty analysis is performed. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, a mathematical description of the effect of glass composition on the rate parameter values should be possible. This would allow for the possibility of calculating the forward dissolution rate of glass based solely on composition. In addition, the method of determination of parameter uncertainty and correlation provides a framework for other rate models that describe the dissolution rates of other amorphous and crystalline materials in a wide range of chemical conditions. The higher level of uncertainty analysis would provide a basis for comparison of different rate models and allow for a better means of quantifiably comparing the various models.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

The Dissolution Behavior of Borosilicate Glasses in Far-From Equilibrium Conditions

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

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.

An area of agreement in the waste glass corrosion community is that, at far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this work is to study the effects of temperature and pH on the dissolution rate of three model nuclear waste glasses (SON68, ISG, AFCI). The dissolution rate data are then used to parameterize a kinetic rate model based on Transition State Theory that has been developed to model glass corrosion behavior in dilute conditions. To do this, experiments were conducted atmore » temperatures of 23, 40, 70, and 90 °C and pH(22 °C) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies though quantifiable differences exist. The glass dissolution rates were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), with which a robust uncertainty analysis is performed. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, a mathematical description of the effect of glass composition on the rate parameter values should be possible. This would allow for the possibility of calculating the forward dissolution rate of glass based solely on composition. In addition, the method of determination of parameter uncertainty and correlation provides a framework for other rate models that describe the dissolution rates of other amorphous and crystalline materials in a wide range of chemical conditions. As a result, the higher level of uncertainty analysis would provide a basis for comparison of different rate models and allow for a better means of quantifiably comparing the various models.« less

Awareness, treatment, control of diabetes mellitus and the risk factors: survey results from northeast China.

PubMed

Wang, Chang; Yu, Yaqin; Zhang, Xiangyang; Li, Yong; Kou, Changgui; Li, Bo; Tao, Yuchun; Zhen, Qing; He, Huan; Kanu, Joseph Sam; Huang, Xufeng; Han, Mei; Liu, Yawen

2014-01-01

The awareness, treatment and control of diabetes mellitus (DM) can effectively reflect on the social status of diabetes conditions. Although several researchers have investigated the awareness, treatment and control rates of diabetes mellitus in China, little is known about their association with risk factors. This study aims to examine the relationship between risk factors and awareness, treatment and control of diabetes mellitus in northeast China. A cross-sectional survey was conducted in 2012. Multistage stratified random cluster sampling design was used to select participants aged 18 to 79 years old. The analysis was based on a representative sample of 1,854 adult subjects. Multivariable logistic regression analysis was used to examine socio-demographic factors associated with the levels of awareness, treatment and control of diabetes mellitus. The awareness, treatment, and control rates of diabetes mellitus were 64.1%, 52.9% and 44.2%, respectively. In the multivariable logistic regression analysis, family history of diabetes was significantly positively associated with awareness (OR, 2.145; 95% CI, 1.600-2.875) and treatment (OR, 2.021; 95% CI, 1.559-2.619) of diabetes mellitus, while negatively associated with control (OR, 0.671; 95% CI, 0.529-0.951). Cigarette smokers and alcohol drinkers were less likely than non-smokers and non-drinkers to be aware of their blood glucose levels (OR, 0.895, 0.614; 95% CI, 0.659-1.216, 0.446-0.844, respectively). Participants who frequently exercise were more likely to be aware of their diabetic conditions than people who never or rarely exercise (OR, 2.003; 95% CI, 1.513-2.651). We found that the awareness and treatment of diabetes mellitus were positively associated with age and were high in participants with a family history of diabetes and those who exercise frequently, but low for cigarette smokers and alcohol drinkers. Participants with a family history of diabetes had their diabetic condition poorly controlled.

Ground Field-Based Hyperspectral Imaging: A Preliminary Study to Assess the Potential of Established Vegetation Indices to Infer Variation in Water-Use Efficiency.

NASA Astrophysics Data System (ADS)

Pelech, E. A.; McGrath, J.; Pederson, T.; Bernacchi, C.

2017-12-01

Increases in the global average temperature will consequently induce a higher occurrence of severe environmental conditions such as drought on arable land. To mitigate these threats, crops for fuel and food must be bred for higher water-use efficiencies (WUE). Defining genomic variation through high-throughput phenotypic analysis in field conditions has the potential to relieve the major bottleneck in linking desirable genetic traits to the associated phenotypic response. This can subsequently enable breeders to create new agricultural germplasm that supports the need for higher water-use efficient crops. From satellites to field-based aerial and ground sensors, the reflectance properties of vegetation measured by hyperspectral imaging is becoming a rapid high-throughput phenotyping technique. A variety of physiological traits can be inferred by regression analysis with leaf reflectance which is controlled by the properties and abundance of water, carbon, nitrogen and pigments. Although, given that the current established vegetation indices are designed to accentuate these properties from spectral reflectance, it becomes a challenge to infer relative measurements of WUE at a crop canopy scale without ground-truth data collection. This study aims to correlate established biomass and canopy-water-content indices with ground-truth data. Five bioenergy sorghum genotypes (Sorghum bicolor L. Moench) that have differences in WUE and wild-type Tobacco (Nicotiana tabacum var. Samsun) under irrigated and rainfed field conditions were examined. A linear regression analysis was conducted to determine if variation in canopy water content and biomass, driven by natural genotypic and artificial treatment influences, can be inferred using established vegetation indices. The results from this study will elucidate the ability of ground field-based hyperspectral imaging to assess variation in water content, biomass and water-use efficiency. This can lead to improved opportunities to select ideal genotypes for an increasing water-limited environment and to help parameterize and validate terrestrial vegetation models that require a better representation of genetic variation within crop species.

The role of socio-economic and clinical factors on HbA1c in children and adolescents with type 1 diabetes: an Italian multicentre survey.

PubMed

Gesuita, Rosaria; Skrami, Edlira; Bonfanti, Riccardo; Cipriano, Paola; Ferrito, Lucia; Frongia, Paola; Iafusco, Dario; Iannilli, Antonio; Lombardo, Fortunato; Mozzillo, Enza; Paleari, Renata; Rabbone, Ivana; Sabbion, Alberto; Salvatoni, Alessandro; Scaramuzza, Andrea; Schiaffini, Riccardo; Sulli, Nicoletta; Toni, Sonia; Carle, Flavia; Cherubini, Valentino

2017-05-01

To identify the role of the family's socio-economic and clinical characteristics on metabolic control in children and adolescents with type 1 diabetes. In this cross-sectional, multicentre study, 768 subjects with type 1 diabetes under 18 years of age were consecutively recruited from January 2008 to February 2009. Target condition was considered for HbA 1c values <7.5% (<58 mmol/mol). A multiple correspondence analysis (MCA) was performed to analyze the association between the socio-economic and clinical characteristics of the participants. A logistic regression analysis was performed to identify factors associated with the subjects metabolic control. In both analyses, the family's socio-economic status was represented, measured by the Hollingshead Four-Factor Index of Social Status (SES) or by parental years of education. A total of 28.1% of subjects reached target HbA1c values. The MCA identified a strong association between at-target condition and several factors: high levels of SES or high levels of parental education, the use of the carbohydrate counting system, the use of insulin pumps, the use of the insulin delivery system over a short period of time, a normal body mass index. The logistic regression analysis showed that SES and the mother's years of education were significantly associated with the target condition [odds ratio (OR): 1.01, 95% confidence interval (CI): 1.01-1.03, p = 0.029; OR: 1.05, 95% CI: 1.01-1.10, p = 0.027, respectively). Personal, clinical, and family characteristics were found to be associated with HbA 1c target. Their identification can be crucial in addressing strategies to optimize metabolic control and improve diabetes management. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Comparison of energy expenditure to walk or run a mile in adult normal weight and overweight men and women.

PubMed

Loftin, Mark; Waddell, Dwight E; Robinson, James H; Owens, Scott G

2010-10-01

We compared the energy expenditure to walk or run a mile in adult normal weight walkers (NWW), overweight walkers (OW), and marathon runners (MR). The sample consisted of 19 NWW, 11 OW, and 20 MR adults. Energy expenditure was measured at preferred walking speed (NWW and OW) and running speed of a recently completed marathon. Body composition was assessed via dual-energy x-ray absorptiometry. Analysis of variance was used to compare groups with the Scheffe's procedure used for post hoc analysis. Multiple regression analysis was used to predict energy expenditure. Results that indicated OW exhibited significantly higher (p < 0.05) mass and fat weight than NWW or MR. Similar values were found between NWW and MR. Absolute energy expenditure to walk or run a mile was similar between groups (NWW 93.9 ± 15.0, OW 98.4 ± 29.9, MR 99.3 ± 10.8 kcal); however, significant differences were noted when energy expenditure was expressed relative to mass (MR > NWW > OW). When energy expenditure was expressed per kilogram of fat-free mass, similar values were found across groups. Multiple regression analysis yielded mass and gender as significant predictors of energy expenditure (R = 0.795, SEE = 10.9 kcal). We suggest that walking is an excellent physical activity for energy expenditure in overweight individuals that are capable of walking without predisposed conditions such as osteoarthritis or cardiovascular risk factors. Moreover, from a practical perspective, our regression equation (kcal = mass (kg) × 0.789 - gender (men = 1, women = 2) × 7.634 + 51.109) allows for the prediction of energy expenditure for a given distance (mile) rather than predicting energy expenditure for a given time (minutes).

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

Collaborative Chronic Care Models for Mental Health Conditions: Cumulative Meta-Analysis and Meta-Regression to Guide Future Research and Implementation

PubMed Central

Grogan-Kaylor, Andrew; Perron, Brian E.; Kilbourne, Amy M.; Woltmann, Emily; Bauer, Mark S.

2013-01-01

Objective Prior meta-analysis indicates that collaborative chronic care models (CCMs) improve mental and physical health outcomes for individuals with mental disorders. This study aimed to investigate the stability of evidence over time and identify patient and intervention factors associated with CCM effects in order to facilitate implementation and sustainability of CCMs in clinical practice. Method We reviewed 53 CCM trials that analyzed depression, mental quality of life (QOL), or physical QOL outcomes. Cumulative meta-analysis and meta-regression were supplemented by descriptive investigations across and within trials. Results Most trials targeted depression in the primary care setting, and cumulative meta-analysis indicated that effect sizes favoring CCM quickly achieved significance for depression outcomes, and more recently achieved significance for mental and physical QOL. Four of six CCM elements (patient self-management support, clinical information systems, system redesign, and provider decision support) were common among reviewed trials, while two elements (healthcare organization support and linkages to community resources) were rare. No single CCM element was statistically associated with the success of the model. Similarly, meta-regression did not identify specific factors associated with CCM effectiveness. Nonetheless, results within individual trials suggest that increased illness severity predicts CCM outcomes. Conclusions Significant CCM trials have been derived primarily from four original CCM elements. Nonetheless, implementing and sustaining this established model will require healthcare organization support. While CCMs have typically been tested as population-based interventions, evidence supports stepped care application to more severely ill individuals. Future priorities include developing implementation strategies to support adoption and sustainability of the model in clinical settings while maximizing fit of this multi-component framework to local contextual factors. PMID:23938600

Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.

Brick mortar exposure and chronic lymphocytic leukemia.

PubMed

Markovic-Denic, L; Jankovic, S; Marinkovic, J; Radovanovic, Z

1995-01-01

A case-control study of 130 patients with chronic lymphocytic leukemia (CLL) and 130 controls matched with respect to sex, age (2 years), type of residence (urban-rural) and area of residence (according to the national per capita income) was carried out. Conditional logistic regression analysis showed that, apart of four risk factors already described in the literature (work in a hazardous industry, hair dye use, family history of leukemia and exposure to electromagnetic radiation), brick mortar exposure was also significantly related to CLL.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

Predicting birth weight with conditionally linear transformation models.

PubMed

Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

2016-12-01

Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Statistical inference methods for sparse biological time series data.

PubMed

Ndukum, Juliet; Fonseca, Luís L; Santos, Helena; Voit, Eberhard O; Datta, Susmita

2011-04-25

Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001). We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.

Predicting Redox Conditions in Groundwater Using Statistical Techniques: Implications for Nitrate Transport in Groundwater and Streams

NASA Astrophysics Data System (ADS)

Tesoriero, A. J.; Terziotti, S.

2014-12-01

Nitrate trends in streams often do not match expectations based on recent nitrogen source loadings to the land surface. Groundwater discharge with long travel times has been suggested as the likely cause for these observations. The fate of nitrate in groundwater depends to a large extent on the occurrence of denitrification along flow paths. Because denitrification in groundwater is inhibited when dissolved oxygen (DO) concentrations are high, defining the oxic-suboxic interface has been critical in determining pathways for nitrate transport in groundwater and to streams at the local scale. Predicting redox conditions on a regional scale is complicated by the spatial variability of reaction rates. In this study, logistic regression and boosted classification tree analysis were used to predict the probability of oxic water in groundwater in the Chesapeake Bay watershed. The probability of oxic water (DO > 2 mg/L) was predicted by relating DO concentrations in over 3,000 groundwater samples to indicators of residence time and/or electron donor availability. Variables that describe position in the flow system (e.g., depth to top of the open interval), soil drainage and surficial geology were the most important predictors of oxic water. Logistic regression and boosted classification tree analysis correctly predicted the presence or absence of oxic conditions in over 75 % of the samples in both training and validation data sets. Predictions of the percentages of oxic wells in deciles of risk were very accurate (r2>0.9) in both the training and validation data sets. Depth to the bottom of the oxic layer was predicted and is being used to estimate the effect that groundwater denitrification has on stream nitrate concentrations and the time lag between the application of nitrogen at the land surface and its effect on streams.

The relationship of environmental factors to the structure and distribution of subtidal seaweed vegetation of the western Basque coast (N Spain)

NASA Astrophysics Data System (ADS)

Díez, I.; Santolaria, A.; Gorostiaga, J. M.

2003-04-01

Subtidal vegetation distribution patterns in relation to environmental conditions (pollution, wave exposure, sedimentation, substratum slope and depth) were studied along the western Basque coast, northern Spain, by applying canonical correspondence analysis and log-linear regressions. A total of 90 species of macrophytes were recorded by systematic sampling along 21 transects. Mesophyllum lichenoides and Cystoseira baccata were the most abundant (accounting for 47% of the overall algal cover). Gelidium sesquipedale, Pterosiphonia complanata, Zanardinia prototypus, Codium decorticatum and Asparagopsis armata ( Falkenbergia phase) were other macrophytes with significant cover. Ordination analysis indicates that the five environmental variables explored account between them for 52% of the species data variance. Pollution, sedimentation and wave exposure were the principal factors explaining differences in flora composition and abundance (24, 14 and 12% of the explained variance, respectively). Log-linear regressions and canonical correspondence analyses reveal that C. baccata and G. sesquipedale exhibit a negative relationship with pollution, while sediment loading negatively affects G. sesquipedale, and C. baccata cannot stand high wave exposure levels. In contrast, P. complanata and C. decorticatum show a positive relationship with pollution and can bear high levels of sedimentation and wave exposure. M. lichenoides and Z. prototypus present a wide tolerance range for all these factors. Macroalgal cover, species richness and diversity remain practically constant from unpolluted to slightly polluted sites, but they decrease sharply under moderately polluted conditions. In the same way, algal cover decreases as sediment loading increases, but diversity and species richness show the highest values at intermediate levels of sedimentation. In relation to wave exposure, maximum algal cover was achieved at very exposed habitats whereas diversity and species richness were higher under semi-exposed conditions.

An empirical study using permutation-based resampling in meta-regression

PubMed Central

2012-01-01

Background In meta-regression, as the number of trials in the analyses decreases, the risk of false positives or false negatives increases. This is partly due to the assumption of normality that may not hold in small samples. Creation of a distribution from the observed trials using permutation methods to calculate P values may allow for less spurious findings. Permutation has not been empirically tested in meta-regression. The objective of this study was to perform an empirical investigation to explore the differences in results for meta-analyses on a small number of trials using standard large sample approaches verses permutation-based methods for meta-regression. Methods We isolated a sample of randomized controlled clinical trials (RCTs) for interventions that have a small number of trials (herbal medicine trials). Trials were then grouped by herbal species and condition and assessed for methodological quality using the Jadad scale, and data were extracted for each outcome. Finally, we performed meta-analyses on the primary outcome of each group of trials and meta-regression for methodological quality subgroups within each meta-analysis. We used large sample methods and permutation methods in our meta-regression modeling. We then compared final models and final P values between methods. Results We collected 110 trials across 5 intervention/outcome pairings and 5 to 10 trials per covariate. When applying large sample methods and permutation-based methods in our backwards stepwise regression the covariates in the final models were identical in all cases. The P values for the covariates in the final model were larger in 78% (7/9) of the cases for permutation and identical for 22% (2/9) of the cases. Conclusions We present empirical evidence that permutation-based resampling may not change final models when using backwards stepwise regression, but may increase P values in meta-regression of multiple covariates for relatively small amount of trials. PMID:22587815

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

NASA Astrophysics Data System (ADS)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

Correlation-study about the ambient dose rate and the weather conditions

NASA Astrophysics Data System (ADS)

Furuya, Masato; Hatano, Yuko; Aoyama, Tomoo; Igarashi, Yasuhito; Kita, Kazuyuki; Ishizuka, Masahide

2016-04-01

The long-term radiation risks are believed to be heavily affected by the resuspension process. We therefore focus on the surface-atmosphere exchange process of released radioactive materials in this study. Radioactive materials were deposited on the soil and float in the air, and such complicated process are influenced by the weather conditions deeply. We need to reveal the correlation between the weather conditions and the ambient dose rate. In this study, we study the correlation between the weather conditions and the ambient dose rate with the correction of the decrease due to the radioactive decay. We found that there is a negative correlation between the ambient dose rate and the soil water content by the correlation coefficient. Using this result, we reconstruct the ambient dose rate from the weather conditions by the multiple regression analysis and found that the reconstructed data agree with the observation very well. Using Kalman filter, which can be sequentially updates the state estimate, we obtained such a good agreement.

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.

Measurement, time-stamping, and analysis of electrodermal activity in fMRI

NASA Astrophysics Data System (ADS)

Smyser, Christopher; Grabowski, Thomas J.; Rainville, Pierre; Bechara, Antione; Razavi, Mehrdad; Mehta, Sonya; Eaton, Brent L.; Bolinger, Lizann

2002-04-01

A low cost fMRI-compatible system was developed for detecting electrodermal activity without inducing image artifact. Subject electrodermal activity was measured on the plantar surface of the foot using a standard recording circuit. Filtered analog skin conductance responses (SCR) were recorded with a general purpose, time-stamping data acquisition system. A conditioning paradigm involving painful thermal stimulation was used to demonstrate SCR detection and investigate neural correlates of conditioned autonomic activity. 128x128 pixel EPI-BOLD images were acquired with a GE 1.5T Signa scanner. Image analysis was performed using voxel-wise multiple linear regression. The covariate of interest was generated by convolving stimulus event onset with a standard hemodynamic response function. The function was time-shifted to determine optimal activation. Significance was tested using the t-statistic. Image quality was unaffected by the device, and conditioned and unconditioned SCRs were successfully detected. Conditioned SCRs correlated significantly with activity in the right anterior insular cortex. The effect was more robust when responses were scaled by SCR amplitude. The ability to measure and time register SCRs during fMRI acquisition enables studies of cognitive processes marked by autonomic activity, including those involving decision-making, pain, emotion, and addiction.

Comparing multiple imputation methods for systematically missing subject-level data.

PubMed

Kline, David; Andridge, Rebecca; Kaizar, Eloise

2017-06-01

When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can occur on the observation-level (time-varying) or the subject-level (non-time-varying). Traditionally, the focus of missing data methods for longitudinal data has been on missing observation-level variables. In this paper, we focus on missing subject-level variables and compare two multiple imputation approaches: a joint modeling approach and a sequential conditional modeling approach. We find the joint modeling approach to be preferable to the sequential conditional approach, except when the covariance structure of the repeated outcome for each individual has homogenous variance and exchangeable correlation. Specifically, the regression coefficient estimates from an analysis incorporating imputed values based on the sequential conditional method are attenuated and less efficient than those from the joint method. Remarkably, the estimates from the sequential conditional method are often less efficient than a complete case analysis, which, in the context of research synthesis, implies that we lose efficiency by combining studies. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Bim expression in endothelial cells and pericytes is essential for regression of the fetal ocular vasculature.

PubMed

Wang, Shoujian; Zaitoun, Ismail S; Johnson, Ryan P; Jamali, Nasim; Gurel, Zafer; Wintheiser, Catherine M; Strasser, Andreas; Lindner, Volkhard; Sheibani, Nader; Sorenson, Christine M

2017-01-01

Apoptosis plays a central role in developmental and pathological angiogenesis and vessel regression. Bim is a pro-apoptotic Bcl-2 family member that plays a prominent role in both developmental and pathological ocular vessel regression, and neovascularization. Endothelial cells (EC) and pericytes (PC) each play unique roles during vascular development, maintenance and regression. We recently showed that germline deletion of Bim results in persistent hyaloid vasculature, increased retinal vascular density and prevents retinal vessel regression in response to hyperoxia. To determine whether retinal vascular regression is attributable to Bim expression in EC or PC we generated mice carrying a conditional Bim allele (BimFlox/Flox) and VE-cadherin-cre (BimEC mice) or Pdgfrb-cre (BimPC mice). BimEC and BimPC mice demonstrated attenuated hyaloid vessel regression and postnatal retinal vascular remodeling. We also observed decreased retinal vascular apoptosis and proliferation. Unlike global Bim -/- mice, mice conditionally lacking Bim in EC or PC underwent hyperoxia-mediated vessel obliteration and subsequent retinal neovascularization during oxygen-induced ischemic retinopathy similar to control littermates. Thus, understanding the cell autonomous role Bim plays in the retinal vascular homeostasis will give us new insight into how to modulate pathological retinal neovascularization and vessel regression to preserve vision.

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

ERIC Educational Resources Information Center

Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

The association between a body shape index and cardiovascular risk in overweight and obese children and adolescents.

PubMed

Mameli, Chiara; Krakauer, Nir Y; Krakauer, Jesse C; Bosetti, Alessandra; Ferrari, Chiara Matilde; Moiana, Norma; Schneider, Laura; Borsani, Barbara; Genoni, Teresa; Zuccotti, Gianvincenzo

2018-01-01

A Body Shape Index (ABSI) and normalized hip circumference (Hip Index, HI) have been recently shown to be strong risk factors for mortality and for cardiovascular disease in adults. We conducted an observational cross-sectional study to evaluate the relationship between ABSI, HI and cardiometabolic risk factors and obesity-related comorbidities in overweight and obese children and adolescents aged 2-18 years. We performed multivariate linear and logistic regression analyses with BMI, ABSI, and HI age and sex normalized z scores as predictors to examine the association with cardiometabolic risk markers (systolic and diastolic blood pressure, fasting glucose and insulin, total cholesterol and its components, transaminases, fat mass % detected by bioelectrical impedance analysis) and obesity-related conditions (including hepatic steatosis and metabolic syndrome). We recruited 217 patients (114 males), mean age 11.3 years. Multivariate linear regression showed a significant association of ABSI z score with 10 out of 15 risk markers expressed as continuous variables, while BMI z score showed a significant correlation with 9 and HI only with 1. In multivariate logistic regression to predict occurrence of obesity-related conditions and above-threshold values of risk factors, BMI z score was significantly correlated to 7 out of 12, ABSI to 5, and HI to 1. Overall, ABSI is an independent anthropometric index that was significantly associated with cardiometabolic risk markers in a pediatric population affected by overweight and obesity.

Modelling Status Food Security Households Disease Sufferers Pulmonary Tuberculosis Uses the Method Regression Logistics Binary

NASA Astrophysics Data System (ADS)

Wulandari, S. P.; Salamah, M.; Rositawati, A. F. D.

2018-04-01

Food security is the condition where the food fulfilment is managed well for the country till the individual. Indonesia is one of the country which has the commitment to create the food security becomes main priority. However, the food necessity becomes common thing means that it doesn’t care about nutrient standard and the health condition of family member, so in the fulfilment of food necessity also has to consider the disease suffered by the family member, one of them is pulmonary tuberculosa. From that reasons, this research is conducted to know the factors which influence on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya by using binary logistic regression method. The analysis result by using binary logistic regression shows that the variables wife latest education, house density and spacious house ventilation significantly affect on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya, where the wife education level is University/equivalent, the house density is eligible or 8 m2/person and spacious house ventilation 10% of the floor area has the opportunity to become food secure households amounted to 0.911089. While the chance of becoming food insecure households amounted to 0.088911. The model household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya has been conformable, and the overall percentages of those classifications are at 71.8%.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

PubMed

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Static and moving solid/gas interface modeling in a hybrid rocket engine

NASA Astrophysics Data System (ADS)

Mangeot, Alexandre; William-Louis, Mame; Gillard, Philippe

2018-07-01

A numerical model was developed with CFD-ACE software to study the working condition of an oxygen-nitrogen/polyethylene hybrid rocket combustor. As a first approach, a simplified numerical model is presented. It includes a compressible transient gas phase in which a two-step combustion mechanism is implemented coupled to a radiative model. The solid phase from the fuel grain is a semi-opaque material with its degradation process modeled by an Arrhenius type law. Two versions of the model were tested. The first considers the solid/gas interface with a static grid while the second uses grid deformation during the computation to follow the asymmetrical regression. The numerical results are obtained with two different regression kinetics originating from ThermoGravimetry Analysis and test bench results. In each case, the fuel surface temperature is retrieved within a range of 5% error. However, good results are only found using kinetics from the test bench. The regression rate is found within 0.03 mm s-1 and average combustor pressure and its variation over time have the same intensity than the measurements conducted on the test bench. The simulation that uses grid deformation to follow the regression shows a good stability over a 10 s simulated time simulation.

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

DTIC Science & Technology

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

Mapping Regional Impervious Surface Distribution from Night Time Light: The Variability across Global Cities

NASA Astrophysics Data System (ADS)

Lin, M.; Yang, Z.; Park, H.; Qian, S.; Chen, J.; Fan, P.

2017-12-01

Impervious surface area (ISA) has become an important indicator for studying urban environments, but mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data is (NTL) and Resolution Imaging Spectroradiometer (MODIS) are the major remote sensing data source for regional ISA mapping. A single regression relationship between fractional ISA and NTL or various index derived based on NTL and MODIS vegetation index (NDVI) data was established in many previous studies for regional ISA mapping. However, due to the varying geographical, climatic, and socio-economic characteristics of different cities, the same regression relationship may vary significantly across different cities in the same region in terms of both fitting performance (i.e. R2) and the rate of change (Slope). In this study, we examined the regression relationship between fractional ISA and Vegetation Adjusted Nighttime light Urban Index (VANUI) for 120 randomly selected cities around the world with a multilevel regression model. We found that indeed there is substantial variability of both the R2 (0.68±0.29) and slopes (0.64±0.40) among individual regressions, which suggests that multilevel/hierarchical models are needed for accuracy improvement of future regional ISA mapping .Further analysis also let us find the this substantial variability are affected by climate conditions, socio-economic status, and urban spatial structures. However, all these effects are nonlinear rather than linear, thus could not modeled explicitly in multilevel linear regression models.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Low temperature-induced circulating triiodothyronine accelerates seasonal testicular regression.

PubMed

Ikegami, Keisuke; Atsumi, Yusuke; Yorinaga, Eriko; Ono, Hiroko; Murayama, Itaru; Nakane, Yusuke; Ota, Wataru; Arai, Natsumi; Tega, Akinori; Iigo, Masayuki; Darras, Veerle M; Tsutsui, Kazuyoshi; Hayashi, Yoshitaka; Yoshida, Shosei; Yoshimura, Takashi

2015-02-01

In temperate zones, animals restrict breeding to specific seasons to maximize the survival of their offspring. Birds have evolved highly sophisticated mechanisms of seasonal regulation, and their testicular mass can change 100-fold within a few weeks. Recent studies on Japanese quail revealed that seasonal gonadal development is regulated by central thyroid hormone activation within the hypothalamus, depending on the photoperiodic changes. By contrast, the mechanisms underlying seasonal testicular regression remain unclear. Here we show the effects of short day and low temperature on testicular regression in quail. Low temperature stimulus accelerated short day-induced testicular regression by shutting down the hypothalamus-pituitary-gonadal axis and inducing meiotic arrest and germ cell apoptosis. Induction of T3 coincided with the climax of testicular regression. Temporal gene expression analysis over the course of apoptosis revealed the suppression of LH response genes and activation of T3 response genes involved in amphibian metamorphosis within the testis. Daily ip administration of T3 mimicked the effects of low temperature stimulus on germ cell apoptosis and testicular mass. Although type 2 deiodinase, a thyroid hormone-activating enzyme, in the brown adipose tissue generates circulating T3 under low-temperature conditions in mammals, there is no distinct brown adipose tissue in birds. In birds, type 2 deiodinase is induced by low temperature exclusively in the liver, which appears to be caused by increased food consumption. We conclude that birds use low temperature-induced circulating T3 not only for adaptive thermoregulation but also to trigger apoptosis to accelerate seasonal testicular regression.

Identifying crash-prone traffic conditions under different weather on freeways.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan

2013-09-01

Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Nutritional status and weight gain in pregnant women.

PubMed

Sato, Ana Paula Sayuri; Fujimori, Elizabeth

2012-01-01

This study described the nutritional status of 228 pregnant women and the influence of this on birth weight. This is a retrospective study, developed in a health center in the municipality of São Paulo, with data obtained from medical records. Linear regression analysis was carried out. An association was verified between the initial and final nutritional status (p<0.001). The mean of total weight gain in the pregnant women who began the pregnancy underweight was higher compared those who started overweight/obese (p=0.005). Weight gain was insufficient for 43.4% of the pregnant women with adequate initial weight and for 36.4% of all the pregnant women studied. However, 37.1% of those who began the pregnancy overweight/obese finished with excessive weight gain, a condition that ultimately affected almost a quarter of the pregnant women. Anemia and low birth weight were uncommon, however, in the linear regression analysis, birth weight was associated with weight gain (p<0.05). The study highlights the importance of nutritional care before and during pregnancy to promote maternal-infant health.

Effects of eye artifact removal methods on single trial P300 detection, a comparative study.

PubMed

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

2014-01-15

Electroencephalographic signals are commonly contaminated by eye artifacts, even if recorded under controlled conditions. The objective of this work was to quantitatively compare standard artifact removal methods (regression, filtered regression, Infomax, and second order blind identification (SOBI)) and two artifact identification approaches for independent component analysis (ICA) methods, i.e. ADJUST and correlation. To this end, eye artifacts were removed and the cleaned datasets were used for single trial classification of P300 (a type of event related potentials elicited using the oddball paradigm). Statistical analysis of the results confirms that the combination of Infomax and ADJUST provides a relatively better performance (0.6% improvement on average of all subject) while the combination of SOBI and correlation performs the worst. Low-pass filtering the data at lower cutoffs (here 4 Hz) can also improve the classification accuracy. Without requiring any artifact reference channel, the combination of Infomax and ADJUST improves the classification performance more than the other methods for both examined filtering cutoffs, i.e., 4 Hz and 25 Hz. Copyright © 2013 Elsevier B.V. All rights reserved.

The parenting attitudes and the stress of mothers predict the asthmatic severity of their children: a prospective study.

PubMed

Nagano, Jun; Kakuta, Chikage; Motomura, Chikako; Odajima, Hiroshi; Sudo, Nobuyuki; Nishima, Sankei; Kubo, Chiharu

2010-10-07

To examine relationships between a mother's stress-related conditions and parenting attitudes and their children's asthmatic status. 274 mothers of an asthmatic child 2 to 12 years old completed a questionnaire including questions about their chronic stress/coping behaviors (the "Stress Inventory"), parenting attitudes (the "Ta-ken Diagnostic Test for Parent-Child Relationship, Parent Form"), and their children's disease status. One year later, a follow-up questionnaire was mailed to the mothers that included questions on the child's disease status. 223 mothers (81%) responded to the follow-up survey. After controlling for non-psychosocial factors including disease severity at baseline, multiple linear regression analysis followed by multiple logistic regression analysis found chronic irritation/anger and emotional suppression to be aggravating factors for children aged < 7 years; for children aged 7 and over, the mothers' egocentric behavior was a mitigating factor while interference was an aggravating factor. Different types of parental stress/coping behaviors and parenting styles may differently predict their children's asthmatic status, and such associations may change as children grow.

Developing optimum sample size and multistage sampling plans for Lobesia botrana (Lepidoptera: Tortricidae) larval infestation and injury in northern Greece.

PubMed

Ifoulis, A A; Savopoulou-Soultani, M

2006-10-01

The purpose of this research was to quantify the spatial pattern and develop a sampling program for larvae of Lobesia botrana Denis and Schiffermüller (Lepidoptera: Tortricidae), an important vineyard pest in northern Greece. Taylor's power law and Iwao's patchiness regression were used to model the relationship between the mean and the variance of larval counts. Analysis of covariance was carried out, separately for infestation and injury, with combined second and third generation data, for vine and half-vine sample units. Common regression coefficients were estimated to permit use of the sampling plan over a wide range of conditions. Optimum sample sizes for infestation and injury, at three levels of precision, were developed. An investigation of a multistage sampling plan with a nested analysis of variance showed that if the goal of sampling is focusing on larval infestation, three grape clusters should be sampled in a half-vine; if the goal of sampling is focusing on injury, then two grape clusters per half-vine are recommended.

Energy absorption as a predictor of leg impedance in highly trained females.

PubMed

Kulas, Anthony S; Schmitz, Randy J; Schultz, Sandra J; Watson, Mary Allen; Perrin, David H

2006-08-01

Although leg spring stiffness represents active muscular recruitment of the lower extremity during dynamic tasks such as hopping and running, the joint-specific characteristics comprising the damping portion of this measure, leg impedance, are uncertain. The purpose of this investigation was to assess the relationship between leg impedance and energy absorption at the ankle, knee, and hip during early (impact) and late (stabilization) phases of landing. Twenty highly trained female dancers (age = 20.3 +/- 1.4 years, height = 163.7 +/- 6.0 cm, mass = 62.1 +/- 8.1 kg) were instrumented for biomechanical analysis. Subjects performed three sets of double-leg landings from under preferred, stiff, and soft landing conditions. A stepwise linear regression analysis revealed that ankle and knee energy absorption at impact, and knee and hip energy absorption during the stabilization phases of landing explained 75.5% of the variance in leg impedance. The primary predictor of leg impedance was knee energy absorption during the stabilization phase, independently accounting for 55% of the variance. Future validation studies applying this regression model to other groups of individuals are warranted.

Different techniques of multispectral data analysis for vegetation fraction retrieval

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi

2012-07-01

Vegetation monitoring is one of the most important applications of remote sensing technologies. In respect to farmlands, the assessment of crop condition constitutes the basis of growth, development, and yield processes monitoring. Plant condition is defined by a set of biometric variables, such as density, height, biomass amount, leaf area index, and etc. The canopy cover fraction is closely related to these variables, and is state-indicative of the growth process. At the same time it is a defining factor of the soil-vegetation system spectral signatures. That is why spectral mixtures decomposition is a primary objective in remotely sensed data processing and interpretation, specifically in agricultural applications. The actual usefulness of the applied methods depends on their prediction reliability. The goal of this paper is to present and compare different techniques for quantitative endmember extraction from soil-crop patterns reflectance. These techniques include: linear spectral unmixing, two-dimensional spectra analysis, spectral ratio analysis (vegetation indices), spectral derivative analysis (red edge position), colorimetric analysis (tristimulus values sum, chromaticity coordinates and dominant wavelength). The objective is to reveal their potential, accuracy and robustness for plant fraction estimation from multispectral data. Regression relationships have been established between crop canopy cover and various spectral estimators.

Optimization of cultural conditions for conversion of glycerol to ethanol by Enterobacter aerogenes S012

PubMed Central

2013-01-01

The aim of this research is to optimize the cultural conditions for the conversion of glycerol to ethanol by Enterobacter aerogenes S012. Taguchi method was used to screen the cultural conditions based on their signal to noise ratio (SN). Temperature (°C), agitation speed (rpm) and time (h) were found to have the highest influence on both glycerol utilization and ethanol production by the organism while pH had the lowest. Full factorial design, statistical analysis, and regression model equation were used to optimize the selected cultural parameters for maximum ethanol production. The result showed that fermentation at 38°C and 200 rpm for 48 h would be ideal for the bacteria to produce maximum amount of ethanol from glycerol. At these optimum conditions, ethanol production, yield and productivity were 25.4 g/l, 0.53 g/l/h, and 1.12 mol/mol-glycerol, repectively. Ethanol production increased to 26.5 g/l while yield and productivity decreased to 1.04 mol/mol-glycerol and 0.37 g/l/h, respectively, after 72 h. Analysis of the fermentation products was performed using HPLC, while anaerobic condition was created by purging the fermentation vessel with nitrogen gas. PMID:23388539

The process and utility of classification and regression tree methodology in nursing research

PubMed Central

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-01-01

Aim This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design Discussion paper. Data sources English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013. Discussion Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. PMID:24237048

The process and utility of classification and regression tree methodology in nursing research.

PubMed

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-06-01

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

Correlates of cognitive function scores in elderly outpatients.

PubMed

Mangione, C M; Seddon, J M; Cook, E F; Krug, J H; Sahagian, C R; Campion, E W; Glynn, R J

1993-05-01

To determine medical, ophthalmologic, and demographic predictors of cognitive function scores as measured by the Telephone Interview for Cognitive Status (TICS), an adaptation of the Folstein Mini-Mental Status Exam. A secondary objective was to perform an item-by-item analysis of the TICS scores to determine which items correlated most highly with the overall scores. Cross-sectional cohort study. The Glaucoma Consultation Service of the Massachusetts Eye and Ear Infirmary. 472 of 565 consecutive patients age 65 and older who were seen at the Glaucoma Consultation Service between November 1, 1987 and October 31, 1988. Each subject had a standard visual examination and review of medical history at entry, followed by a telephone interview that collected information on demographic characteristics, cognitive status, health status, accidents, falls, symptoms of depression, and alcohol intake. A multivariate linear regression model of correlates of TICS score found the strongest correlates to be education, age, occupation, and the presence of depressive symptoms. The only significant ocular condition that correlated with lower TICS score was the presence of surgical aphakia (model R2 = .46). Forty-six percent (216/472) of patients fell below the established definition of normal on the mental status scale. In a logistic regression analysis, the strongest correlates of an abnormal cognitive function score were age, diabetes, educational status, and occupational status. An item analysis using step-wise linear regression showed that 85 percent of the variance in the TICS score was explained by the ability to perform serial sevens and to repeat 10 items immediately after hearing them. Educational status correlated most highly with both of these items (Kendall Tau R = .43 and Kendall Tau R = .30, respectively). Education, occupation, depression, and age were the strongest correlates of the score on this new screening test for assessing cognitive status. These factors were stronger correlates of the TICS score than chronic medical conditions, visual loss, or medications. The Telephone Interview for Cognitive Status is a useful instrument, but it may overestimate the prevalence of dementia in studies with a high prevalence of persons with less than a high school education.

Advantages of the net benefit regression framework for economic evaluations of interventions in the workplace: a case study of the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders.

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2014-04-01

Economic evaluations commonly accompany trials of new treatments or interventions; however, regression methods and their corresponding advantages for the analysis of cost-effectiveness data are not well known. To illustrate regression-based economic evaluation, we present a case study investigating the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders. We implement net benefit regression to illustrate its strengths and limitations. Net benefit regression offers a simple option for cost-effectiveness analyses of person-level data. By placing economic evaluation in a regression framework, regression-based techniques can facilitate the analysis and provide simple solutions to commonly encountered challenges. Economic evaluations of person-level data (eg, from a clinical trial) should use net benefit regression to facilitate analysis and enhance results.

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

A regional classification scheme for estimating reference water quality in streams using land-use-adjusted spatial regression-tree analysis

USGS Publications Warehouse

Robertson, Dale M.; Saad, D.A.; Heisey, D.M.

2006-01-01

Various approaches are used to subdivide large areas into regions containing streams that have similar reference or background water quality and that respond similarly to different factors. For many applications, such as establishing reference conditions, it is preferable to use physical characteristics that are not affected by human activities to delineate these regions. However, most approaches, such as ecoregion classifications, rely on land use to delineate regions or have difficulties compensating for the effects of land use. Land use not only directly affects water quality, but it is often correlated with the factors used to define the regions. In this article, we describe modifications to SPARTA (spatial regression-tree analysis), a relatively new approach applied to water-quality and environmental characteristic data to delineate zones with similar factors affecting water quality. In this modified approach, land-use-adjusted (residualized) water quality and environmental characteristics are computed for each site. Regression-tree analysis is applied to the residualized data to determine the most statistically important environmental characteristics describing the distribution of a specific water-quality constituent. Geographic information for small basins throughout the study area is then used to subdivide the area into relatively homogeneous environmental water-quality zones. For each zone, commonly used approaches are subsequently used to define its reference water quality and how its water quality responds to changes in land use. SPARTA is used to delineate zones of similar reference concentrations of total phosphorus and suspended sediment throughout the upper Midwestern part of the United States. ?? 2006 Springer Science+Business Media, Inc.

Social support and the self-rated health of older people

PubMed Central

Dai, Yue; Zhang, Chen-Yun; Zhang, Bao-Quan; Li, Zhanzhan; Jiang, Caixiao; Huang, Hui-Ling

2016-01-01

Abstract The lack of social support in elderly populations incurs real societal costs and can lead to their poor health. The aim of this study is to investigate the self-rated health (SRH) and social support among older people as well as its associated factors. We conducted a cross-sectional study among 312 urban community-dwelling elderly aged 65 to 90 years in Tainan Taiwan and Fuzhou Fujian Province from March 2012 to October 2012. A Spearson correlation test, independent t test, a Pearson χ2 test, a linear regression analysis, and a multiple-level model were performed to analyze the results. The participants identified children as the most important source of objective and subjective support, followed by spouse and relatives. Tainan's elderly received more daily life assistance and emotional support, showed stronger awareness of the need to seek help, and maintained a higher frequency of social interactions compared with the elderly in Fuzhou. The mean objective support, subjective support, and support utilization scores as well as the overall social support among Tainan's elderly were significantly high compared with the scores among Fuzhou's elderly. Further, Tainan's elderly rated better SRH than Fuzhou's elderly. Correlation analysis showed that social support was significantly correlated with city, age, living conditions, marital status, and SRH. Multiple linear regression analysis, with social support as a dependent variable, retained the following independent predictors in the final regression model: city (4.792, 95% confidence interval [CI]: 3.068–6.516, P = 0.000), age (−0.805, 95% CI: −1.394 to −0.135, P = 0.013), marital status (−1.260, 95% CI: −1.891 to −0.629, P = 0.000), living conditions (4.069, 95% CI: 3.022–5.116, P = 0.000), and SRH −1.941, 95% CI: −3.194 to −0.688, P = 0.003). The multiple-level model showed that city would impact older people's social support (χ2 = 5.103, P < 0.001). Marital status (−2.133, 95% CI: −2.768 to −1.499, P = 0.000), education (1.697, 95% CI: 0.589–2.805 P = 0.003), living conditions (4.20, 95% CI: 1.762–6.638, P = 0.000), and SRH (−3.144, 95% CI: −4.502 to −1.727, P = 0.000) were the associated factors. Thus, city, age, marital status, education, living conditions, and SRH might be the associated factors for social support among older people. This study presents some feasible implications for social support improvement in China and in other nations worldwide. PMID:27310979

Relative Deprivation, Poverty, and Subjective Health: JAGES Cross-Sectional Study

PubMed Central

Saito, Masashige; Kondo, Katsunori; Kondo, Naoki; Abe, Aya; Ojima, Toshiyuki; Suzuki, Kayo

2014-01-01

To evaluate the association between relative deprivation (lacking daily necessities) and subjective health in older Japanese adults, we performed a cross-sectional analysis using data from the Japan Gerontological Evaluation Study (JAGES). The data were obtained from functionally independent residents aged ≥65 years from 24 municipalities in Japan (n = 24,742). Thirteen items in three dimensions were used to evaluate relative deprivation of material conditions. Approximately 28% of older Japanese people indicated that they lacked some daily necessities (non-monetary poverty). A two-level Poisson regression analysis revealed that relative deprivation was associated with poor self-rated health (PR = 1.3–1.5) and depressive symptoms (PR = 1.5–1.8) in both men and women, and these relationships were stronger than those observed in people living in relative poverty (monetary poverty). The interaction effect between relative deprivation and relative poverty was not associated with poor health. As a dimension of the social determinants of health, poverty should be evaluated from a multidimensional approach, capturing not only monetary conditions but also material-based, non-monetary conditions. PMID:25350284

Relative deprivation, poverty, and subjective health: JAGES cross-sectional study.

PubMed

Saito, Masashige; Kondo, Katsunori; Kondo, Naoki; Abe, Aya; Ojima, Toshiyuki; Suzuki, Kayo

2014-01-01

To evaluate the association between relative deprivation (lacking daily necessities) and subjective health in older Japanese adults, we performed a cross-sectional analysis using data from the Japan Gerontological Evaluation Study (JAGES). The data were obtained from functionally independent residents aged ≥65 years from 24 municipalities in Japan (n = 24,742). Thirteen items in three dimensions were used to evaluate relative deprivation of material conditions. Approximately 28% of older Japanese people indicated that they lacked some daily necessities (non-monetary poverty). A two-level Poisson regression analysis revealed that relative deprivation was associated with poor self-rated health (PR = 1.3-1.5) and depressive symptoms (PR = 1.5-1.8) in both men and women, and these relationships were stronger than those observed in people living in relative poverty (monetary poverty). The interaction effect between relative deprivation and relative poverty was not associated with poor health. As a dimension of the social determinants of health, poverty should be evaluated from a multidimensional approach, capturing not only monetary conditions but also material-based, non-monetary conditions.

Correcting for the influence of sampling conditions on biomarkers of exposure to phenols and phthalates: a 2-step standardization method based on regression residuals.

PubMed

Mortamais, Marion; Chevrier, Cécile; Philippat, Claire; Petit, Claire; Calafat, Antonia M; Ye, Xiaoyun; Silva, Manori J; Brambilla, Christian; Eijkemans, Marinus J C; Charles, Marie-Aline; Cordier, Sylvaine; Slama, Rémy

2012-04-26

Environmental epidemiology and biomonitoring studies typically rely on biological samples to assay the concentration of non-persistent exposure biomarkers. Between-participant variations in sampling conditions of these biological samples constitute a potential source of exposure misclassification. Few studies attempted to correct biomarker levels for this error. We aimed to assess the influence of sampling conditions on concentrations of urinary biomarkers of select phenols and phthalates, two widely-produced families of chemicals, and to standardize biomarker concentrations on sampling conditions. Urine samples were collected between 2002 and 2006 among 287 pregnant women from Eden and Pélagie cohorts, from which phthalates and phenols metabolites levels were assayed. We applied a 2-step standardization method based on regression residuals. First, the influence of sampling conditions (including sampling hour, duration of storage before freezing) and of creatinine levels on biomarker concentrations were characterized using adjusted linear regression models. In the second step, the model estimates were used to remove the variability in biomarker concentrations due to sampling conditions and to standardize concentrations as if all samples had been collected under the same conditions (e.g., same hour of urine collection). Sampling hour was associated with concentrations of several exposure biomarkers. After standardization for sampling conditions, median concentrations differed by--38% for 2,5-dichlorophenol to +80 % for a metabolite of diisodecyl phthalate. However, at the individual level, standardized biomarker levels were strongly correlated (correlation coefficients above 0.80) with unstandardized measures. Sampling conditions, such as sampling hour, should be systematically collected in biomarker-based studies, in particular when the biomarker half-life is short. The 2-step standardization method based on regression residuals that we proposed in order to limit the impact of heterogeneity in sampling conditions could be further tested in studies describing levels of biomarkers or their influence on health.

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.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Quantification and regionalization of groundwater recharge in South-Central Kansas: Integrating field characterization, statistical analysis, and GIS

USGS Publications Warehouse

Sophocleous, M.

2000-01-01

A practical methodology for recharge characterization was developed based on several years of field-oriented research at 10 sites in the Great Bend Prairie of south-central Kansas. This methodology combines the soil-water budget on a storm-by-storm year-round basis with the resulting watertable rises. The estimated 1985-1992 average annual recharge was less than 50mm/year with a range from 15 mm/year (during the 1998 drought) to 178 mm/year (during the 1993 flood year). Most of this recharge occurs during the spring months. To regionalize these site-specific estimates, an additional methodology based on multiple (forward) regression analysis combined with classification and GIS overlay analyses was developed and implemented. The multiple regression analysis showed that the most influential variables were, in order of decreasing importance, total annual precipitation, average maximum springtime soil-profile water storage, average shallowest springtime depth to watertable, and average springtime precipitation rate. Therefore, four GIS (ARC/INFO) data "layers" or coverages were constructed for the study region based on these four variables, and each such coverage was classified into the same number of data classes to avoid biasing the results. The normalized regression coefficients were employed to weigh the class rankings of each recharge-affecting variable. This approach resulted in recharge zonations that agreed well with the site recharge estimates. During the "Great Flood of 1993," when rainfall totals exceeded normal levels by -200% in the northern portion of the study region, the developed regionalization methodology was tested against such extreme conditions, and proved to be both practical, based on readily available or easily measurable data, and robust. It was concluded that the combination of multiple regression and GIS overlay analyses is a powerful and practical approach to regionalizing small samples of recharge estimates.

Automated particle identification through regression analysis of size, shape and colour

NASA Astrophysics Data System (ADS)

Rodriguez Luna, J. C.; Cooper, J. M.; Neale, S. L.

2016-04-01

Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to "predict" with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own.

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.

Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis.

PubMed

Mercuri, A; Pagliari, M; Baxevanis, F; Fares, R; Fotaki, N

2017-02-25

In this study the selection of in vivo predictive in vitro dissolution experimental set-ups using a multivariate analysis approach, in line with the Quality by Design (QbD) principles, is explored. The dissolution variables selected using a design of experiments (DoE) were the dissolution apparatus [USP1 apparatus (basket) and USP2 apparatus (paddle)], the rotational speed of the basket/or paddle, the operator conditions (dissolution apparatus brand and operator), the volume, the pH, and the ethanol content of the dissolution medium. The dissolution profiles of two nifedipine capsules (poorly soluble compound), under conditions mimicking the intake of the capsules with i. water, ii. orange juice and iii. an alcoholic drink (orange juice and ethanol) were analysed using multiple linear regression (MLR). Optimised dissolution set-ups, generated based on the mathematical model obtained via MLR, were used to build predicted in vitro-in vivo correlations (IVIVC). IVIVC could be achieved using physiologically relevant in vitro conditions mimicking the intake of the capsules with an alcoholic drink (orange juice and ethanol). The multivariate analysis revealed that the concentration of ethanol used in the in vitro dissolution experiments (47% v/v) can be lowered to less than 20% v/v, reflecting recently found physiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Factors associated with preventable infant death: a multiple logistic regression.

PubMed

Vidal E Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

2018-01-01

OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight.

Evaluation of Female Breast Cancer Risk Among the Betel Quid Chewer: A Bio-Statistical Assessment in Assam, India.

PubMed

Rajbongshi, Nijara; Mahanta, Lipi B; Nath, Dilip C

2015-06-01

Breast cancer is the most commonly diagnosed cancer among the female population of Assam, India. Chewing of betel quid with or without tobacco is common practice among female population of this region. Moreoverthe method of preparing the betel quid is different from other parts of the country.So matched case control study is conducted to analyse whetherbetel quid chewing plays a significant role in the high incidence of breast cancer occurrences in Assam. Here, controls are matched to the cases by age at diagnosis (±5 years), family income and place of residence with matching ratio 1:1. Conditional logistic regression models and odd ratios (OR) was used to draw conclusions. It is observed that cases are more habituated to chewing habits than the controls.Further the conditional logistic regression analysis reveals that betel quid chewer faces 2.353 times more risk having breast cancer than the non-chewer with p value 0.0003 (95% CI 1.334-4.150). Though the female population in Assam usually does not smoke, the addictive habits typical to this region have equal effect on the occurrence of breast cancer.

Association between age and high-risk human papilloma virus in Mexican oral cancer patients.

PubMed

González-Ramírez, I; Irigoyen-Camacho, M E; Ramírez-Amador, V; Lizano-Soberón, M; Carrillo-García, A; García-Carrancá, A; Sánchez-Pérez, Y; Méndez-Martínez, R; Granados-García, M; Ruíz-Godoy, Lm; García-Cuellar, Cm

2013-11-01

Studies reporting low prevalence of HPV in OSCC with declining age at presentation are increasing. The aim of this study was to determine the prevalence of HPV in a group of OSCC cases and controls in a Mexican population. The matched case-control study included 80 OSCC cases and 320 controls. HPV/DNA presence was evaluated through PCR amplification using three sets of consensus primers for the L1 gene. A conditional logistic regression analysis was carried out for the matched OSCC cases and controls. Interactions between risk factors and OCSS were tested in the construction process of the models. HPV prevalence was 5% in OSCC cases and 2.5% in controls. HPV-detected types were 16, 18 and 56. According to conditional logistics regression model, an association was detected between HR-HPV and OSCC. All HR-HPV-positive OSCC cases corresponded to young patients (<45 years), non-smokers and non-alcohol drinkers. The HR-HPV can be a contributing factor to oral carcinogenesis, especially in younger individuals without known risk factors such as tobacco and alcohol. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Refining the notion of maturing out: results from the national epidemiologic survey on alcohol and related conditions.

PubMed

Vergés, Alvaro; Haeny, Angela M; Jackson, Kristina M; Bucholz, Kathleen K; Grant, Julia D; Trull, Timothy J; Wood, Phillip K; Sher, Kenneth J

2013-12-01

Our aim was to determine if the decrease in drug use disorders with age is attributable to changes in persistence, as implied by the notion of maturing out. Also, we examined the association between role transitions and persistence, recurrence, and new onset of drug use disorders. We performed secondary analysis of the 2 waves of the National Epidemiologic Survey on Alcohol and Related Conditions data (baseline assessment 2001-2002, follow-up conducted 2004-2005). We conducted logistic regressions and multinomial logistic regression to determine the effect of age on wave 2 diagnosis status, as well as the interaction between age and role transitions. Rates of persistence were stable over the life span, whereas rates of new onset and recurrence decreased with age. Changes in parenthood, marital, and employment status were associated with persistence, new onset, and recurrence. We found an interaction between marital status and age. Our findings challenge commonly held notions that the age-related decrease in drug use disorders is attributable to an increase in persistence, and that the effects of role transitions are stronger during young, compared with middle and older, adulthood.

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

Development of a traveltime prediction equation for streams in Arkansas

USGS Publications Warehouse

Funkhouser, Jaysson E.; Barks, C. Shane

2004-01-01

During 1971 and 1981 and 2001 and 2003, traveltime measurements were made at 33 sample sites on 18 streams throughout northern and western Arkansas using fluorescent dye. Most measurements were made during steady-state base-flow conditions with the exception of three measurements made during near steady-state medium-flow conditions (for the study described in this report, medium-flow is approximately 100-150 percent of the mean monthly streamflow during the month the dye trace was conducted). These traveltime data were compared to the U.S. Geological Survey?s national traveltime prediction equation and used to develop a specific traveltime prediction equation for Arkansas streams. In general, the national traveltime prediction equation yielded results that over-predicted the velocity of the streams for 29 of the 33 sites measured. The standard error for the national traveltime prediction equation was 105 percent. The coefficient of determination was 0.78. The Arkansas prediction equation developed from a regression analysis of dye-tracing results was a significant improvement over the national prediction equation. This regression analysis yielded a standard error of 46 percent and a coefficient of determination of 0.74. The predicted velocities using this equation compared better to measured velocities. Using the variables in a regression analysis, the Arkansas prediction equation derived for the peak velocity in feet per second was: (Actual Equation Shown in report) In addition to knowing when the peak concentration will arrive at a site, it is of great interest to know when the leading edge of a contaminant plume will arrive. The traveltime of the leading edge of a contaminant plume indicates when a potential problem might first develop and also defines the overall shape of the concentration response function. Previous USGS reports have shown no significant relation between any of the variables and the time from injection to the arrival of the leading edge of the dye plume. For this report, the analysis of the dye-tracing data yielded a significant correlation between traveltime of the leading edge and traveltime of the peak concentration with an R2 value of 0.99. These data indicate that the traveltime of the leading edge can be estimated from: (Actual Equation Shown in Report)

Insomnia, trouble sleeping, and complementary and alternative medicine: Analysis of the 2002 national health interview survey data.

PubMed

Pearson, Nancy J; Johnson, Laura Lee; Nahin, Richard L

2006-09-18

Insomnia and other disorders that result in trouble sleeping are common in the United States and are often associated with chronic health conditions. Some individuals with insomnia or trouble sleeping use complementary and alternative medicine (CAM) therapies to treat their condition, but the prevalence of such use and the most common types of CAM therapies selected are not known. Prevalence of insomnia or trouble sleeping and of CAM use for treating such conditions was examined using the 2002 National Health Interview Survey. Logistic regression was used to examine associations between insomnia or trouble sleeping, comorbid conditions, and use of CAM treatments. The 12-month prevalence rate of insomnia or trouble sleeping was 17.4%. There was a strong positive association between adults who reported having insomnia or trouble sleeping and adults who reported 4 of 5 common conditions: obesity (adjusted odds ratio [OR], 1.15; 99% confidence interval [CI], 1.01-1.31), hypertension (OR, 1.32; 99% CI, 1.16-1.51), congestive heart failure (OR, 2.24; 99% CI, 1.60-3.14), and anxiety or depression (OR, 5.64; 99% CI, 5.07-6.29). Of those with insomnia or trouble sleeping, 4.5% used some form of CAM therapy to treat their condition. According to the National Health Interview Survey analysis, over 1.6 million civilian, noninstitutionalized adult US citizens use CAM to treat insomnia or trouble sleeping. The details of this analysis will serve as a guide for future research on CAM therapies for sleep disorders.

Multiple Chronic Conditions and Hospitalizations Among Recipients of Long-Term Services and Supports

PubMed Central

Van Cleave, Janet H.; Egleston, Brian L.; Abbott, Katherine M.; Hirschman, Karen B.; Rao, Aditi; Naylor, Mary D.

2016-01-01

Background Among older adults receiving long term-services and supports (LTSS), debilitating hospitalizations is a pervasive clinical and research problem. Multiple chronic conditions (MCC) are prevalent in LTSS recipients. However, the combination of MCC and diseases associated with hospitalizations of LTSS recipients is unclear. Objective The purpose of this analysis was to determine the association between classes of MCC in newly enrolled LTSS recipients and the number of hospitalizations over a one-year period following enrollment. Methods This report is based on secondary analysis of extant data from a longitudinal cohort study of 470 new recipients of LTSS, ages 60 years and older, receiving services in assisted living facilities, nursing homes, or through home- and community-based services. Using baseline chronic conditions reported in medical records, latent class analysis (LCA) was used to identify classes of MCC and posterior probabilities of membership in each class. Poisson regressions were used to estimate the relative ratio between posterior probabilities of class membership and number of hospitalizations during the 3 month period prior to the start of LTSS (baseline) and then every three months forward through 12 months. Results Three latent MCC-based classes named Cardiopulmonary, Cerebrovascular/Paralysis, and All Other Conditions were identified. The Cardiopulmonary class was associated with elevated numbers of hospitalization compared to the All Other Conditions class (relative ratio [RR] = 1.88, 95% CI [1.33, 2.65], p < .001). Conclusion Older LTSS recipients with a combination of MCCs that includes cardiopulmonary conditions have increased risk for hospitalization. PMID:27801713

Early Reticulocytosis and Anemia Are Associated with Abnormal and Conditional Transcranial Doppler Velocities in Children with Sickle Cell Anemia.

PubMed

Meier, Emily Riehm; Fasano, Ross M; Estrada, Monica; He, Jianping; Luban, Naomi L C; McCarter, Robert

2016-02-01

To improve prediction of sickle cell anemia severity at an early age, we evaluated whether absolute reticulocyte count (ARC) or hemoglobin (Hb) levels during early infancy (2-6 months of age) in patients with sickle cell anemia predict the risk of later developing an abnormal (abTCD) or conditional (cdTCD) Transcranial Doppler (TCD). We used chart review to identify 121 consecutive patients who underwent TCD screening and had steady state ARC and Hb levels recorded between 2 and 6 months of age. Cox regression analysis was used to determine the relationship between ARC, Hb levels, and risk of developing cdTCD/abTCD over time. Mean ARC in early infancy was highest and mean Hb lowest in those children with abTCDs and cdTCDs. Cox regression analysis revealed that those subjects with an ARC ≥200 K/μL in early infancy had nearly 3 times the risk of having an abTCD/cdTCD than the group with an ARC <200 K/μL, and patients with a Hb <8.5 g/dL had 2.7 times the risk of having an abTCD/cdTCD. These data suggest that both elevated ARC and low baseline Hb during early infancy are associated with an increased risk of developing a cdTCD or abTCD later in childhood. Copyright © 2016 Elsevier Inc. All rights reserved.

Dose-response effects for depression and Schizophrenia management on hospital utilization in Illinois Medicaid: a multivariate regression analysis.

PubMed

Berg, Gregory D; Donnelly, Shawn; Warnick, Kathleen; Medina, Wendie; Miller, Mary

2014-07-03

The prevalence of schizophrenia and depression in the United States is far higher among Medicaid recipients than in the general population. Individuals suffering from mental illness, including schizophrenia and depression, also have higher rates of emergency department utilization, which is costly and may not generate the positive health outcomes desired. Disease management programs strive to help individuals suffering from chronic illnesses better manage their condition(s) and seek health care in the appropriate settings. The objective of this manuscript is to estimate a dose-response impact on hospital inpatient and emergency room utilizations for any reason by Medicaid recipients with depression or schizophrenia who received disease management contacts. Multivariate regression analysis of panel data taken from administrative claims was conducted to test the hypothesis that increased contacts lower the likelihood of all-cause inpatient admissions and emergency room visits. Subjects included 6,274 members of Illinois' non-institutionalized Medicaid-only aged, blind or disabled population diagnosed with depression or schizophrenia. The statistical measure is the odds ratio. The odds ratio association is between the monthly utilization indicators and the number of contacts (doses) a member had for each particular disease management intervention. Higher numbers of intervention contacts for Medicaid recipients diagnosed with depression or schizophrenia were associated with statistically significant reductions in all-cause inpatient admissions and emergency room utilizations. There is a high correlation between depression and schizophrenia disease management contacts and lowered all-cause hospital inpatient and emergency room utilizations.

Treatment of Nausea and Vomiting in Pregnancy: Factors Associated with ED Revisits

PubMed Central

Sharp, Brian R.; Sharp, Kristen M.; Patterson, Brian; Dooley-Hash, Suzanne

2016-01-01

Introduction Nausea and vomiting in pregnancy (NVP) is a condition that commonly affects women in the first trimester of pregnancy. Despite frequently leading to emergency department (ED) visits, little evidence exists to characterize the nature of ED visits or to guide its treatment in the ED. Our objectives were to evaluate the treatment of NVP in the ED and to identify factors that predict return visits to the ED for NVP. Methods We conducted a retrospective database analysis using the electronic medical record from a single, large academic hospital. Demographic and treatment variables were collected using a chart review of 113 ED patient visits with a billing diagnosis of “nausea and vomiting in pregnancy” or “hyperemesis gravidarum.” Logistic regression analysis was used with a primary outcome of return visit to the ED for the same diagnoses. Results There was wide treatment variability of nausea and vomiting in pregnancy patients in the ED. Of the 113 patient visits, 38 (33.6%) had a return ED visit for NVP. High gravidity (OR 1.31, 95% CI [1.06–1.61]), high parity (OR 1.50 95% CI [1.12–2.00]), and early gestational age (OR 0.74 95% CI [0.60–0.90]) were associated with an increase in return ED visits in univariate logistic regression models, while only early gestational age (OR 0.74 95% CI [0.59–0.91]) was associated with increased return ED visits in a multiple regression model. Admission to the hospital was found to decrease the likelihood of return ED visits (p=0.002). Conclusion NVP can be difficult to manage and has a high ED return visit rate. Optimizing care with aggressive, standardized treatment in the ED and upon discharge, particularly if factors predictive of return ED visits are present, may improve quality of care and reduce ED utilization for this condition. PMID:27625723

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Community-Acquired Pneumonia Hospitalization among Children with Neurologic Disorders.

PubMed

Millman, Alexander J; Finelli, Lyn; Bramley, Anna M; Peacock, Georgina; Williams, Derek J; Arnold, Sandra R; Grijalva, Carlos G; Anderson, Evan J; McCullers, Jonathan A; Ampofo, Krow; Pavia, Andrew T; Edwards, Kathryn M; Jain, Seema

2016-06-01

To describe and compare the clinical characteristics, outcomes, and etiology of pneumonia among children hospitalized with community-acquired pneumonia (CAP) with neurologic disorders, non-neurologic underlying conditions, and no underlying conditions. Children <18 years old hospitalized with clinical and radiographic CAP were enrolled at 3 US children's hospitals. Neurologic disorders included cerebral palsy, developmental delay, Down syndrome, epilepsy, non-Down syndrome chromosomal abnormalities, and spinal cord abnormalities. We compared the epidemiology, etiology, and clinical outcomes of CAP in children with neurologic disorders with those with non-neurologic underlying conditions, and those with no underlying conditions using bivariate, age-stratified, and multivariate logistic regression analyses. From January 2010-June 2012, 2358 children with radiographically confirmed CAP were enrolled; 280 (11.9%) had a neurologic disorder (52.1% of these individuals also had non-neurologic underlying conditions), 934 (39.6%) had non-neurologic underlying conditions only, and 1144 (48.5%) had no underlying conditions. Children with neurologic disorders were older and more likely to require intensive care unit (ICU) admission than children with non-neurologic underlying conditions and children with no underlying conditions; similar proportions were mechanically ventilated. In age-stratified analysis, children with neurologic disorders were less likely to have a pathogen detected than children with non-neurologic underlying conditions. In multivariate analysis, having a neurologic disorder was associated with ICU admission for children ≥2 years of age. Children with neurologic disorders hospitalized with CAP were less likely to have a pathogen detected and more likely to be admitted to the ICU than children without neurologic disorders. Published by Elsevier Inc.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Measuring Ventilatory Activity with Structured Light Plethysmography (SLP) Reduces Instrumental Observer Effect and Preserves Tidal Breathing Variability in Healthy and COPD

PubMed Central

Niérat, Marie-Cécile; Dubé, Bruno-Pierre; Llontop, Claudia; Bellocq, Agnès; Layachi Ben Mohamed, Lila; Rivals, Isabelle; Straus, Christian; Similowski, Thomas; Laveneziana, Pierantonio

2017-01-01

The use of a mouthpiece to measure ventilatory flow with a pneumotachograph (PNT) introduces a major perturbation to breathing (“instrumental/observer effect”) and suffices to modify the respiratory behavior. Structured light plethysmography (SLP) is a non-contact method of assessment of breathing pattern during tidal breathing. Firstly, we validated the SLP measurements by comparing timing components of the ventilatory pattern obtained by SLP vs. PNT under the same condition; secondly, we compared SLP to SLP+PNT measurements of breathing pattern to evaluate the disruption of breathing pattern and breathing variability in healthy and COPD subjects. Measurements were taken during tidal breathing with SLP alone and SLP+PNT recording in 30 COPD and healthy subjects. Measurements included: respiratory frequency (Rf), inspiratory, expiratory, and total breath time/duration (Ti, Te, and Tt). Passing-Bablok regression analysis was used to evaluate the interchangeability of timing components of the ventilatory pattern (Rf, Ti, Te, and Tt) between measurements performed under the following experimental conditions: SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT. The variability of different ventilatory variables was assessed through their coefficients of variation (CVs). In healthy: according to Passing-Bablok regression, Rf, TI, TE and TT were interchangeable between measurements obtained under the three experimental conditions (SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT). All the CVs describing “traditional” ventilatory variables (Rf, Ti, Te, Ti/Te, and Ti/Tt) were significantly smaller in SLP+PNT condition. This was not the case for more “specific” SLP-derived variables. In COPD: according to Passing-Bablok regression, Rf, TI, TE, and TT were interchangeable between measurements obtained under SLP vs. PNT and SLP+PNT vs. PNT, whereas only Rf, TE, and TT were interchangeable between measurements obtained under SLP+PNT vs. SLP. However, most discrete variables were significantly different between the SLP and SLP+PNT conditions and CVs were significantly lower when COPD patients were assessed in the SLP+PNT condition. Measuring ventilatory activity with SLP preserves resting tidal breathing variability, reduces instrumental observer effect and avoids any disruptions in breathing pattern induced by the use of PNT-mouthpiece-nose-clip combination. PMID:28572773

Measuring Ventilatory Activity with Structured Light Plethysmography (SLP) Reduces Instrumental Observer Effect and Preserves Tidal Breathing Variability in Healthy and COPD.

PubMed

Niérat, Marie-Cécile; Dubé, Bruno-Pierre; Llontop, Claudia; Bellocq, Agnès; Layachi Ben Mohamed, Lila; Rivals, Isabelle; Straus, Christian; Similowski, Thomas; Laveneziana, Pierantonio

2017-01-01

The use of a mouthpiece to measure ventilatory flow with a pneumotachograph (PNT) introduces a major perturbation to breathing ("instrumental/observer effect") and suffices to modify the respiratory behavior. Structured light plethysmography (SLP) is a non-contact method of assessment of breathing pattern during tidal breathing. Firstly, we validated the SLP measurements by comparing timing components of the ventilatory pattern obtained by SLP vs. PNT under the same condition; secondly, we compared SLP to SLP+PNT measurements of breathing pattern to evaluate the disruption of breathing pattern and breathing variability in healthy and COPD subjects. Measurements were taken during tidal breathing with SLP alone and SLP+PNT recording in 30 COPD and healthy subjects. Measurements included: respiratory frequency (R f ), inspiratory, expiratory, and total breath time/duration (Ti, Te, and Tt). Passing-Bablok regression analysis was used to evaluate the interchangeability of timing components of the ventilatory pattern (R f , Ti, Te, and Tt) between measurements performed under the following experimental conditions: SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT. The variability of different ventilatory variables was assessed through their coefficients of variation (CVs). In healthy: according to Passing-Bablok regression, Rf, TI, TE and TT were interchangeable between measurements obtained under the three experimental conditions (SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT). All the CVs describing "traditional" ventilatory variables (R f , Ti, Te, Ti/Te, and Ti/Tt) were significantly smaller in SLP+PNT condition. This was not the case for more "specific" SLP-derived variables. In COPD: according to Passing-Bablok regression, Rf, TI, TE, and TT were interchangeable between measurements obtained under SLP vs. PNT and SLP+PNT vs. PNT, whereas only Rf, TE, and TT were interchangeable between measurements obtained under SLP+PNT vs. SLP. However, most discrete variables were significantly different between the SLP and SLP+PNT conditions and CVs were significantly lower when COPD patients were assessed in the SLP+PNT condition. Measuring ventilatory activity with SLP preserves resting tidal breathing variability, reduces instrumental observer effect and avoids any disruptions in breathing pattern induced by the use of PNT-mouthpiece-nose-clip combination.

Regression Analysis of Stage Variability for West-Central Florida Lakes

USGS Publications Warehouse

Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy

2008-01-01

The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground-water exchange in controlling the stage of karst lakes in Florida. Regression equations were used to predict lake-stage variability for the recent period for 12 additional lakes, and the median difference between predicted and observed values ranged from 11 to 23 percent. Coefficients of determination for the historical period were considerably lower (maximum R2 of 0.28) than for the recent period. Reasons for these low R2 values are probably related to the small number of lakes (20) with stage data for an equivalent time period that were unaffected by ground-water pumping, the similarity of many of the lake types (large surface-water drainage lakes), and the greater uncertainty in defining historical basin characteristics. The lack of lake-stage data unaffected by ground-water pumping and the poor regression results obtained for that group of lakes limit the ability to predict natural lake-stage variability using this method in west-central Florida.

Inclusion of Exercise Intensities Above the Lactate Threshold in VO2/Running Speed Regression Does not Improve the Precision of Accumulated Oxygen Deficit Estimation in Endurance-Trained Runners

PubMed Central

Reis, Victor M.; Silva, António J.; Ascensão, António; Duarte, José A.

2005-01-01

The present study intended to verify if the inclusion of intensities above lactate threshold (LT) in the VO2/running speed regression (RSR) affects the estimation error of accumulated oxygen deficit (AOD) during a treadmill running performed by endurance-trained subjects. Fourteen male endurance-trained runners performed a sub maximal treadmill running test followed by an exhaustive supra maximal test 48h later. The total energy demand (TED) and the AOD during the supra maximal test were calculated from the RSR established on first testing. For those purposes two regressions were used: a complete regression (CR) including all available sub maximal VO2 measurements and a sub threshold regression (STR) including solely the VO2 values measured during exercise intensities below LT. TED mean values obtained with CR and STR were not significantly different under the two conditions of analysis (177.71 ± 5.99 and 174.03 ± 6.53 ml·kg-1, respectively). Also the mean values of AOD obtained with CR and STR did not differ under the two conditions (49.75 ± 8.38 and 45.8 9 ± 9.79 ml·kg-1, respectively). Moreover, the precision of those estimations was also similar under the two procedures. The mean error for TED estimation was 3.27 ± 1.58 and 3.41 ± 1.85 ml·kg-1 (for CR and STR, respectively) and the mean error for AOD estimation was 5.03 ± 0.32 and 5.14 ± 0.35 ml·kg-1 (for CR and STR, respectively). The results indicated that the inclusion of exercise intensities above LT in the RSR does not improve the precision of the AOD estimation in endurance-trained runners. However, the use of STR may induce an underestimation of AOD comparatively to the use of CR. Key Points It has been suggested that the inclusion of exercise intensities above the lactate threshold in the VO2/power regression can significantly affect the estimation of the energy cost and, thus, the estimation of the AOD. However data on the precision of those AOD measurements is rarely provided. We have evaluated the effects of the inclusion of those exercise intensities on the AOD precision. The results have indicated that the inclusion of exercise intensities above the lactate threshold in the VO2/running speed regression does not improve the precision of AOD estimation in endurance-trained runners. However, the use of sub threshold regressions may induce an underestimation of AOD comparatively to the use of complete regressions. PMID:24501560

Advertising for Demand Creation for Voluntary Medical Male Circumcision.

PubMed

Wilson, Nicholas; Frade, Sasha; Rech, Dino; Friedman, Willa

2016-08-15

To measure the effects of information, a challenge, and a conditional cash transfer on take-up of voluntary medical male circumcision (VMMC). A randomized, controlled experiment with 4000 postcard recipients in Soweto (Johannesburg), South Africa. We examined differences in take-up of several decisions in the VMMC cascade between the control arm and each of several intervention arms using logistic regression. Logistic regression analysis indicated that the group offered US $10 as compensation and the group challenged with "Are you tough enough?" had significantly higher take-up of the VMMC procedure than did the control group [odds ratios, respectively, 5.30 (CI: 2.20 to 12.76) and 2.70 (CI: 1.05 to 6.91)]. Similarly, the compensation group had significantly higher take-up of the VMMC counseling session than did the control group [odds ratio 3.76 (CI: 1.79 to 7.89)]. The analysis did not reveal significantly different take-up of either the VMMC counseling session or the procedure in the partner preference information group compared with the control group [odds ratios, respectively, 1.23 (CI: 0.51 to 2.97) and 1.67 (CI: 0.61 to 4.62)]. The analysis did not reveal significantly higher take-up of the VMMC nurse hotline in any intervention group compared with the control group [odds ratios for US $10, information, and challenge, respectively, 1.17 (CI: 0.67 to 2.07), 0.69 (CI: 0.36 to 1.32), and 0.60 (0.31 to 1.18)]. Among adult males in Soweto, South Africa, compensation of US $10 provided conditional on completing the VMMC counseling session compared with no compensation offer and a postcard with a challenge, "Are you tough enough?" compared with no challenge, resulted in moderate increases in take-up of circumcision.

Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation

DOE PAGES

Ma, Fangfang; Jazmin, Lara J.; Young, Jamey D.; ...

2014-11-03

Improving plant productivity is an important aim for metabolic engineering. There are few comprehensive methods that quantitatively describe leaf metabolism, although such information would be valuable for increasing photosynthetic capacity, enhancing biomass production, and rerouting carbon flux toward desirable end products. Isotopically nonstationary metabolic flux analysis (INST-MFA) has been previously applied to map carbon fluxes in photoautotrophic bacteria, which involves model-based regression of transient 13C-labeling patterns of intracellular metabolites. However, experimental and computational difficulties have hindered its application to terrestrial plant systems. Here, we performed in vivo isotopic labeling of Arabidopsis thaliana rosettes with 13CO 2 and estimated fluxes throughoutmore » leaf photosynthetic metabolism by INST-MFA. Plants grown at 200 µmol m $-$2s $-$1 light were compared with plants acclimated for 9 d at an irradiance of 500 µmol∙m $-$2∙s $-$1. Approximately 1,400 independent mass isotopomer measurements obtained from analysis of 37 metabolite fragment ions were regressed to estimate 136 total fluxes (54 free fluxes) under each condition. The results provide a comprehensive description of changes in carbon partitioning and overall photosynthetic flux after long-term developmental acclimation of leaves to high light. Despite a doubling in the carboxylation rate, the photorespiratory flux increased from 17 to 28% of net CO 2 assimilation with high-light acclimation (Vc/Vo: 3.5:1 vs. 2.3:1, respectively). In conclusion, this study highlights the potential of 13C INST-MFA to describe emergent flux phenotypes that respond to environmental conditions or plant physiology and cannot be obtained by other complementary approaches.« less

Understanding differences between caregivers and non-caregivers in completer rates of Chronic Disease Self-Management Program.

PubMed

Shi, J; McCallion, P; Ferretti, L A

2017-06-01

The Chronic Disease Self-Management Program (CDSMP) was developed to advance participants' self-care of chronic illness and may be offered to both individuals with chronic conditions and their caregivers. Previous studies of CDSMP have identified multiple resulting health benefits for participants as well as factors associated with participants' completion rates. This study investigated differences on these issues between caregiving and non-caregiving participants. Secondary analysis using regression analysis to predict the outcome. Baseline data were collected directly from adult (over 18 years) participants of CDSMP workshops in New York State from 2012 to 2015 (n = 2685). Multi-level logistic regression analysis was used to compare the difference on completion of workshops (attended four or more of sessions) and contributing factors with the independent variable of whether participants provided care/assistance to a family member or friends with long-term illness or disability. Additional individual-level variables controlled for in the model were age, gender, race/ethnicity, living arrangement, education, the number of chronic conditions and disabilities; as were workshop-level characteristics of class size, language used, workshop leader experience, location urbanity and delivery site type. Participants who provided care to family or friends were 28% more likely to complete the workshop compared with those who did not (odds ratio = 1.279, P < 0.05). Different factors influenced the completion of CDSMP workshop for caregivers and non-caregivers. People who provide care to others appeared to have stronger motivation to complete the workshops with greater benefits. Agencies offering CDSMP should encourage caregivers to attend. Copyright © 2017. Published by Elsevier Ltd.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Examination of Parameters Affecting the House Prices by Multiple Regression Analysis and its Contributions to Earthquake-Based Urban Transformation

NASA Astrophysics Data System (ADS)

Denli, H. H.; Durmus, B.

2016-12-01

The purpose of this study is to examine the factors which may affect the apartment prices with multiple linear regression analysis models and visualize the results by value maps. The study is focused on a county of Istanbul - Turkey. Totally 390 apartments around the county Umraniye are evaluated due to their physical and locational conditions. The identification of factors affecting the price of apartments in the county with a population of approximately 600k is expected to provide a significant contribution to the apartment market.Physical factors are selected as the age, number of rooms, size, floor numbers of the building and the floor that the apartment is positioned in. Positional factors are selected as the distances to the nearest hospital, school, park and police station. Totally ten physical and locational parameters are examined by regression analysis.After the regression analysis has been performed, value maps are composed from the parameters age, price and price per square meters. The most significant of the composed maps is the price per square meters map. Results show that the location of the apartment has the most influence to the square meter price information of the apartment. A different practice is developed from the composed maps by searching the ability of using price per square meters map in urban transformation practices. By marking the buildings older than 15 years in the price per square meters map, a different and new interpretation has been made to determine the buildings, to which should be given priority during an urban transformation in the county.This county is very close to the North Anatolian Fault zone and is under the threat of earthquakes. By marking the apartments older than 15 years on the price per square meters map, both older and expensive square meters apartments list can be gathered. By the help of this list, the priority could be given to the selected higher valued old apartments to support the economy of the country during an earthquake loss. We may call this urban transformation as earthquake-based urban transformation.

Comparative study of human blood Raman spectra and biochemical analysis of patients with cancer

NASA Astrophysics Data System (ADS)

Shamina, Lyudmila A.; Bratchenko, Ivan A.; Artemyev, Dmitry N.; Myakinin, Oleg O.; Moryatov, Alexander A.; Orlov, Andrey E.; Kozlov, Sergey V.; Zakharov, Valery P.

2018-04-01

In this study we measured spectral features of blood by Raman spectroscopy. Correlation of the obtained spectral data and biochemical studies results is investigated. Analysis of specific spectra allows for identification of informative spectral bands proportional to components whose content is associated with body fluids homeostasis changes at various pathological conditions. Regression analysis of the obtained spectral data allows for discriminating the lung cancer from other tumors with a posteriori probability of 88.3%. The potentiality of applying surface-enhanced Raman spectroscopy with utilized experimental setup for further studies of the body fluids component composition was estimated. The greatest signal amplification was achieved for the gold substrate with a surface roughness of 1 μm. In general, the developed approach of body fluids analysis provides the basis of a useful and minimally invasive method of pathologies screening.

Multivariate and geo-spatial approach for seawater quality of Chidiyatappu Bay, south Andaman Islands, India.

PubMed

Jha, Dilip Kumar; Vinithkumar, Nambali Valsalan; Sahu, Biraja Kumar; Dheenan, Palaiya Sukumaran; Das, Apurba Kumar; Begum, Mehmuna; Devi, Marimuthu Prashanthi; Kirubagaran, Ramalingam

2015-07-15

Chidiyatappu Bay is one of the least disturbed marine environments of Andaman & Nicobar Islands, the union territory of India. Oceanic flushing from southeast and northwest direction is prevalent in this bay. Further, anthropogenic activity is minimal in the adjoining environment. Considering the pristine nature of this bay, seawater samples collected from 12 sampling stations covering three seasons were analyzed. Principal Component Analysis (PCA) revealed 69.9% of total variance and exhibited strong factor loading for nitrite, chlorophyll a and phaeophytin. In addition, analysis of variance (ANOVA-one way), regression analysis, box-whisker plots and Geographical Information System based hot spot analysis further simplified and supported multivariate results. The results obtained are important to establish reference conditions for comparative study with other similar ecosystems in the region. Copyright © 2015 Elsevier Ltd. All rights reserved.

MetaComp: comprehensive analysis software for comparative meta-omics including comparative metagenomics.

PubMed

Zhai, Peng; Yang, Longshu; Guo, Xiao; Wang, Zhe; Guo, Jiangtao; Wang, Xiaoqi; Zhu, Huaiqiu

2017-10-02

During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community. We developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function-regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples. MetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: http://cqb.pku.edu.cn/ZhuLab/MetaComp/ and https://github.com/pzhaipku/MetaComp/ .

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Dynamic travel time estimation using regression trees.

DOT National Transportation Integrated Search

2008-10-01

This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

Using exogenous variables in testing for monotonic trends in hydrologic time series

USGS Publications Warehouse

Alley, William M.

1988-01-01

One approach that has been used in performing a nonparametric test for monotonic trend in a hydrologic time series consists of a two-stage analysis. First, a regression equation is estimated for the variable being tested as a function of an exogenous variable. A nonparametric trend test such as the Kendall test is then performed on the residuals from the equation. By analogy to stagewise regression and through Monte Carlo experiments, it is demonstrated that this approach will tend to underestimate the magnitude of the trend and to result in some loss in power as a result of ignoring the interaction between the exogenous variable and time. An alternative approach, referred to as the adjusted variable Kendall test, is demonstrated to generally have increased statistical power and to provide more reliable estimates of the trend slope. In addition, the utility of including an exogenous variable in a trend test is examined under selected conditions.

Prediction of elemental creep. [steady state and cyclic data from regression analysis

NASA Technical Reports Server (NTRS)

Davis, J. W.; Rummler, D. R.

1975-01-01

Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.

Analytical and regression models of glass rod drawing process

NASA Astrophysics Data System (ADS)

Alekseeva, L. B.

2018-03-01

The process of drawing glass rods (light guides) is being studied. The parameters of the process affecting the quality of the light guide have been determined. To solve the problem, mathematical models based on general equations of continuum mechanics are used. The conditions for the stable flow of the drawing process have been found, which are determined by the stability of the motion of the glass mass in the formation zone to small uncontrolled perturbations. The sensitivity of the formation zone to perturbations of the drawing speed and viscosity is estimated. Experimental models of the drawing process, based on the regression analysis methods, have been obtained. These models make it possible to customize a specific production process to obtain light guides of the required quality. They allow one to find the optimum combination of process parameters in the chosen area and to determine the required accuracy of maintaining them at a specified level.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

Assessment of heart rate variability based on mobile device for planning physical activity

NASA Astrophysics Data System (ADS)

Svirin, I. S.; Epishina, E. V.; Voronin, V. V.; Semenishchev, E. A.; Solodova, E. N.; Nabilskaya, N. V.

2015-05-01

In this paper we present a method for the functional analysis of human heart based on electrocardiography (ECG) signals. The approach using the apparatus of analytical and differential geometry and correlation and regression analysis. ECG contains information on the current condition of the cardiovascular system as well as on the pathological changes in the heart. Mathematical processing of the heart rate variability allows to obtain a great set of mathematical and statistical characteristics. These characteristics of the heart rate are used when solving research problems to study physiological changes that determine functional changes of an individual. The proposed method implemented for up-to-date mobile Android and iOS based devices.

Applying Regression Analysis to Problems in Institutional Research.

ERIC Educational Resources Information Center

Bohannon, Tom R.

1988-01-01

Regression analysis is one of the most frequently used statistical techniques in institutional research. Principles of least squares, model building, residual analysis, influence statistics, and multi-collinearity are described and illustrated. (Author/MSE)

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

PubMed Central

Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.

2016-01-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis. PMID:27274911

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

PubMed

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate/n-Dodecane/Nitric Acid Solvent

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

Tatiana G. Levitskaia; James M. Peterson; Emily L. Campbell

2013-12-01

In liquid–liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness, and frequent solvent analysis is warranted. Our research explores the feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutylphosphoric acid (HDBP) was assessed. Fourier transform infrared (FTIR)more » spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to high-dose external ?-irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus, demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.« less

Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate /n-Dodecane/Nitric Acid Solvent

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

Levitskaia, Tatiana G.; Peterson, James M.; Campbell, Emily L.

2013-11-05

In liquid-liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness and frequent solvent analysis is warranted. Our research explores feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutyl phosphoric acid (HDBP) was assessed. Fourier Transform Infrared Spectroscopymore » (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to the high dose external gamma irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.« less

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

Interpreting Bivariate Regression Coefficients: Going beyond the Average

ERIC Educational Resources Information Center

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Effect of compression ratio, nozzle opening pressure, engine load, and butanol addition on nanoparticle emissions from a non-road diesel engine.

PubMed

Maurya, Rakesh Kumar; Saxena, Mohit Raj; Rai, Piyush; Bhardwaj, Aashish

2018-05-01

Currently, diesel engines are more preferred over gasoline engines due to their higher torque output and fuel economy. However, diesel engines confront major challenge of meeting the future stringent emission norms (especially soot particle emissions) while maintaining the same fuel economy. In this study, nanosize range soot particle emission characteristics of a stationary (non-road) diesel engine have been experimentally investigated. Experiments are conducted at a constant speed of 1500 rpm for three compression ratios and nozzle opening pressures at different engine loads. In-cylinder pressure history for 2000 consecutive engine cycles is recorded and averaged data is used for analysis of combustion characteristics. An electrical mobility-based fast particle sizer is used for analyzing particle size and mass distributions of engine exhaust particles at different test conditions. Soot particle distribution from 5 to 1000 nm was recorded. Results show that total particle concentration decreases with an increase in engine operating loads. Moreover, the addition of butanol in the diesel fuel leads to the reduction in soot particle concentration. Regression analysis was also conducted to derive a correlation between combustion parameters and particle number emissions for different compression ratios. Regression analysis shows a strong correlation between cylinder pressure-based combustion parameters and particle number emission.

Visuoconstructional Impairment in Subtypes of Mild Cognitive Impairment

PubMed Central

Ahmed, Samrah; Brennan, Laura; Eppig, Joel; Price, Catherine C.; Lamar, Melissa; Delano-Wood, Lisa; Bangen, Katherine J.; Edmonds, Emily C.; Clark, Lindsey; Nation, Daniel A.; Jak, Amy; Au, Rhoda; Swenson, Rodney; Bondi, Mark W.; Libon, David J.

2018-01-01

Clock Drawing Test performance was examined alongside other neuropsychological tests in mild cognitive impairment (MCI). We tested the hypothesis that clock-drawing errors are related to executive impairment. The current research examined 86 patients with MCI for whom, in prior research, cluster analysis was used to sort patients into dysexecutive (dMCI, n=22), amnestic (aMCI, n=13), and multi-domain (mMCI, n=51) subtypes. First, principal components analysis (PCA) and linear regression examined relations between clock-drawing errors and neuropsychological test performance independent of MCI subtype. Second, between-group differences were assessed with analysis of variance (ANOVA) where MCI subgroups were compared to normal controls (NC). PCA yielded a 3-group solution. Contrary to expectations, clock-drawing errors loaded with lower performance on naming/lexical retrieval, rather than with executive tests. Regression analyses found increasing clock-drawing errors to command were associated with worse performance only on naming/lexical retrieval tests. ANOVAs revealed no differences in clock-drawing errors between dMCI versus mMCI or aMCI versus NCs. Both the dMCI and mMCI groups generated more clock-drawing errors than the aMCI and NC groups in the command condition. In MCI, language-related skills contribute to clock-drawing impairment. PMID:26397732

Agreement evaluation of AVHRR and MODIS 16-day composite NDVI data sets

USGS Publications Warehouse

Ji, Lei; Gallo, Kevin P.; Eidenshink, Jeffery C.; Dwyer, John L.

2008-01-01

Satellite-derived normalized difference vegetation index (NDVI) data have been used extensively to detect and monitor vegetation conditions at regional and global levels. A combination of NDVI data sets derived from AVHRR and MODIS can be used to construct a long NDVI time series that may also be extended to VIIRS. Comparative analysis of NDVI data derived from AVHRR and MODIS is critical to understanding the data continuity through the time series. In this study, the AVHRR and MODIS 16-day composite NDVI products were compared using regression and agreement analysis methods. The analysis shows a high agreement between the AVHRR-NDVI and MODIS-NDVI observed from 2002 and 2003 for the conterminous United States, but the difference between the two data sets is appreciable. Twenty per cent of the total difference between the two data sets is due to systematic difference, with the remainder due to unsystematic difference. The systematic difference can be eliminated with a linear regression-based transformation between two data sets, and the unsystematic difference can be reduced partially by applying spatial filters to the data. We conclude that the continuity of NDVI time series from AVHRR to MODIS is satisfactory, but a linear transformation between the two sets is recommended.

Dimensional analysis of detrimental ozone generation by positive wire-to-plate corona discharge in air

NASA Astrophysics Data System (ADS)

Bo, Z.; Chen, J. H.

2010-02-01

The dimensional analysis technique is used to formulate a correlation between ozone generation rate and various parameters that are important in the design and operation of positive wire-to-plate corona discharges in indoor air. The dimensionless relation is determined by linear regression analysis based on the results from 36 laboratory-scale experiments. The derived equation is validated by experimental data and a numerical model published in the literature. Applications of such derived equation are illustrated through an example selection of the appropriate set of operating conditions in the design/operation of a photocopier to follow the federal regulations of ozone emission. Finally, a new current-voltage characteristic equation is proposed for positive wire-to-plate corona discharges based on the derived dimensionless equation.