#### Sample records for conditions multivariate biasing

1. ibr: Iterative bias reduction multivariate smoothing

SciTech Connect

Hengartner, Nicholas W; Cornillon, Pierre-andre; Matzner - Lober, Eric

2009-01-01

Regression is a fundamental data analysis tool for relating a univariate response variable Y to a multivariate predictor X {element_of} E R{sup d} from the observations (X{sub i}, Y{sub i}), i = 1,...,n. Traditional nonparametric regression use the assumption that the regression function varies smoothly in the independent variable x to locally estimate the conditional expectation m(x) = E[Y|X = x]. The resulting vector of predicted values {cflx Y}{sub i} at the observed covariates X{sub i} is called a regression smoother, or simply a smoother, because the predicted values {cflx Y}{sub i} are less variable than the original observations Y{sub i}. Linear smoothers are linear in the response variable Y and are operationally written as {cflx m} = X{sub {lambda}}Y, where S{sub {lambda}} is a n x n smoothing matrix. The smoothing matrix S{sub {lambda}} typically depends on a tuning parameter which we denote by {lambda}, and that governs the tradeoff between the smoothness of the estimate and the goodness-of-fit of the smoother to the data by controlling the effective size of the local neighborhood over which the responses are averaged. We parameterize the smoothing matrix such that large values of {lambda} are associated to smoothers that averages over larger neighborhood and produce very smooth curves, while small {lambda} are associated to smoothers that average over smaller neighborhood to produce a more wiggly curve that wants to interpolate the data. The parameter {lambda} is the bandwidth for kernel smoother, the span size for running-mean smoother, bin smoother, and the penalty factor {lambda} for spline smoother.

2. Diamond nucleation under bias conditions

SciTech Connect

Stoeckel, R.; Stammler, M.; Janischowsky, K.; Ley, L.; Albrecht, M.; Strunk, H.P.

1998-01-01

The so-called bias pretreatment allows the growth of heteroepitaxial diamond films by plasma chemical vapor deposition on silicon (100) surfaces. We present plan-view and cross-sectional transmission electron micrographs of the substrate surface at different phases of the bias pretreatment. These observations are augmented by measurements of the etch rates of Si, SiC, and different carbon modifications under plasma conditions and the size distribution of oriented diamond crystals grown after bias pretreatment. Based on these results a new model for diamond nucleation under bias conditions is proposed. First, a closed layer of nearly epitaxially oriented cubic SiC with a thickness of about 10 nm is formed. Subplantation of carbon into this SiC layer causes a supersaturation with carbon and results in the subcutaneous formation of epitaxially oriented nucleation centers in the SiC layer. Etching of the SiC during the bias pretreatment as well as during diamond growth brings these nucleation centers to the sample surface and causes the growth of diamonds epitaxially oriented on the Si/SiC substrate. {copyright} {ital 1998 American Institute of Physics.}

3. A direct-gradient multivariate index of biotic condition

USGS Publications Warehouse

Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

2012-01-01

Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

4. Bias correction of multi-variate indices: Heat stress in Switzerland

Casanueva, Ana; Kotlarski, Sven; Liniger, Mark A.

2017-04-01

High resolution regional climate model (RCM) simulations are important tools to provide the meteorological variables required in climate impact assessments. Despite the advances towards higher spatial resolution and better resolved physical processes, RCMs often cannot be directly used in impact studies due to their partly substantial biases. In the climate change context, distributional bias correction (BC) methods are frequently used to deal with systematic model biases. BC methods can correct either some parameters of the distribution (e.g. the mean via distributional shift or scaling) or all quantiles (e.g. via the empirical quantile mapping). The empirical quantile mapping (QM) is widely used in the literature to bias correct individual variables (e.g. temperature, precipitation), in a few cases also for variables such as humidity or wind. In the present work we analyze the suitability of QM to derive a multi-variate index (the wet bulb temperature, WBT) from the new generation of climate change scenarios for Switzerland (CH2018). WBT is a relatively simple proxy for heat stress on the human body. It is a simple, but non-linear multi-variate index that depends on temperature and humidity. Since extreme heat stress conditions occur at sub-daily scale but only daily values are usually available from RCMs, we analyze the sensitivity of the WBT to the use of different daily aggregated values in its calculation, compared to the maximum WBT obtained from observed hourly data. Further, we show that the separate correction of temperature and humidity allows reproduction of the distribution of the daily maximum WBT. Additionally, we explore climate change projections of WBT comparing the results from bias corrected and raw RCM data using a selection of EURO-CORDEX RCM simulations.

5. Multivariate Bias Correction Procedures for Improving Water Quality Predictions using Mechanistic Models

Libera, D.; Arumugam, S.

2015-12-01

Water quality observations are usually not available on a continuous basis because of the expensive cost and labor requirements so calibrating and validating a mechanistic model is often difficult. Further, any model predictions inherently have bias (i.e., under/over estimation) and require techniques that preserve the long-term mean monthly attributes. This study suggests and compares two multivariate bias-correction techniques to improve the performance of the SWAT model in predicting daily streamflow, TN Loads across the southeast based on split-sample validation. The first approach is a dimension reduction technique, canonical correlation analysis that regresses the observed multivariate attributes with the SWAT model simulated values. The second approach is from signal processing, importance weighting, that applies a weight based off the ratio of the observed and model densities to the model data to shift the mean, variance, and cross-correlation towards the observed values. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are also compared with independent estimates from the USGS LOADEST model. Uncertainties in the bias-corrected estimates due to limited water quality observations are also discussed.

6. Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

7. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

2012-07-01

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

8. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis.

PubMed

Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

2012-07-01

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission (P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

9. Conditional bias-penalized optimal estimation for QPE

Seo, D.

2011-12-01

Most precipitation estimation techniques employ some form of optimal estimation, which usually targets unbiasedness and minimum error variance. Because these properties generally hold only in the unconditional sense, the resulting estimates are subject to conditional biases that may be unacceptably large. A prime example is precipitation analysis using rain gauge data for which, e.g., kriging may significantly underestimate heavy precipitation and, albeit less consequentially, overestimate very light precipitation. In this presentation, we introduce an extremely simple extension to the widely used optimal estimation techniques of simple and ordinary kriging, referred to herein as conditional bias-penalized kriging (CBPK), which minimizes explicitly conditional bias in addition to unconditional error variance. To understand the properties and performance characteristics of CBPK, we carried out numerical experiments in which normal and lognormal random fields of varying spatial correlation scale and rain gauge network density are synthetically generated, and the estimates are cross-validated; the results are summarized in this presentation. Also presented are generalization of CBPK in the framework of classical optimal linear estimation theory, and how it may be used in multisensor QPE.

10. Pseudo-observations for "soft" bias correction aimed at multi-variable impact studies

Berg, Peter; Bosshard, Thoams; Yang, Wei

2017-04-01

Correcting biases in climate model simulations is likely to affect climate change signals, which is not necessarily justified. Observations have high uncertainty in themselves, and in the case of gridded observations they should be treated as models rather than an actual truth. One might, e.g., be better off by retaining small scale information from a climate model, rather than imposing that from interpolated gauge observations with some more or less sparse gauge network. For some variables, there are even too few gauges, such that a spatial grid is not supported. Keeping the model solution might then be a better approach than attempting to correct the climate model to spatially incorrect data. Here, we present a method for creating a pseudo-observational data set which retains small scale features from a core regional climate model simulation, while correcting larger scale features according to different gridded or purely gauge based data sets. Then, a set of regional climate projections are bias corrected toward the pseudo-observational data, and applied for an assessment of future fire risk for the forests of Sweden. The applied fire weather model takes daily precipitation and the 1200UTC value of temperature, wind and relative humidity, which makes it a sensitive system to different bias correction approaches.

11. Stochastic bias from non-Gaussian initial conditions

SciTech Connect

Baumann, Daniel; Ferraro, Simone; Smith, Kendrick M.; Green, Daniel E-mail: sferraro@princeton.edu E-mail: kmsmith@astro.princeton.edu

2013-05-01

In this article, we show that a stochastic form of scale-dependent halo bias arises in multi-source inflationary models, where multiple fields determine the initial curvature perturbation. We derive this effect for general non-Gaussian initial conditions and study various examples, such as curvaton models and quasi-single field inflation. We present a general formula for both the stochastic and the non-stochastic parts of the halo bias, in terms of the N-point cumulants of the curvature perturbation at the end of inflation. At lowest order, the stochasticity arises if the collapsed limit of the four-point function is boosted relative to the square of the three-point function in the squeezed limit. We derive all our results in two ways, using the barrier crossing formalism and the peak-background split method. In a companion paper [1], we prove that these two approaches are mathematically equivalent.

12. Visual Bias Predicts Gait Adaptability in Novel Sensory Discordant Conditions

NASA Technical Reports Server (NTRS)

Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

2010-01-01

We designed a gait training study that presented combinations of visual flow and support-surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their postural stability and cognitive performance in a new discordant environment presented at the conclusion of training (Transfer Test). Our training system comprised a treadmill placed on a motion base facing a virtual visual scene that provided a variety of sensory challenges. Ten healthy adults completed 3 training sessions during which they walked on a treadmill at 1.1 m/s while receiving discordant support-surface and visual manipulations. At the first visit, in an analysis of normalized torso translation measured in a scene-movement-only condition, 3 of 10 subjects were classified as visually dependent. During the Transfer Test, all participants received a 2-minute novel exposure. In a combined measure of stride frequency and reaction time, the non-visually dependent subjects showed improved adaptation on the Transfer Test compared to their visually dependent counterparts. This finding suggests that individual differences in the ability to adapt to new sensorimotor conditions may be explained by individuals innate sensory biases. An accurate preflight assessment of crewmembers biases for visual dependence could be used to predict their propensities to adapt to novel sensory conditions. It may also facilitate the development of customized training regimens that could expedite adaptation to alternate gravitational environments.

13. Novelty, conditioning and attentional bias to sexual rewards.

PubMed

Banca, Paula; Morris, Laurel S; Mitchell, Simon; Harrison, Neil A; Potenza, Marc N; Voon, Valerie

2016-01-01

14. Novelty, conditioning and attentional bias to sexual rewards

PubMed Central

Banca, Paula; Morris, Laurel S.; Mitchell, Simon; Harrison, Neil A.; Potenza, Marc N.; Voon, Valerie

2016-01-01

The Internet provides a large source of novel and rewarding stimuli, particularly with respect to sexually explicit materials. Novelty-seeking and cue-conditioning are fundamental processes underlying preference and approach behaviors implicated in disorders of addiction. Here we examine these processes in individuals with compulsive sexual behaviors (CSB), hypothesizing a greater preference for sexual novelty and stimuli conditioned to sexual rewards relative to healthy volunteers. Twenty-two CSB males and forty age-matched male volunteers were tested in two separate behavioral tasks focusing on preferences for novelty and conditioned stimuli. Twenty subjects from each group were also assessed in a third conditioning and extinction task using functional magnetic resonance imaging. CSB was associated with enhanced novelty preference for sexual, as compared to control images, and a generalized preference for cues conditioned to sexual and monetary versus neutral outcomes compared to healthy volunteers. CSB individuals also had greater dorsal cingulate habituation to repeated sexual versus monetary images with the degree of habituation correlating with enhanced preference for sexual novelty. Approach behaviors to sexually conditioned cues dissociable from novelty preference were associated with an early attentional bias to sexual images. This study shows that CSB individuals have a dysfunctional enhanced preference for sexual novelty possibly mediated by greater cingulate habituation along with a generalized enhancement of conditioning to rewards. We further emphasize a dissociable role for cue-conditioning and novelty preference on the early attentional bias for sexual cues. These findings have wider relevance as the Internet provides a broad range of novel and potentially rewarding stimuli. PMID:26606725

15. Measurement bias detection with Kronecker product restricted models for multivariate longitudinal data: an illustration with health-related quality of life data from thirteen measurement occasions.

PubMed

Verdam, Mathilde G E; Oort, Frans J

2014-01-01

Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data.A method for the investigation of measurement bias with Kronecker product restricted models.Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions.The use of curves to facilitate substantive interpretation of apparent measurement bias.Assessment of change in common factor means, after accounting for apparent measurement bias.Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks.

16. Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden

Yang, W.; Gardelin, M.; Olsson, J.; Bosshard, T.

2015-09-01

As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

17. Effect of altered sensory conditions on multivariate descriptors of human postural sway

NASA Technical Reports Server (NTRS)

Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1998-01-01

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

18. Effect of altered sensory conditions on multivariate descriptors of human postural sway

NASA Technical Reports Server (NTRS)

Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1998-01-01

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

19. Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random Coefficient-Dependent Missing Longitudinal Data.

PubMed

Gottfredson, Nisha C; Sterba, Sonya K; Jackson, Kristina M

2017-01-01

Random coefficient-dependent (RCD) missingness is a non-ignorable mechanism through which missing data can arise in longitudinal designs. RCD, for which we cannot test, is a problematic form of missingness that occurs if subject-specific random effects correlate with propensity for missingness or dropout. Particularly when covariate missingness is a problem, investigators typically handle missing longitudinal data by using single-level multiple imputation procedures implemented with long-format data, which ignores within-person dependency entirely, or implemented with wide-format (i.e., multivariate) data, which ignores some aspects of within-person dependency. When either of these standard approaches to handling missing longitudinal data is used, RCD missingness leads to parameter bias and incorrect inference. We explain why multilevel multiple imputation (MMI) should alleviate bias induced by a RCD missing data mechanism under conditions that contribute to stronger determinacy of random coefficients. We evaluate our hypothesis with a simulation study. Three design factors are considered: intraclass correlation (ICC; ranging from .25 to .75), number of waves (ranging from 4 to 8), and percent of missing data (ranging from 20 to 50%). We find that MMI greatly outperforms the single-level wide-format (multivariate) method for imputation under a RCD mechanism. For the MMI analyses, bias was most alleviated when the ICC is high, there were more waves of data, and when there was less missing data. Practical recommendations for handling longitudinal missing data are suggested.

20. Comparison of conditional bias-adjusted estimators for interim analysis in clinical trials with survival data.

PubMed

Shimura, Masashi; Gosho, Masahiko; Hirakawa, Akihiro

2017-02-17

Group sequential designs are widely used in clinical trials to determine whether a trial should be terminated early. In such trials, maximum likelihood estimates are often used to describe the difference in efficacy between the experimental and reference treatments; however, these are well known for displaying conditional and unconditional biases. Established bias-adjusted estimators include the conditional mean-adjusted estimator (CMAE), conditional median unbiased estimator, conditional uniformly minimum variance unbiased estimator (CUMVUE), and weighted estimator. However, their performances have been inadequately investigated. In this study, we review the characteristics of these bias-adjusted estimators and compare their conditional bias, overall bias, and conditional mean-squared errors in clinical trials with survival endpoints through simulation studies. The coverage probabilities of the confidence intervals for the four estimators are also evaluated. We find that the CMAE reduced conditional bias and showed relatively small conditional mean-squared errors when the trials terminated at the interim analysis. The conditional coverage probability of the conditional median unbiased estimator was well below the nominal value. In trials that did not terminate early, the CUMVUE performed with less bias and an acceptable conditional coverage probability than was observed for the other estimators. In conclusion, when planning an interim analysis, we recommend using the CUMVUE for trials that do not terminate early and the CMAE for those that terminate early. Copyright © 2017 John Wiley & Sons, Ltd.

1. Linking multimetric and multivariate approaches to assess the ecological condition of streams.

PubMed

Collier, Kevin J

2009-10-01

Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

2. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

PubMed

Chen, Yonghong; Bressler, Steven L; Ding, Mingzhou

2006-01-30

It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

3. A multivariate conditional model for streamflow prediction and spatial precipitation refinement

Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui

2015-10-01

The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

4. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions

2013-07-01

This paper deals with the diagnostics of planetary gearboxes under nonstationary operating conditions. In most diagnostics applications, energy of vibration signals (calculated directly from time series or extracted from spectral representation of signal) is used. Unfortunately energy based features are sensitive to load conditions and it makes diagnostics difficult. In this paper we used energy based 15D data vectors (namely spectral amplitudes of planetary mesh frequency and its harmonics) in order to investigate if it is possible to improve diagnostics efficiency in comparison to previous, one dimensional, approaches proposed for the same problem. Two multivariate methods, Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA), were used as techniques for data analysis. We used these techniques in order to investigate dimensionality of the data and to visualize data in 3D and 2D spaces in order to understand data behavior and assess classification ability. As a case study the data from two planetary gearboxes used in complex mining machines (one in bad condition and the other in good condition) were analyzed. For these two machines more than 2000 15D vectors were acquired. It should be noted that due to non-stationarity of loading conditions, previous diagnostics results obtained using other techniques were moderately good (ca. 80% recognition efficiency); however there is still some need to improve diagnostics classification ability. After application of the proposed approaches it was found that the entire data could be reduced to 2 dimensions whereby data instances became visible and a good discriminant function (characterized by a misclassification rate of .0023, i.e. only 5 erroneous classifications for a total of 2183 instances) could be derived. This paper suggests a novel way for condition monitoring of planetary gearboxes based on multivariate statistics. The emphasis is put on the algebraic and geometric interpretations of the PCA

5. Recursive estimators of mean-areal and local bias in precipitation products that account for conditional bias

Zhang, Yu; Seo, Dong-Jun

2017-03-01

This paper presents novel formulations of Mean field bias (MFB) and local bias (LB) correction schemes that incorporate conditional bias (CB) penalty. These schemes are based on the operational MFB and LB algorithms in the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE). By incorporating CB penalty in the cost function of exponential smoothers, we are able to derive augmented versions of recursive estimators of MFB and LB. Two extended versions of MFB algorithms are presented, one incorporating spatial variation of gauge locations only (MFB-L), and the second integrating both gauge locations and CB penalty (MFB-X). These two MFB schemes and the extended LB scheme (LB-X) are assessed relative to the original MFB and LB algorithms (referred to as MFB-O and LB-O, respectively) through a retrospective experiment over a radar domain in north-central Texas, and through a synthetic experiment over the Mid-Atlantic region. The outcome of the former experiment indicates that introducing the CB penalty to the MFB formulation leads to small, but consistent improvements in bias and CB, while its impacts on hourly correlation and Root Mean Square Error (RMSE) are mixed. Incorporating CB penalty in LB formulation tends to improve the RMSE at high rainfall thresholds, but its impacts on bias are also mixed. The synthetic experiment suggests that beneficial impacts are more conspicuous at low gauge density (9 per 58,000 km2), and tend to diminish at higher gauge density. The improvement at high rainfall intensity is partly an outcome of the conservativeness of the extended LB scheme. This conservativeness arises in part from the more frequent presence of negative eigenvalues in the extended covariance matrix which leads to no, or smaller incremental changes to the smoothed rainfall amounts.

6. Hysteresis Loss Analysis of Soft Magnetic Materials Under Direct Current Bias Conditions (Preprint)

DTIC Science & Technology

2015-09-01

correlated higher losses is not well understood. A domain imaging study under applied fields that uses the conditions reported in this study may...AFRL-RQ-WP-TP-2015-0133 Hysteresis Loss Analysis of Soft Magnetic Materials Under Direct Current Bias Conditions (Preprint) Zafer Turgut...Technical Paper 1 October 2013 to 1 September 2015 4. TITLE AND SUBTITLE Hysteresis Loss Analysis of Soft Magnetic Materials Under Direct Current Bias

7. Bayesian inference of genetic parameters based on conditional decompositions of multivariate normal distributions.

PubMed

Hallander, Jon; Waldmann, Patrik; Wang, Chunkao; Sillanpää, Mikko J

2010-06-01

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.

8. Selection bias due to parity-conditioning in studies of time trends in fertility.

PubMed

Sallmén, Markku; Bonde, Jens Peter; Lindbohm, Marja-Liisa; Kristensen, Petter

2015-01-01

Studies of couple fertility over time have often examined study populations with broad age ranges at a cross-section of time. An increase in fertility has been observed in studies that followed episodes of fertility events either prospectively among nulliparous women or retrospectively among parous women. Fertility has a biological effect on parity. If defined at a cross-section of time, parity will also be affected by year of birth, and thus becomes a collider. Conditioning (stratifying, restricting, or adjusting) on a collider may cause selection bias in the studied association. A study with prospective follow-up was taken as the model to assess the validity of fertility studies. We demonstrate the potential for selection bias using causal graphs and nationwide birth statistics and other demographic data. We tested the existence of parity-conditioning bias in data including both parous and nulliparous women. We also used a simulation approach to assess the strength of the bias in populations with prior at-risk cycles. Finally, we evaluated the potential for selection bias due to conditioning on parity in various sampling frames. Analyses indicate that the observed increase in fertility over time can be entirely explained by selection bias due to parity-conditioning. Heterogeneity in fertility and differential success in prior at-risk cycles are the ultimate factors behind the selection bias. The potential for selection bias due to parity-conditioning varies by sampling frame. A prospective multidecade study with representative sampling of birth cohorts and follow-up from menarche to menopause would bypass the described bias.

9. Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics.

PubMed

Senila, Marin; Levei, Erika Andrea; Senila, Lacrimioara Ramona

2012-10-18

The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141-833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed.

10. Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

PubMed Central

2012-01-01

Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

11. Influence of growth conditions on exchange bias of NiMn-based spin valves

SciTech Connect

Wienecke, Anja; Kruppe, Rahel; Rissing, Lutz

2015-05-07

As shown in previous investigations, a correlation between a NiMn-based spin valve's thermal stability and its inherent exchange bias exists, even if the blocking temperature of the antiferromagnet is clearly above the heating temperature and the reason for thermal degradation is mainly diffusion and not the loss of exchange bias. Samples with high exchange bias are thermally more stable than samples with low exchange bias. Those structures promoting a high exchange bias are seemingly the same suppressing thermally induced diffusion processes (A. Wienecke and L. Rissing, “Relationship between thermal stability and layer-stack/structure of NiMn-based GMR systems,” in IEEE Transaction on Magnetic Conference (EMSA 2014)). Many investigations were carried out on the influence of the sputtering parameters as well as the layer thickness on the magnetoresistive effect. The influence of these parameters on the exchange bias and the sample's thermal stability, respectively, was hardly taken into account. The investigation described here concentrates on the last named issue. The focus lies on the influence of the sputtering parameters and layer thickness of the “starting layers” in the stack and the layers forming the (synthetic) antiferromagnet. This paper includes a guideline for the evaluated sputtering conditions and layer thicknesses to realize a high exchange bias and presumably good thermal stability for NiMn-based spin valves with a synthetic antiferromagnet.

12. Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors

PubMed Central

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression. PMID:27907077

13. Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors.

PubMed

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression.

14. Effect of bias condition on heavy ion radiation in bipolar junction transistors

Liu, Chao-Ming; Li, Xing-Ji; Geng, Hong-Bin; Yang, De-Zhuang; He, Shi-Yu

2012-08-01

The characteristic degradations in a silicon NPN bipolar junction transistor (BJT) of 3DG142 type are examined under irradiation with 40-MeV chlorine (Cl) ions under forward, grounded, and reverse bias conditions, respectively. Different electrical parameters are in-situ measured during the exposure under each bias condition. From the experimental data, a larger variation of base current (IB) is observed after irradiation at a given value of base-emitter voltage (VBE), while the collector current is slightly affected by irradiation at a given VBE. The gain degradation is affected mostly by the behaviour of the base current. From the experimental data, the variation of current gain in the case of forward bias is much smaller than that in the other conditions. Moreover, for 3DG142 BJT, the current gain degradation in the case of reverse bias is more severe than that in the grounded case at low fluence, while at high fluence, the gain degradation in the reverse bias case becomes smaller than that in the grounded case.

15. Observational fear conditioning in the acquisition and extinction of attentional bias for threat: an experimental evaluation.

PubMed

Kelly, Megan M; Forsyth, John P

2007-05-01

Anxious persons show automatic and strategic attentional biases for threatening information. Yet, the mechanisms and processes that underlie such biases remain unclear. The central aim of the present study was to elucidate the relation between observational threat learning and the acquisition and extinction of biased threat processing by integrating emotional Stroop color naming tasks within an observational differential fear conditioning procedure. Forty-three healthy female participants underwent several consecutive observational fear conditioning phases. During acquisition, participants watched a confederate displaying mock panic attacks (UCS) paired with a verbal stimulus (CS+), but not with a second nonreinforced verbal stimulus (CS-). As expected, participants showed greater magnitude electrodermal and verbal-evaluative (e.g., distress, fear) conditioned responses to the CS+ over the CS- word. Participants also demonstrated slower color-naming latencies to CS+ compared to the CS- word following acquisition and showed attenuation of this preferential processing bias for threat following extinction. Findings are discussed broadly in the context of the interplay between fear learning and processing biases for threat as observed in persons suffering from anxiety disorders.

16. The potential for social contextual and group biases in team decision-making: biases, conditions and psychological mechanisms.

PubMed

Jones, P E; Roelofsma, P H

2000-08-01

This paper provides a critical review of social contextual and group biases that are relevant to team decision-making in command and control situations. Motivated by the insufficient level of attention this area has received, the purpose of the paper is to provide an insight into the potential that these types of biases have to affect the decision-making of such teams. The biases considered are: false consensus, groupthink, group polarization and group escalation of commitment. For each bias the following four questions are addressed. What is the descriptive nature of the bias? What factors induce the bias? What psychological mechanisms underlie the bias? What is the relevance of the bias to command and control teams? The analysis suggests that these biases have a strong potential to affect team decisions. Consistent with the nature of team decision-making in command and control situations, all of the biases considered tend to be associated with those decisions that are important or novel and are promoted by time pressure and high levels of uncertainty. A concept unifying these biases is that of the shared mental model, but whereas false consensus emanates from social projection tendencies, the rest emanate from social influence factors. The authors also discuss the 'tricky' distinction between teams and groups and propose a revised definition for command and control team. Finally, the authors emphasize the need for future empirical research in this area to pay additional attention to the social side of cognition and the potential that social biases have to affect team decision-making.

17. Exploration of Temporal ICD Coding Bias Related to Acute Diabetic Conditions

PubMed Central

McKillop, Mollie; Polubriaginof, Fernanda; Weng, Chunhua

2015-01-01

Electronic Health Records (EHRs) hold great promise for secondary data reuse but have been reported to contain severe biases. The temporal characteristics of coding biases remain unclear. This study used a survival analysis approach to reveal temporal bias trends for coding acute diabetic conditions among 268 diabetes patients. For glucose-controlled ketoacidosis patients we found it took an average of 7.5 months for the incorrect code to be removed, while for glucose-controlled hypoglycemic patients it took an average of 9 months. We also examined blood glucose lab values and performed a case review to confirm the validity of our findings. We discuss the implications of our findings and propose future work. PMID:26958300

18. Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

19. N-body simulations with generic non-Gaussian initial conditions II: halo bias

SciTech Connect

Wagner, Christian; Verde, Licia E-mail: liciaverde@icc.ub.edu

2012-03-01

We present N-body simulations for generic non-Gaussian initial conditions with the aim of exploring and modelling the scale-dependent halo bias. This effect is evident on very large scales requiring large simulation boxes. In addition, the previously available prescription to implement generic non-Gaussian initial conditions has been improved to keep under control higher-order terms which were spoiling the power spectrum on large scales. We pay particular attention to the differences between physical, inflation-motivated primordial bispectra and their factorizable templates, and to the operational definition of the non-Gaussian halo bias (which has both a scale-dependent and an approximately scale-independent contributions). We find that analytic predictions for both the non-Gaussian halo mass function and halo bias work well once a fudge factor (which was introduced before but still lacks convincing physical explanation) is calibrated on simulations. The halo bias remains therefore an extremely promising tool to probe primordial non-Gaussianity and thus to give insights into the physical mechanism that generated the primordial perturbations. The simulation outputs and tables of the analytic predictions will be made publicly available via the non-Gaussian comparison project web site http://icc.ub.edu/∼liciaverde/NGSCP.html.

20. Paternal condition drives progeny sex-ratio bias in a lizard that lacks parental care.

PubMed

Cox, Robert M; Duryea, M Catherine; Najarro, Michael; Calsbeek, Ryan

2011-01-01

Sex-allocation theory predicts that females in good condition should preferentially produce offspring of the sex that benefits the most from an increase in maternal investment. However, it is generally assumed that the condition of the sire has little effect on progeny sex ratio, particularly in species that lack parental care. We used a controlled breeding experiment and molecular paternity analyses to examine the effects of both maternal and paternal condition on progeny sex ratio and progeny fitness in the brown anole (Anolis sagrei), a polygynous lizard that lacks parental care. Contrary to the predictions of sex-allocation theory, we found no relationship between maternal condition and progeny sex ratio. By contrast, progeny sex ratio shifted dramatically from female-biased to male-biased as paternal condition increased. This pattern was driven entirely by an increase in the production of sons as paternal condition improved. Despite strong natural selection favoring large size and high condition in both sons and daughters, we found no evidence that progeny survival was related to paternal condition. Our results emphasize the importance of considering the paternal phenotype in studies of sex allocation and highlight the need for further research into the pathways that link paternal condition to progeny fitness. © 2010 The Author(s). Evolution© 2010 The Society for the Study of Evolution.

1. Radiation induced deep level defects in bipolar junction transistors under various bias conditions

Liu, Chaoming; Yang, Jianqun; Li, Xingji; Ma, Guoliang; Xiao, Liyi; Bollmann, Joachim

2015-12-01

Bipolar junction transistor (BJT) is sensitive to ionization and displacement radiation effects in space. In this paper, 35 MeV Si ions were used as irradiation source to research the radiation damage on NPN and PNP bipolar transistors. The changing of electrical parameters of transistors was in situ measured with increasing irradiation fluence of 35 MeV Si ions. Using deep level transient spectroscopy (DLTS), defects in the bipolar junction transistors under various bias conditions are measured after irradiation. Based on the in situ electrical measurement and DLTS spectra, it is clearly that the bias conditions can affect the concentration of deep level defects, and the radiation damage induced by heavy ions.

2. Bias-reduced and separation-proof conditional logistic regression with small or sparse data sets.

PubMed

Heinze, Georg; Puhr, Rainer

2010-03-30

Conditional logistic regression is used for the analysis of binary outcomes when subjects are stratified into several subsets, e.g. matched pairs or blocks. Log odds ratio estimates are usually found by maximizing the conditional likelihood. This approach eliminates all strata-specific parameters by conditioning on the number of events within each stratum. However, in the analyses of both an animal experiment and a lung cancer case-control study, conditional maximum likelihood (CML) resulted in infinite odds ratio estimates and monotone likelihood. Estimation can be improved by using Cytel Inc.'s well-known LogXact software, which provides a median unbiased estimate and exact or mid-p confidence intervals. Here, we suggest and outline point and interval estimation based on maximization of a penalized conditional likelihood in the spirit of Firth's (Biometrika 1993; 80:27-38) bias correction method (CFL). We present comparative analyses of both studies, demonstrating some advantages of CFL over competitors. We report on a small-sample simulation study where CFL log odds ratio estimates were almost unbiased, whereas LogXact estimates showed some bias and CML estimates exhibited serious bias. Confidence intervals and tests based on the penalized conditional likelihood had close-to-nominal coverage rates and yielded highest power among all methods compared, respectively. Therefore, we propose CFL as an attractive solution to the stratified analysis of binary data, irrespective of the occurrence of monotone likelihood. A SAS program implementing CFL is available at: http://www.muw.ac.at/msi/biometrie/programs.

3. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study.

PubMed

Austin, Peter C; Grootendorst, Paul; Normand, Sharon-Lise T; Anderson, Geoffrey M

2007-02-20

Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide.

4. Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory

PubMed Central

Koenig, Stephan; Uengoer, Metin; Lachnit, Harald

2017-01-01

We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy, respectively. The cues differed with respect to the strength of their shock association (L < P < H) and the uncertainty of their prediction (L < P > H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues. After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically. Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention. PMID:28588466

5. Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory.

PubMed

Koenig, Stephan; Uengoer, Metin; Lachnit, Harald

2017-01-01

We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy, respectively. The cues differed with respect to the strength of their shock association (L < P < H) and the uncertainty of their prediction (L < P > H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues. After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues' potential to capture and hold overt attention automatically. Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues' shock association for the early attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention.

6. On the impact of bias correcting and conditioning precipitation inputs on seasonal streamflow forecast quality

Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; Perrin, Charles

2017-04-01

Skillful seasonal streamflow forecasts are increasingly requested for decision-making in areas such as drought risk assessment or reservoir management. Meteorological forcing can be the major source of uncertainty in seasonal forecasts as early as in the first month of the forecast period. The choice of the hydrological model inputs thus has a major impact on the quality of generated streamflow forecasts. In this study, we assess the impact of two types of precipitation forecast post-treatment: 1) bias correction and 2) conditioning, on streamflow forecast quality. We first evaluated several bias correction approaches and conditioned precipitation scenarios in sixteen catchments in France, with the help of ECMWF System 4 seasonal precipitation forecasts and the GR6J hydrological model. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often sharper than the conventional ESP method. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. The empirical distribution mapping of daily values was successful in improving forecast reliability, but sometimes at the expense of forecast sharpness. We also evaluated several conditioning methods based on ECMWF System 4 precipitation forecasts to generate seasonal streamflow forecasts in the same sixteen catchments. Four precipitation indices based on System 4 precipitation were used to condition historical streamflow or historical precipitations to be used as input to the GR6J model. Our results evaluate how the conditioning impacts the reliability and sharpness of streamflow forecasts, as well as forecasts of drought indices. We show that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) can improve the sharpness of ensemble forecasts based on historical data, but also often decrease reliability. References: Crochemore, L., Ramos, M.-H., and Pappenberger

7. Multivariate or Multivariable Regression?

PubMed Central

Goodman, Melody

2013-01-01

The terms multivariate and multivariable are often used interchangeably in the public health literature. However, these terms actually represent 2 very distinct types of analyses. We define the 2 types of analysis and assess the prevalence of use of the statistical term multivariate in a 1-year span of articles published in the American Journal of Public Health. Our goal is to make a clear distinction and to identify the nuances that make these types of analyses so distinct from one another. PMID:23153131

8. Hot-spot heating susceptibility due to reverse bias operating conditions

NASA Technical Reports Server (NTRS)

Gonzalez, C. C.

1985-01-01

Because of field experience (indicating that cell and module degradation could occur as a result of hot spot heating), a laboratory test was developed at JPL to determine hot spot susceptibility of modules. The initial hot spot testing work at JPL formed a foundation for the test development. Test parameters are selected as follows. For high shunt resistance cells, the applied back bias test current is set equal to the test cell current at maximum power. For low shunt resistance cells, the test current is set equal to the cell short circuit current. The shadow level is selected to conform to that which would lead to maximum back bias voltage under the appropriate test current level. The test voltage is determined by the bypass diode frequency. The test conditions are meant to simulate the thermal boundary conditions for 100 mW/sq cm, 40C ambient environment. The test lasts 100 hours. A key assumption made during the development of the test is that no current imbalance results from the connecting of multiparallel cell strings. Therefore, the test as originally developed was applicable for single string case only.

9. Obesity paradox: conditioning on disease enhances biases in estimating the mortality risks of obesity.

PubMed

Preston, Samuel H; Stokes, Andrew

2014-05-01

Many studies have documented an obesity paradox-a survival advantage of being obese-in populations diagnosed with a medical condition. Whether obesity is causally associated with improved mortality in these conditions is unresolved. We develop the logic of collider bias as it pertains to the association between smoking and obesity in a diseased population. Data from the National Health and Nutrition Examination Survey (NHANES) are used to investigate this bias empirically among persons with diabetes and prediabetes (dysglycemia). We also use NHANES to investigate whether reverse causal pathways are more prominent among people with dysglycemia than in the source population. Cox regression analysis is used to examine the extent of the obesity paradox among those with dysglycemia. In the regression analysis, we explore interactions between obesity and smoking, and we implement a variety of data restrictions designed to reduce the extent of reverse causality. We find an obesity paradox among persons with dysglycemia. In this population, the inverse association between obesity and smoking is much stronger than in the source population, and the extent of illness and weight loss is greater. The obesity paradox is absent among never-smokers. Among smokers, the paradox is eliminated through successive efforts to reduce the extent of reverse causality. Higher mortality among normal-weight people with dysglycemia is not causal but is rather a product of the closer inverse association between obesity and smoking in this subpopulation.

10. Condition bias of hunter-shot ring-necked ducks exposed to lead

USGS Publications Warehouse

McCracken, K.G.; Afton, A.D.; Peters, M.S.

2000-01-01

11. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods.

PubMed

Wang, Yiyi; Kockelman, Kara M

2013-11-01

This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates.

12. A hybrid framework for downscaling time-dependent multivariate coastal boundary conditions

Alvarez Antolinez, J. A.; Murray, A. B.; Moore, L. J.; Wood, J.; Mendez, F. J.

2016-12-01

response and to attribute particular local forcing conditions to synoptic-scale atmospheric patterns, for both extratropical and tropical cyclone activity.

13. Conditioned social dominance threat: observation of others’ social dominance biases threat learning

PubMed Central

Molapour, Tanaz; Olsson, Andreas

2016-01-01

Social groups are organized along dominance hierarchies, which determine how we respond to threats posed by dominant and subordinate others. The persuasive impact of these dominance threats on mental and physical well-being has been well described but it is unknown how dominance rank of others bias our experience and learning in the first place. We introduce a model of conditioned social dominance threat in humans, where the presence of a dominant other is paired with an aversive event. Participants first learned about the dominance rank of others by observing their dyadic confrontations. During subsequent fear learning, the dominant and subordinate others were equally predictive of an aversive consequence (mild electric shock) to the participant. In three separate experiments, we show that participants’ eye-blink startle responses and amygdala reactivity adaptively tracked dominance of others during observation of confrontation. Importantly, during fear learning dominant vs subordinate others elicited stronger and more persistent learned threat responses as measured by physiological arousal and amygdala activity. Our results characterize the neural basis of learning through observing conflicts between others, and how this affects subsequent learning through direct, personal experiences. PMID:27217107

14. Degradation of Leakage Currents in Solid Tantalum Capacitors Under Steady-State Bias Conditions

NASA Technical Reports Server (NTRS)

Teverovsky, Alexander A.

2010-01-01

Degradation of leakage currents in various types of solid tantalum capacitors under steady-state bias conditions was investigated at temperatures from 105 oC to 170 oC and voltages up to two times the rated voltage. Variations of leakage currents with time under highly accelerated life testing (HALT) and annealing, thermally stimulated depolarization currents, and I-V characteristics were measured to understand the conduction mechanism and the reason for current degradation. During HALT the currents increase gradually up to three orders of magnitude in some cases, and then stabilize with time. This degradation is reversible and annealing can restore the initial levels of leakage currents. The results are attributed to migration of positively charged oxygen vacancies in tantalum pentoxide films that diminish the Schottky barrier at the MnO2/Ta2O5 interface and increase electron injection. A simple model allows for estimation of concentration and mobility of oxygen vacancies based on the level of current degradation.

15. Assessment of infrared spectroscopy and multivariate techniques for monitoring the service condition of diesel-engine lubricating oils.

PubMed

Caneca, Arnobio Roberto; Pimentel, M Fernanda; Galvão, Roberto Kawakami Harrop; da Matta, Cláudia Eliane; de Carvalho, Florival Rodrigues; Raimundo, Ivo M; Pasquini, Celio; Rohwedder, Jarbas J R

2006-09-15

This paper presents two methodologies for monitoring the service condition of diesel-engine lubricating oils on the basis of infrared spectra. In the first approach, oils samples are discriminated into three groups, each one associated to a given wear stage. An algorithm is proposed to select spectral variables with good discriminant power and small collinearity for the purpose of discriminant analysis classification. As a result, a classification accuracy of 93% was obtained both in the middle (MIR) and near-infrared (NIR) ranges. The second approach employs multivariate calibration methods to predict the viscosity of the lubricant. In this case, the use of absorbance measurements in the NIR spectral range was not successful, because of experimental difficulties associated to the presence of particulate matter. Such a problem was circumvented by the use of attenuated total reflectance (ATR) measurements in the MIR spectral range, in which an RMSEP of 3.8cSt and a relative average error of 3.2% were attained.

16. Plasma biasing to control the growth conditions of diamond-likecarbon

SciTech Connect

Anders, Andre; Pasaja, Nitisak; Lim, Sunnie H.N.; Petersen, TimC.; Keast, Vicki J.

2006-04-30

It is well known that the structure and properties ofdiamond-like carbon, and in particular the sp3/sp2 ratio, can becontrolled by the energy of the condensing carbon ions or atoms. In manypractical cases, the energy of ions arriving at the surface of thegrowing film is determined by the bias applied to the substrate. The biascauses a sheath to form between substrate and plasma in which thepotential difference between plasma potential and surface potentialdrops. In this contribution, we demonstrate that the same results can beobtained with grounded substrates by shifting the plasma potential. This"plasma biasing" (as opposed to "substrate biasing") is shown to workwell with pulsed cathodic carbon arcs, resulting in tetrahedral amorphouscarbon (ta-C) films that are comparable to the films obtained with theconventional substrate bias. To verify the plasma bias approach, ta-Cfilms were deposited by both conventional and plasma bias andcharacterized by transmission electron microscopy (TEM) and electronenergy loss spectrometry (EELS). Detailed data for comparison of thesefilms are provided.

17. Forecasting the Rupture Directivity of Large Earthquakes: Centroid Bias of the Conditional Hypocenter Distribution

Donovan, J.; Jordan, T. H.

2012-12-01

Forecasting the rupture directivity of large earthquakes is an important problem in probabilistic seismic hazard analysis (PSHA), because directivity is known to strongly influence ground motions. We describe how rupture directivity can be forecast in terms of the "conditional hypocenter distribution" or CHD, defined to be the probability distribution of a hypocenter given the spatial distribution of moment release (fault slip). The simplest CHD is a uniform distribution, in which the hypocenter probability density equals the moment-release probability density. For rupture models in which the rupture velocity and rise time depend only on the local slip, the CHD completely specifies the distribution of the directivity parameter D, defined in terms of the degree-two polynomial moments of the source space-time function. This parameter, which is zero for a bilateral rupture and unity for a unilateral rupture, can be estimated from finite-source models or by the direct inversion of seismograms (McGuire et al., 2002). We compile D-values from published studies of 65 large earthquakes and show that these data are statistically inconsistent with the uniform CHD advocated by McGuire et al. (2002). Instead, the data indicate a "centroid biased" CHD, in which the expected distance between the hypocenter and the hypocentroid is less than that of a uniform CHD. In other words, the observed directivities appear to be closer to bilateral than predicted by this simple model. We discuss the implications of these results for rupture dynamics and fault-zone heterogeneities. We also explore their PSHA implications by modifying the CyberShake simulation-based hazard model for the Los Angeles region, which assumed a uniform CHD (Graves et al., 2011).

18. Behavior of electrons under different biasing conditions in a multidipole plasma

Mishra, M. K.; Phukan, A.

2012-08-01

This paper reports about the bi-Maxwellian nature of electrons in a filament discharge plasma that exist in a suitable working pressure range. The plasma is produced within a multidipole magnetic cage with a stainless steel mesh grid connected at one end of the cage. The variation in electron energy, plasma potential and electron energy probability function are studied by applying different bias voltage to the magnetic cage, mesh grid and the filaments. It is observed that the electron energies are highly influenced by the bias applied to the magnetic cage and the filaments. The plasma potential is found to be mostly affected by the cage bias voltage. A plane Langmuir probe is used to estimate the plasma parameters.

19. Seasonal streamflow forecasting: experiences with precipitation bias correction and SPI conditioning to improve performance for hydrological events

Ramos, M. H.; Crochemore, L.; Pappenberger, F.; Perrin, C.

2016-12-01

Many fields such as drought risk assessment or reservoir management can benefit from seasonal streamflow forecasts. This study presents the results of two analyses aiming to: 1) assess the skill of seasonal precipitation forecasts in France and provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times, and 2) evaluate how the conditioning of historical data based on the Standardized Precipitation Index (SPI) from bias-corrected GCM precipitation forecasts can be useful to select traces within the historical data and further improve the forecast of droughts. We evaluated several bias correction approaches and conditioned precipitation scenarios in sixteen catchments in France, with the help of ECMWF System 4 seasonal precipitation forecasts and the GR6J hydrological model. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often sharper than the conventional ESP method. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. The simple linear scaling of monthly values contributed mainly to increase forecast sharpness and accuracy, while the empirical distribution mapping of daily values was successful in improving forecast reliability. Our results also show that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) can improve the sharpness of ensemble forecasts based on historical data, while maintaining good reliability. An evaluation of forecast ensembles for low-flow forecasting showed that the SPI3-conditioned ensembles provided reliable forecasts of low-flow duration and deficit volume based on the 80th exceedance percentile. Drought risk forecasting is illustrated for the 2003 drought event.

20. Process-induced bias: a study of resist design, device node, illumination conditions, and process implications

Carcasi, Michael; Scheer, Steven; Fonseca, Carlos; Shibata, Tsuyoshi; Kosugi, Hitoshi; Kondo, Yoshihiro; Saito, Takashi

2009-03-01

Critical dimension uniformity (CDU) has both across field and across wafer components. CD error generated by across wafer etching non-uniformity and other process variations can have a significant impact on CDU. To correct these across wafer systematic variations, compensation by exposure dose and/or post exposure bake (PEB) temperature have been proposed. These compensation strategies often focus on a specific structure without evaluating how process compensation impacts the CDU of all structures to be printed in a given design. In one previous study limited to a single resist and minimal coater/developer and scanner variations, the authors evaluated the relative merits of across wafer dose and PEB temperature compensation on the process induced CD bias and CDU. For the process studied, it was found that using PEB temperature to control CD across wafer was preferable to using dose compensation. In another previous study, the impact of resist design was explored to understand how resist design, as well as coater/developer and scanner processing, impact process induced bias (PIB). The previous PIB studies were limited to a single illumination case and explore the effect of PIB on only L/S structures. It is the goal of this work to understand additionally how illumination design and mask design, as well as resist design and coater/developer and scanner processing, impact process induced bias (PIB)/OPC integrity.

1. Auditory-evoked spike firing in the lateral amygdala and Pavlovian fear conditioning: mnemonic code or fear bias?

PubMed

Goosens, Ki A; Hobin, Jennifer A; Maren, Stephen

2003-12-04

Amygdala neuroplasticity has emerged as a candidate substrate for Pavlovian fear memory. By this view, conditional stimulus (CS)-evoked activity represents a mnemonic code that initiates the expression of fear behaviors. However, a fear state may nonassociatively enhance sensory processing, biasing CS-evoked activity in amygdala neurons. Here we describe experiments that dissociate auditory CS-evoked spike firing in the lateral amygdala (LA) and both conditional fear behavior and LA excitability in rats. We found that the expression of conditional freezing and increased LA excitability was neither necessary nor sufficient for the expression of conditional increases in CS-evoked spike firing. Rather, conditioning-related changes in CS-evoked spike firing were solely determined by the associative history of the CS. Thus, our data support a model in which associative activity in the LA encodes fear memory and contributes to the expression of learned fear behaviors.

2. Implicit conditioning of faces via the social regulation of emotion: ERP evidence of early attentional biases for security conditioned faces.

PubMed

Beckes, Lane; Coan, James A; Morris, James P

2013-08-01

Not much is known about the neural and psychological processes that promote the initial conditions necessary for positive social bonding. This study explores one method of conditioned bonding utilizing dynamics related to the social regulation of emotion and attachment theory. This form of conditioning involves repeated presentations of negative stimuli followed by images of warm, smiling faces. L. Beckes, J. Simpson, and A. Erickson (2010) found that this conditioning procedure results in positive associations with the faces measured via a lexical decision task, suggesting they are perceived as comforting. This study found that the P1 ERP was similarly modified by this conditioning procedure and the P1 amplitude predicted lexical decision times to insecure words primed by the faces. The findings have implications for understanding how the brain detects supportive people, the flexibility and modifiability of early ERP components, and social bonding more broadly.

3. Terahertz responsivity of field-effect transistors under arbitrary biasing conditions

Földesy, Péter

2013-09-01

Current biased photoresponse model of long channel field-effect transistor (FET) detectors is introduced to describe the low frequency behavior in complex circuit environment. The model is applicable in all FET working regions, including subthreshold, linear, saturated modes, includes bulk potential variations, and handles the simultaneous gate-source and drain-source detection or source-driven topologies. The model is based on the phenomenological representation that links the photoresponse to the gate transconductance over drain current ratio (gm/ID) and circuit theory. A derived method is provided to analyze the detector behavior, to characterize existing antenna coupled detectors, and to predict the photoresponse in a complex circuit. The model is validated by measurements of 180 nm gate length silicon and GaAs high electron mobility FETs.

4. Non-random temporary emigration and the robust design: Conditions for bias at the end of a time series: Section VIII

USGS Publications Warehouse

Langtimm, Catherine A.

2008-01-01

Knowing the extent and magnitude of the potential bias can help in making decisions as to what time frame provides the best estimates or the most reliable opportunity to model and test hypotheses about factors affecting survival probability. To assess bias, truncating the capture histories to shorter time frames and reanalyzing the data to compare time-specific estimates may help identify spurious effects. Running simulations that mimic the parameter values and movement conditions in the real situation can provide estimates of standardized bias that can be used to identify those annual estimates that are biased to the point where the 95% confidence intervals are inadequate in describing the uncertainty of the estimates.

5. Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations.

PubMed

Wallace, Jack; Champagne, Pascale; Hall, Geof

2016-06-01

The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets. Copyright © 2016 Elsevier Ltd. All rights reserved.

6. HEALTHY YOUNG WOMEN WITH SEROTONIN TRANSPORTER SS POLYMORPHISM SHOW A PRO-INFLAMMATORY BIAS UNDER RESTING & STRESS CONDITIONS

PubMed Central

Fredericks, Carolyn A.; Drabant, Emily M.; Edge, Michael D.; Tillie, Jean M.; Hallmayer, Joachim; Ramel, Wiveka; Kuo, Janice R.; Mackey, Sean; Gross, James J.; Dhabhar, Firdaus S.

2010-01-01

The study of functionally relevant biological effects of serotonin transporter gene promoter region (5-HTTLPR) polymorphisms is especially important given the current controversy about the clinical relevance of these polymorphisms. Here we report an intrinsic immunobiological difference between individuals carrying two short (SS) versus long (LL) 5-HTTLPR alleles, that is observed in healthy subjects reporting low exposure to life stress. Given that 5-HTTLPR polymorphisms are thought to influence susceptibility to depression and are associated with robust neurobiological effects, that depression is associated with higher pro-inflammatory and lower anti-inflammatory cytokines, and that acute stressors increase circulating concentrations of pro-inflammatory cytokines, we hypothesized that compared to LL-individuals, SS-individuals may show a pro-inflammatory bias under resting conditions and/or during stress. 15-LL and 11-SS-individuals participated in the Trier Social Stress Test (TSST). Serum IL-6 and IL-10 were quantified at baseline and 30, 60, 90, and 120 minutes after beginning the 20-minute stress test. Compared to LL-individuals, SS-individuals showed a higher IL-6/IL-10 ratio at baseline and during stress. Importantly, this pro-inflammatory bias was observed despite both groups being healthy, reporting similar intensities of stress and negative emotionality during the TSST, and reporting similar low exposures to early and recent life stress. To our knowledge, this is the first report of a pro-inflammatory bias/phenotype in individuals carrying the SS genotype of 5-HTTLPR. Thus, healthy SS-individuals may be chronically exposed to a pro-inflammatory physiological burden under resting and stress conditions, which could increase their vulnerability to disorders like depression and other diseases that can be facilitated/exacerbated by a chronic proinflammatory state. PMID:19883751

7. Foreign Exchange Value-at-Risk with Multiple Currency Exposure: A Multivariate and Copula Generalized Autoregressive Conditional Heteroskedasticity Approach

DTIC Science & Technology

2014-11-01

à un risque financier lié aux varia- tions du taux de change, et les responsables de la gestion interne se voient donc pressés de trouver des...Jondeau, E. and Rockinger, M. (2006), The Copula-GARCH model of conditional dependencies: An international stock market application, Journal of

8. The Performance of Cross-Validation Indices Used to Select among Competing Covariance Structure Models under Multivariate Nonnormality Conditions

ERIC Educational Resources Information Center

Whittaker, Tiffany A.; Stapleton, Laura M.

2006-01-01

Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…

9. The Performance of Cross-Validation Indices Used to Select among Competing Covariance Structure Models under Multivariate Nonnormality Conditions

ERIC Educational Resources Information Center

Whittaker, Tiffany A.; Stapleton, Laura M.

2006-01-01

Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…

10. Determination of ethyl glucuronide in human hair samples: A multivariate analysis of the impact of extraction conditions on quantitative results.

PubMed

Mueller, Alexander; Jungen, Hilke; Iwersen-Bergmann, Stefanie; Raduenz, Lars; Lezius, Susanne; Andresen-Streichert, Hilke

2017-02-01

Ethyl glucuronide (EtG), a minor metabolite of ethanol, is used as a direct alcohol biomarker for the prolonged detection of ethanol consumption. Hair testing for EtG offers retrospective, long-term detection of ethanol exposition for several months and has gained practical importance in forensic and clinical toxicology. Since quantitative results of EtG hair testings are included in interpretations, a rugged quantitation of EtG in hair matrix is important. As generally known, sample preparation is critical in hair testing, and the scope of this study was on extraction of EtG from hair matrix. The influence of extraction solvent, ultrasonication, incubation temperature, incubation time, solvent amount and hair particle size on quantitative results was investigated by a multifactorial experimental design using a validated analytical method and twelve different batches of authentic human hair material. Eight series of extraction experiments in a Plackett-Burman setup were carried out on each hair material with the studied factors at high or low levels. The effect of pulverization was further studied by two additional experimental series. Five independent samplings were performed for each run, resulting in a total number of 600 determinations. Considerable differences in quantitative EtG results were observed, concentrations above and below interpretative cut-offs were obtained from the same hair materials using different extraction conditions. Statistical analysis revealed extraction solvent and temperature as the most important experimental factors with significant influence on quantitative results. The impact of pulverization depended on other experimental factors and the different hair matrices themselves proved to be important predictors of extraction efficiency. A standardization of extraction procedures should be discussed, since it will probably reduce interlaboratory variabilities and improve the quality and acceptance of hair EtG analysis. Copyright © 2016

11. 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.

12. 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

13. 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.

14. Multivariate normality

NASA Technical Reports Server (NTRS)

Crutcher, H. L.; Falls, L. W.

1976-01-01

Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

15. Bias from conditioning on live birth in pregnancy cohorts: an illustration based on neurodevelopment in children after prenatal exposure to organic pollutants.

PubMed

Liew, Zeyan; Olsen, Jørn; Cui, Xin; Ritz, Beate; Arah, Onyebuchi A

2015-02-01

Only 60-70% of fertilized eggs may result in a live birth, and very early fetal loss mainly goes unnoticed. Outcomes that can only be ascertained in live-born children will be missing for those who do not survive till birth. In this article, we illustrate a common bias structure (leading to 'live-birth bias') that arises from studying the effects of prenatal exposure to environmental factors on long-term health outcomes among live births only in pregnancy cohorts. To illustrate this we used prenatal exposure to perfluoroalkyl substances (PFAS) and attention-deficit/hyperactivity disorder (ADHD) in school-aged children as an example. PFAS are persistent organic pollutants that may impact human fecundity and be toxic for neurodevelopment. We simulated several hypothetical scenarios based on characteristics from the Danish National Birth Cohort and found that a weak inverse association may appear even if PFAS do not cause ADHD but have a considerable effect on fetal survival. The magnitude of the negative bias was generally small, and adjusting for common causes of the outcome and fetal loss can reduce the bias. Our example highlights the need to identify the determinants of pregnancy loss and the importance of quantifying bias arising from conditioning on live birth in observational studies.

16. Bias from conditioning on live birth in pregnancy cohorts: an illustration based on neurodevelopment in children after prenatal exposure to organic pollutants

PubMed Central

Liew, Zeyan; Olsen, Jørn; Cui, Xin; Ritz, Beate; Arah, Onyebuchi A

2015-01-01

Only 60–70% of fertilized eggs may result in a live birth, and very early fetal loss mainly goes unnoticed. Outcomes that can only be ascertained in live-born children will be missing for those who do not survive till birth. In this article, we illustrate a common bias structure (leading to ‘live-birth bias’) that arises from studying the effects of prenatal exposure to environmental factors on long-term health outcomes among live births only in pregnancy cohorts. To illustrate this we used prenatal exposure to perfluoroalkyl substances (PFAS) and attention-deficit/hyperactivity disorder (ADHD) in school-aged children as an example. PFAS are persistent organic pollutants that may impact human fecundity and be toxic for neurodevelopment. We simulated several hypothetical scenarios based on characteristics from the Danish National Birth Cohort and found that a weak inverse association may appear even if PFAS do not cause ADHD but have a considerable effect on fetal survival. The magnitude of the negative bias was generally small, and adjusting for common causes of the outcome and fetal loss can reduce the bias. Our example highlights the need to identify the determinants of pregnancy loss and the importance of quantifying bias arising from conditioning on live birth in observational studies. PMID:25604449

17. Housing conditions affect rat responses to two types of ambiguity in a reward–reward discrimination cognitive bias task

PubMed Central

Parker, Richard M.A.; Paul, Elizabeth S.; Burman, Oliver H.P.; Browne, William J.; Mendl, Michael

2014-01-01

Decision-making under ambiguity in cognitive bias tasks is a promising new indicator of affective valence in animals. Rat studies support the hypothesis that animals in a negative affective state evaluate ambiguous cues negatively. Prior automated operant go/go judgement bias tasks have involved training rats that an auditory cue of one frequency predicts a Reward and a cue of a different frequency predicts a Punisher (RP task), and then measuring whether ambiguous cues of intermediate frequency are judged as predicting reward (‘optimism’) or punishment (‘pessimism’). We investigated whether an automated Reward–Reward (RR) task yielded similar results to, and was faster to train than, RP tasks. We also introduced a new ambiguity test (simultaneous presentation of the two training cues) alongside the standard single ambiguous cue test. Half of the rats experienced an unpredictable housing treatment (UHT) designed to induce a negative state. Control rats were relatively ‘pessimistic’, whilst UHT rats were quicker, but no less accurate, in their responses in the RR test, and showed less anxiety-like behaviour in independent tests. A possible reason for these findings is that rats adapted to and were stimulated by UHT, whilst control rats in a predictable environment were more sensitive to novelty and change. Responses in the new ambiguity test correlated positively with those in single ambiguous cue tests, and may provide a measure of attention bias. The RR task was quicker to train than previous automated RP tasks. Together, they could be used to disentangle how reward and punishment processes underpin affect-induced cognitive biases. PMID:25106739

18. Housing conditions affect rat responses to two types of ambiguity in a reward-reward discrimination cognitive bias task.

PubMed

Parker, Richard M A; Paul, Elizabeth S; Burman, Oliver H P; Browne, William J; Mendl, Michael

2014-11-01

Decision-making under ambiguity in cognitive bias tasks is a promising new indicator of affective valence in animals. Rat studies support the hypothesis that animals in a negative affective state evaluate ambiguous cues negatively. Prior automated operant go/go judgement bias tasks have involved training rats that an auditory cue of one frequency predicts a Reward and a cue of a different frequency predicts a Punisher (RP task), and then measuring whether ambiguous cues of intermediate frequency are judged as predicting reward ('optimism') or punishment ('pessimism'). We investigated whether an automated Reward-Reward (RR) task yielded similar results to, and was faster to train than, RP tasks. We also introduced a new ambiguity test (simultaneous presentation of the two training cues) alongside the standard single ambiguous cue test. Half of the rats experienced an unpredictable housing treatment (UHT) designed to induce a negative state. Control rats were relatively 'pessimistic', whilst UHT rats were quicker, but no less accurate, in their responses in the RR test, and showed less anxiety-like behaviour in independent tests. A possible reason for these findings is that rats adapted to and were stimulated by UHT, whilst control rats in a predictable environment were more sensitive to novelty and change. Responses in the new ambiguity test correlated positively with those in single ambiguous cue tests, and may provide a measure of attention bias. The RR task was quicker to train than previous automated RP tasks. Together, they could be used to disentangle how reward and punishment processes underpin affect-induced cognitive biases.

19. Analysis of TID process, geometry, and bias condition dependence in 14-nm FinFETs and implications for RF and SRAM performance

DOE PAGES

King, M. P.; Wu, X.; Eller, Manfred; ...

2016-12-07

Here, total ionizing dose results are provided, showing the effects of different threshold adjust implant processes and irradiation bias conditions of 14-nm FinFETs. Minimal radiation-induced threshold voltage shift across a variety of transistor types is observed. Off-state leakage current of nMOSFET transistors exhibits a strong gate bias dependence, indicating electrostatic gate control of the sub-fin region and the corresponding parasitic conduction path are the largest concern for radiation hardness in FinFET technology. The high-Vth transistors exhibit the best irradiation performance across all bias conditions, showing a reasonably small change in off-state leakage current and Vth, while the low-Vth transistors exhibitmore » a larger change in off-state leakage current. The “worst-case” bias condition during irradiation for both pull-down and pass-gate nMOSFETs in static random access memory is determined to be the on-state (Vgs = Vdd). We find the nMOSFET pull-down and pass-gate transistors of the SRAM bit-cell show less radiation-induced degradation due to transistor geometry and channel doping differences than the low-Vth transistor. Near-threshold operation is presented as a methodology for reducing radiation-induced increases in off-state device leakage current. In a 14-nm FinFET technology, the modeling indicates devices with high channel stop doping show the most robust response to TID allowing stable operation of ring oscillators and the SRAM bit-cell with minimal shift in critical operating characteristics.« less

20. Analysis of TID process, geometry, and bias condition dependence in 14-nm FinFETs and implications for RF and SRAM performance

SciTech Connect

King, M. P.; Wu, X.; Eller, Manfred; Samavedam, Srikanth; Shaneyfelt, M. R.; Silva, A. I.; Draper, B. L.; Rice, W. C.; Meisenheimer, T. L.; Felix, J. A.; Shetler, K. J.; Zhang, E. X.; Haeffner, T. D.; Ball, D. R.; Alles, M. L.; Kauppila, J. S.; Massengill, L. W.

2016-12-07

Here, total ionizing dose results are provided, showing the effects of different threshold adjust implant processes and irradiation bias conditions of 14-nm FinFETs. Minimal radiation-induced threshold voltage shift across a variety of transistor types is observed. Off-state leakage current of nMOSFET transistors exhibits a strong gate bias dependence, indicating electrostatic gate control of the sub-fin region and the corresponding parasitic conduction path are the largest concern for radiation hardness in FinFET technology. The high-Vth transistors exhibit the best irradiation performance across all bias conditions, showing a reasonably small change in off-state leakage current and Vth, while the low-Vth transistors exhibit a larger change in off-state leakage current. The “worst-case” bias condition during irradiation for both pull-down and pass-gate nMOSFETs in static random access memory is determined to be the on-state (Vgs = Vdd). We find the nMOSFET pull-down and pass-gate transistors of the SRAM bit-cell show less radiation-induced degradation due to transistor geometry and channel doping differences than the low-Vth transistor. Near-threshold operation is presented as a methodology for reducing radiation-induced increases in off-state device leakage current. In a 14-nm FinFET technology, the modeling indicates devices with high channel stop doping show the most robust response to TID allowing stable operation of ring oscillators and the SRAM bit-cell with minimal shift in critical operating characteristics.

1. Renormalized halo bias

SciTech Connect

Assassi, Valentin; Baumann, Daniel; Green, Daniel; Zaldarriaga, Matias E-mail: dbaumann@damtp.cam.ac.uk E-mail: matiasz@ias.edu

2014-08-01

This paper provides a systematic study of renormalization in models of halo biasing. Building on work of McDonald, we show that Eulerian biasing is only consistent with renormalization if non-local terms and higher-derivative contributions are included in the biasing model. We explicitly determine the complete list of required bias parameters for Gaussian initial conditions, up to quartic order in the dark matter density contrast and at leading order in derivatives. At quadratic order, this means including the gravitational tidal tensor, while at cubic order the velocity potential appears as an independent degree of freedom. Our study naturally leads to an effective theory of biasing in which the halo density is written as a double expansion in fluctuations and spatial derivatives. We show that the bias expansion can be organized in terms of Galileon operators which aren't renormalized at leading order in derivatives. Finally, we discuss how the renormalized bias parameters impact the statistics of halos.

2. Time-varying associations of suicide with deployments, mental health conditions, and stressful life events among current and former US military personnel: a retrospective multivariate analysis.

PubMed

Shen, Yu-Chu; Cunha, Jesse M; Williams, Thomas V

2016-11-01

US military suicides have increased substantially over the past decade and currently account for almost 20% of all military deaths. We investigated the associations of a comprehensive set of time-varying risk factors with suicides among current and former military service members. We did a retrospective multivariate analysis of all US military personnel between 2001 and 2011 (n=110 035 573 person-quarter-years, representing 3 795 823 service members). Outcome was death by suicide, either during service or post-separation. We used Cox proportional hazard models at the person-quarter level to examine associations of deployment, mental disorders, history of unlawful activity, stressful life events, and other demographic and service factors with death by suicide. The strongest predictors of death by suicide were current and past diagnoses of self-inflicted injuries, major depression, bipolar disorder, substance use disorder, and other mental health conditions (compared with service members with no history of diagnoses, the hazard ratio [HR] ranged from 1·4 [95% CI 1·14-1·72] to 8·34 [6·71-10·37]). Compared with service members who were never deployed, hazard rates of suicide (which represent the probability of death by suicide in a specific quarter given that the individual was alive in the previous quarter) were lower among the currently deployed (HR 0·50, 95% CI 0·40-0·61) but significantly higher in the quarters following first deployment (HR 1·51 [1·17-1·96] if deployed in the previous three quarters; 1·14 [1·06-1·23] if deployed four or more quarters ago). The hazard rate of suicide increased within the first year of separation from the military (HR 2·49, 95% CI 2·12-2·91), and remained high for those who had separated from the military 6 or more years ago (HR 1·63, 1·45-1·82). The increased hazard rate of death by suicide for military personnel varies by time since exposure to deployment, mental health diagnoses, and other stressful

3. Post-training re-exposure to fear conditioned stimuli enhances memory consolidation and biases rats toward the use of dorsolateral striatum-dependent response learning.

PubMed

Leong, Kah-Chung; Goodman, Jarid; Packard, Mark G

2015-09-15

In a dual-solution task that can be acquired using either hippocampus-dependent "place" or dorsolateral striatum-dependent "response" learning, emotional arousal induced by unconditioned stimuli (e.g. anxiogenic drug injections or predator odor exposure) biases rats toward response learning. In the present experiments emotionally-arousing conditioned stimuli were used to modulate the relative use of multiple memory systems. In Experiment 1, adult male Long-Evans rats initially received three standard fear-conditioning trials in which a tone (2 kHz, 75 dB) was paired with a brief electrical shock (1 mA, 2s). On day 2, the rats were trained in a dual-solution plus-maze task to swim from the same start arm (South) to a hidden escape platform always located in the same goal arm (East). Immediately following training, rats received post-training re-exposure to the fear-conditioned stimuli (i.e. tone and context) without shock. On day 3, the relative use of place or response learning was assessed on a probe trial in which rats were started from the opposite start arm (North). Post-training re-exposure to fear-conditioned stimuli produced preferential use of a response strategy. In Experiment 2, different rats received fear conditioning and were then trained in a single-solution task that required the use of response learning. Immediately following training, rats received post-training re-exposure to the fear-conditioned stimuli without shock. Re-exposure to fear-conditioned stimuli enhanced memory consolidation in the response learning task. Thus, re-exposure to fear-conditioned stimuli biases rats toward the use of dorsolateral striatum-dependent response learning and enhances memory consolidation of response learning.

4. Eliminating Bias

EPA Pesticide Factsheets

Learn how to eliminate bias from monitoring systems by instituting appropriate installation, operation, and quality assurance procedures. Provides links to download An Operator's Guide to Eliminating Bias in CEM Systems.

5. Intergroup bias.

PubMed

Hewstone, Miles; Rubin, Mark; Willis, Hazel

2002-01-01

This chapter reviews the extensive literature on bias in favor of in-groups at the expense of out-groups. We focus on five issues and identify areas for future research: (a) measurement and conceptual issues (especially in-group favoritism vs. out-group derogation, and explicit vs. implicit measures of bias); (b) modern theories of bias highlighting motivational explanations (social identity, optimal distinctiveness, uncertainty reduction, social dominance, terror management); (c) key moderators of bias, especially those that exacerbate bias (identification, group size, status and power, threat, positive-negative asymmetry, personality and individual differences); (d) reduction of bias (individual vs. intergroup approaches, especially models of social categorization); and (e) the link between intergroup bias and more corrosive forms of social hostility.

6. Are Early-Life Socioeconomic Conditions Directly Related to Birth Outcomes? Grandmaternal Education, Grandchild Birth Weight, and Associated Bias Analyses

PubMed Central

Huang, Jonathan Y.; Gavin, Amelia R.; Richardson, Thomas S.; Rowhani-Rahbar, Ali; Siscovick, David S.; Enquobahrie, Daniel A.

2015-01-01

Grandmaternal education may be related to grandchild birth weight (GBW) through maternal early-life development; however, conventional regression models may be endogenously confounded. Alternative models employing explicit structural assumptions may provide incrementally clearer evidence. We used data from the US National Longitudinal Study of Adolescent to Adult Health (1995–2009; 1,681 mother-child pairs) to estimate “direct effects” of grandmaternal educational level (less than high school, high school diploma or equivalent, or college degree) at the time of the mother's birth on GBW, adjusted for maternal life-course factors: maltreatment as a child, education and income as an adult, prepregnancy overweight, and prenatal smoking. Using conventional and marginal structural model (MSM) approaches, we estimated 54-g (95% confidence interval: −14.0, 122.1) and 87-g (95% confidence interval: 10.9, 162.5) higher GBWs per increase in educational level, respectively. The MSM allowed simultaneous mediation by and adjustment for prepregnancy overweight. Estimates were insensitive to alternate structural assumptions and mediator parameterizations. Bias analysis suggested that a single unmeasured confounder would have to have a strong influence on GBW (approximately 150 g) or be greatly imbalanced across exposure groups (approximately 25%) to completely explain the findings. Coupling an MSM with sensitivity analyses provides some evidence that maternal early-life socioeconomic environment is directly associated with offspring birth weight. PMID:26283086

7. Are Early-Life Socioeconomic Conditions Directly Related to Birth Outcomes? Grandmaternal Education, Grandchild Birth Weight, and Associated Bias Analyses.

PubMed

Huang, Jonathan Y; Gavin, Amelia R; Richardson, Thomas S; Rowhani-Rahbar, Ali; Siscovick, David S; Enquobahrie, Daniel A

2015-10-01

Grandmaternal education may be related to grandchild birth weight (GBW) through maternal early-life development; however, conventional regression models may be endogenously confounded. Alternative models employing explicit structural assumptions may provide incrementally clearer evidence. We used data from the US National Longitudinal Study of Adolescent to Adult Health (1995-2009; 1,681 mother-child pairs) to estimate "direct effects" of grandmaternal educational level (less than high school, high school diploma or equivalent, or college degree) at the time of the mother's birth on GBW, adjusted for maternal life-course factors: maltreatment as a child, education and income as an adult, prepregnancy overweight, and prenatal smoking. Using conventional and marginal structural model (MSM) approaches, we estimated 54-g (95% confidence interval: -14.0, 122.1) and 87-g (95% confidence interval: 10.9, 162.5) higher GBWs per increase in educational level, respectively. The MSM allowed simultaneous mediation by and adjustment for prepregnancy overweight. Estimates were insensitive to alternate structural assumptions and mediator parameterizations. Bias analysis suggested that a single unmeasured confounder would have to have a strong influence on GBW (approximately 150 g) or be greatly imbalanced across exposure groups (approximately 25%) to completely explain the findings. Coupling an MSM with sensitivity analyses provides some evidence that maternal early-life socioeconomic environment is directly associated with offspring birth weight.

8. Analytical description of the injection ratio of self-biased bipolar transistors under the very high injection conditions of ESD events

Gendron, A.; Renaud, P.; Bafleur, M.; Nolhier, N.

2008-05-01

This paper proposes a 1D-analytical description of the injection ratio of a self-biased bipolar transistor under very high current injection conditions. Starting from an expression of the current gain based on the stored charge into the emitter and base regions, we derive a new analytical expression of the current injection ratio. This analytical description demonstrates the presence of an asymptotic limit for the injection ratio at very high current densities, as the ratio of electron/hole mobilities in the case of an NPN transistor and to the ratio of hole/electron saturation velocities for a PNP. Moreover, for the first time, a base narrowing effect is demonstrated and explained in the case of a self-biased PNP, in contrast with the base widening effect (Kirk effect [Kirk CT, A theory of transistor cutoff frequency (fT) falloff at high current densities, IRE Trans Electr Dev 1961: p. 164-73]) reported for lower current density. These results are validated by numerical simulation and show a good agreement with experimental characterizations of transistors especially designed to operate under extreme condition such as electrostatic discharge (ESD) events.

9. Sensory Bias Predicts Postural Stability, Anxiety, and Cognitive Performance in Healthy Adults Walking in Novel Discordant Conditions

NASA Technical Reports Server (NTRS)

Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

2010-01-01

We designed a gait training study that presented combinations of visual flow and support surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their scores on a collective measure of anxiety, cognition, and postural stability in a new discordant environment presented at the conclusion of training (Transfer Test). A treadmill was mounted to a motion base platform positioned 2 m behind a large visual screen. Training consisted of three walking sessions, each within a week of the previous visit, that presented four 5-minute exposures to various combinations of support surface and visual scene manipulations, all lateral sinusoids. The conditions were scene translation only, support surface translation only, simultaneous scene and support surface translations in-phase, and simultaneous scene and support surface translations 180 out-of-phase. During the Transfer Test, the trained participants received a 2-minute novel exposure. A visual sinusoidal roll perturbation, with twice the original flow rate, was superimposed on a sinusoidal support surface roll perturbation that was 90 out of phase with the scene. A high correlation existed between normalized torso translation, measured in the scene-only condition at the first visit, and a combined measure of normalized heart rate, stride frequency, and reaction time at the transfer test. Results suggest that visually dependent participants experience decreased postural stability, increased anxiety, and increased reaction times compared to their less visually dependent counterparts when negotiating novel discordant conditions.

10. Sensory Bias Predicts Postural Stability, Anxiety, and Cognitive Performance in Healthy Adults Walking in Novel Discordant Conditions

NASA Technical Reports Server (NTRS)

Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

2010-01-01

We designed a gait training study that presented combinations of visual flow and support surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their scores on a collective measure of anxiety, cognition, and postural stability in a new discordant environment presented at the conclusion of training (Transfer Test). A treadmill was mounted to a motion base platform positioned 2 m behind a large visual screen. Training consisted of three walking sessions, each within a week of the previous visit, that presented four 5-minute exposures to various combinations of support surface and visual scene manipulations, all lateral sinusoids. The conditions were scene translation only, support surface translation only, simultaneous scene and support surface translations in-phase, and simultaneous scene and support surface translations 180 out-of-phase. During the Transfer Test, the trained participants received a 2-minute novel exposure. A visual sinusoidal roll perturbation, with twice the original flow rate, was superimposed on a sinusoidal support surface roll perturbation that was 90 out of phase with the scene. A high correlation existed between normalized torso translation, measured in the scene-only condition at the first visit, and a combined measure of normalized heart rate, stride frequency, and reaction time at the transfer test. Results suggest that visually dependent participants experience decreased postural stability, increased anxiety, and increased reaction times compared to their less visually dependent counterparts when negotiating novel discordant conditions.

11. Estimating the decomposition of predictive information in multivariate systems

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

12. Estimating the decomposition of predictive information in multivariate systems.

PubMed

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

13. Correcting acoustic Doppler current profiler discharge measurement bias from moving-bed conditions without global positioning during the 2004 Glen Canyon Dam controlled flood on the Colorado River

USGS Publications Warehouse

Gartner, J.W.; Ganju, N.K.

2007-01-01

Discharge measurements were made by acoustic Doppler current profiler at two locations on the Colorado River during the 2004 controlled flood from Glen Canyon Dam, Arizona. Measurement hardware and software have constantly improved from the 1980s such that discharge measurements by acoustic profiling instruments are now routinely made over a wide range of hydrologic conditions. However, measurements made with instruments deployed from moving boats require reliable boat velocity data for accurate measurements of discharge. This is normally accomplished by using special acoustic bottom track pings that sense instrument motion over bottom. While this method is suitable for most conditions, high current flows that produce downstream bed sediment movement create a condition known as moving bed that will bias velocities and discharge to lower than actual values. When this situation exists, one solution is to determine boat velocity with satellite positioning information. Another solution is to use a lower frequency instrument. Discharge measurements made during the 2004 Glen Canyon controlled flood were subject to moving-bed conditions and frequent loss of bottom track. Due to site conditions and equipment availability, the measurements were conducted without benefit of external positioning information or lower frequency instruments. This paper documents and evaluates several techniques used to correct the resulting underestimated discharge measurements. One technique produces discharge values in good agreement with estimates from numerical model and measured hydrographs during the flood. ?? 2007, by the American Society of Limnology and Oceanography, Inc.

14. Multivariate optimization and supplementation strategies for the simultaneous production of amylases, cellulases, xylanases, and proteases by Aspergillus awamori under solid-state fermentation conditions.

PubMed

de Castro, Aline Machado; Castilho, Leda R; Freire, Denise Maria Guimarães

2015-02-01

The production of extracts containing a pool of enzymes for extensive biomass deconstruction can lead to significant advantages in biorefinery applications. In this work, a strain of Aspergillus awamori IOC-3914 was used for the simultaneous production of five groups of hydrolases by solid-state fermentation of babassu cake. Sequential experimental design strategies and multivariate optimization using the desirability function were first used to study temperature, moisture content, and granulometry. After that, further improvements in product yields were achieved by supplementation with other agro-industrial materials. At the end of the study, the production of enzymes was up to 3.3-fold increased, and brewer's spent grains and babassu flour showed to be the best supplements. Maximum activities for endoamylases, exoamylases, cellulases (CMCases), xylanases, and proteases achieved were 197, 106, 20, 835, and 57 U g(-1), respectively. The strain was also able to produce β-glucosidases and debranching amylases (up to 35 and 43 U g(-1), respectively), indicating the potential of its enzyme pool for cellulose and starch degradation.

15. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

16. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

17. Field-aligned neutral wind bias correction scheme for global ionospheric modeling at midlatitudes by assimilating FORMOSAT-3/COSMIC hmF2 data under geomagnetically quiet conditions

Sun, Yang-Yi; Matsuo, Tomoko; Maruyama, Naomi; Liu, Jann-Yenq

2015-04-01

This study demonstrates the usage of a data assimilation procedure, which ingests the FORMOSAT-3/COSMIC (F3/C) hmF2 observations to correct the model wind biases to enhance the capability of the new global Ionosphere Plasmasphere Electrodynamics (IPE) model under geomagnetically quiet conditions. The IPE model is built upon the field line interhemispheric plasma model with a realistic geomagnetic field model and empirical model drivers. The hmF2 observed by the F3/C radio occultation technique is utilized to adjust global thermospheric field-aligned neutral winds (i.e., a component of the thermospheric neutral wind parallel to the magnetic field) at midlatitudes according to a linear relationship between time differentials of the field-aligned wind and hmF2. The adjusted winds are further applied to drive the IPE model. The comparison of the modeled electron density with the observations of F3/C and ground-based GPS receivers at the 2012 March equinox suggests that the modeled electron density can be significantly improved in the midlatitude regions of the Southern Hemisphere, if the wind correction scheme is applied. Moreover, the F3/C observation, the IPE model, and the wind bias correction scheme are applied to study the 2012 Southern Hemisphere Midlatitude Summer Nighttime Anomaly (southern MSNA)/Weddell Sea Anomaly (WSA) event at December solstice for examining the role of the neutral winds in controlling the longitudinal variation of the southern MSNA/WSA behavior. With the help of the wind bias correction scheme, the IPE model better tracks the F3/C-observed eastward movement of the southern MSNA/WSA feature. The apparent eastward movement of the southern MSNA/WSA features in the local time coordinate is primarily caused by the longitudinal variation in the declination angle of the geomagnetic field that controls the field-aligned projection of both geographic meridional and zonal components of the neutral wind. Both the IPE simulations and the F3/C

18. Multivariable PID control by decoupling

Garrido, Juan; Vázquez, Francisco; Morilla, Fernando

2016-04-01

This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.

19. Patterns of ectoparasitism in North American red squirrels (Tamiasciurus hudsonicus): Sex-biases, seasonality, age, and effects on male body condition

PubMed Central

Patterson, Jesse E.H.; Neuhaus, Peter; Kutz, Susan J.; Ruckstuhl, Kathreen E.

2015-01-01

Within many species, males are often more heavily parasitised than females. Several hypotheses have been proposed to explain this phenomenon, including immunocompetence handicaps, sexual size dimorphism and behavioural differences. Here we set out to test the latter two hypotheses and make inferences about the former by assessing patterns of ectoparasitism across various life-history stages in a population of North American red squirrels (Tamiasciurus hudsonicus). We also conducted an ectoparasite removal experiment to investigate the effects of ectoparasites on male body condition. We found that males were more intensely parasitized than females, but only during the mating period. There was no difference in ectoparasite intensity between male and female juveniles at birth or at emergence, suggesting that ectoparasites do not exploit male red squirrels for longer-range natal dispersal. Male red squirrels in our population were slightly heavier than females, however we did not find any evidence that this dimorphism drives male-biased ectoparasitism. Finally, we could not detect an effect of ectoparasite removal on male body mass. Our results lend support to the hypothesis that ectoparasites exploit their male hosts for transmission and that male red squirrels are important for the transmission dynamics of ectoparasites in this population; however, the mechanisms (i.e., immunocompetence, testosterone) are not known. PMID:26236631

20. Journal bias or author bias?

PubMed

Harris, Ian

2016-01-01

I read with interest the comment by Mark Wilson in the Indian Journal of Medical Ethics regarding bias and conflicts of interest in medical journals. Wilson targets one journal (the New England Journal of Medicine: NEJM) and one particular "scandal" to make his point that journals' decisions on publication are biased by commercial conflicts of interest (CoIs). It is interesting that he chooses the NEJM which, by his own admission, had one of the strictest CoI policies and had published widely on this topic. The feeling is that if the NEJM can be guilty, they can all be guilty.

1. Multivariable Control Systems

DTIC Science & Technology

1968-01-01

one). Examples abound of systems with numerous controlled variables, and the modern tendency is toward ever greater utilization of systems and plants of this kind. We call them multivariable control systems (MCS).

2. Elements of a pragmatic approach for dealing with bias and uncertainty in experiments through predictions : experiment design and data conditioning; %22real space%22 model validation and conditioning; hierarchical modeling and extrapolative prediction.

SciTech Connect

Romero, Vicente Jose

2011-11-01

This report explores some important considerations in devising a practical and consistent framework and methodology for utilizing experiments and experimental data to support modeling and prediction. A pragmatic and versatile 'Real Space' approach is outlined for confronting experimental and modeling bias and uncertainty to mitigate risk in modeling and prediction. The elements of experiment design and data analysis, data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. Rationale is given for the various choices underlying the Real Space end-to-end approach. The approach adopts and refines some elements and constructs from the literature and adds pivotal new elements and constructs. Crucially, the approach reflects a pragmatism and versatility derived from working many industrial-scale problems involving complex physics and constitutive models, steady-state and time-varying nonlinear behavior and boundary conditions, and various types of uncertainty in experiments and models. The framework benefits from a broad exposure to integrated experimental and modeling activities in the areas of heat transfer, solid and structural mechanics, irradiated electronics, and combustion in fluids and solids.

3. A general, multivariate definition of causal effects in epidemiology.

PubMed

Flanders, W Dana; Klein, Mitchel

2015-07-01

Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

4. [Biases in the study of prognostic factors].

PubMed

1999-01-01

The main objective is to detail the main biases in the study of prognostic factors. Confounding bias is illustrated with social class, a prognostic factor still discussed. Within selection bias several cases are commented: response bias, specially frequent when the patients of a clinical trial are used; the shortcomings in the formation of an inception cohort; the fallacy of Neyman (bias due to the duration of disease) when the study begins with a cross-sectional study; the selection bias in the treatment of survivors for the different treatment opportunity of those living longer; the bias due to the inclusion of heterogeneous diagnostic groups; and the selection bias due to differential information losses and the use of statistical multivariate procedures. Within the biases during follow-up, an empiric rule to value the impact of the number of losses is given. In information bias the Will Rogers' phenomenon and the usefulness of clinical databases are discussed. Lastly, a recommendation against the use of cutoff points yielded by bivariate analyses to select the variable to be included in multivariate analysis is given.

5. Multivariate Optimization of Conditions for Digestion of Wet Feeds for Dogs and Cats Using a Closed Digester Block and Multielement Determination by ICP-OES.

PubMed

Ávila, Dayara Virgínia Lino; Souza, Sidnei Oliveira; Costa, Silvânio Silvério Lopes; Garcia, Carlos Alexandre Borges; Alves, José do Patrocínio Hora; Araujo, Rennan Geovanny Oliveira; Passos, Elisangela Andrade

2017-09-01

A full 24 factorial design was applied to find the best combination of diluted reagents (HNO3 and H2O2), time, and temperature for the digestion of samples of wet feed for dogs and cats using a closed digestion block. The residual carbon concentration (RCC) was used as the response in the factorial design. All variables and their interactions significantly influenced the digestion of the feed samples, as indicated by the RCC. The conditions established for the digestion of 0.05 g (dry mass) wet feed samples were the addition of 3.0 mol/L HNO3 and 5.0% m/m H2O2 in a final volume of 10 mL, followed by heating in a closed digestion block at a temperature of 170°C for 120 min. Analyses were performed by inductively coupled plasma (ICP) optical emission spectrometry (OES). LOQs ranged from 0.2 μg/g (Mg and Sr) to 51 μg/g (P). Accuracy of the analytical method was confirmed through the analysis of the Standard Reference Materials Tomato Leaves (NIST 1573), Apple Leaves (NIST 1515), and Peach Leaves (NIST 1547). The agreement values achieved ranged from 80.2 ± 0.3% for Ba to 113.8 ± 7.1% for Zn (n = 3). Addition and recovery tests were carried out by adding the analytes to a feed sample at two concentration levels, and the recoveries were between 84 ± 6 and 114 ± 10% for macroelements (Ca, K, Mg, and P; n = 3) and between 88 ± 3 and 113 ± 7% for microelements and trace elements (B, Cu, Fe, Sr, and Zn; n = 3). The precision values achieved for the different elements, expressed as RSDs, were better than 7.3% (Zn; n = 3) except for Cu determination, that was 14.6% (n=3). The optimized analytical method was applied to 10 commercial samples of wet feed for cats and dogs, with the concentrations of Al, B, Ba, Ca, Cu, Fe, K, Mg, Mn, P, Sr, and Zn determined by ICP-OES.

6. Electrical, optical, and material characterizations of blue InGaN light emitting diodes submitted to reverse-bias stress in water vapor condition

SciTech Connect

Chen, Hsiang Chu, Yu-Cheng; Chen, Yun-Ti; Chen, Chian-You; Shei, Shih-Chang

2014-09-07

In this paper, we investigate degradation of InGaN/GaN light emitting diodes (LEDs) under reverse-bias operations in water vapor and dry air. To examine failure origins, electrical characterizations including current-voltage, breakdown current profiles, optical measurement, and multiple material analyses were performed. Our findings indicate that the diffusion of indium atoms in water vapor can expedite degradation. Investigation of reverse-bias stress can help provide insight into the effects of water vapor on LEDs.

7. Bias modification training can alter approach bias and chocolate consumption.

PubMed

Schumacher, Sophie E; Kemps, Eva; Tiggemann, Marika

2016-01-01

Recent evidence has demonstrated that bias modification training has potential to reduce cognitive biases for attractive targets and affect health behaviours. The present study investigated whether cognitive bias modification training could be applied to reduce approach bias for chocolate and affect subsequent chocolate consumption. A sample of 120 women (18-27 years) were randomly assigned to an approach-chocolate condition or avoid-chocolate condition, in which they were trained to approach or avoid pictorial chocolate stimuli, respectively. Training had the predicted effect on approach bias, such that participants trained to approach chocolate demonstrated an increased approach bias to chocolate stimuli whereas participants trained to avoid such stimuli showed a reduced bias. Further, participants trained to avoid chocolate ate significantly less of a chocolate muffin in a subsequent taste test than participants trained to approach chocolate. Theoretically, results provide support for the dual process model's conceptualisation of consumption as being driven by implicit processes such as approach bias. In practice, approach bias modification may be a useful component of interventions designed to curb the consumption of unhealthy foods. Copyright © 2015 Elsevier Ltd. All rights reserved.

8. Multivariate Data EXplorer (MDX)

SciTech Connect

2012-08-01

The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

9. Multivariate bubbles and antibubbles

Fry, John

2014-08-01

In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.

10. Multivariate Data EXplorer (MDX)

SciTech Connect

2012-08-01

The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

11. Transient multivariable sensor evaluation

DOEpatents

Vilim, Richard B.; Heifetz, Alexander

2017-02-21

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

12. Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

13. Introduction to multivariate discrimination

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

14. Multivariate volume rendering

SciTech Connect

Crawfis, R.A.

1996-03-01

This paper presents a new technique for representing multivalued data sets defined on an integer lattice. It extends the state-of-the-art in volume rendering to include nonhomogeneous volume representations. That is, volume rendering of materials with very fine detail (e.g. translucent granite) within a voxel. Multivariate volume rendering is achieved by introducing controlled amounts of noise within the volume representation. Varying the local amount of noise within the volume is used to represent a separate scalar variable. The technique can also be used in image synthesis to create more realistic clouds and fog.

15. Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

16. Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

17. Berkson's bias, selection bias, and missing data.

PubMed

Westreich, Daniel

2012-01-01

Although Berkson's bias is widely recognized in the epidemiologic literature, it remains underappreciated as a model of both selection bias and bias due to missing data. Simple causal diagrams and 2 × 2 tables illustrate how Berkson's bias connects to collider bias and selection bias more generally, and show the strong analogies between Berksonian selection bias and bias due to missing data. In some situations, considerations of whether data are missing at random or missing not at random are less important than the causal structure of the missing data process. Although dealing with missing data always relies on strong assumptions about unobserved variables, the intuitions built with simple examples can provide a better understanding of approaches to missing data in real-world situations.

18. Estimating uncertainty in multivariate responses to selection.

PubMed

Stinchcombe, John R; Simonsen, Anna K; Blows, Mark W

2014-04-01

Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson-Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian-MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson-Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses.

19. Multivariate analyses in microbial ecology

PubMed Central

Ramette, Alban

2007-01-01

Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology. PMID:17892477

20. Observational biases for transiting planets

Kipping, David M.; Sandford, Emily

2016-12-01

Observational biases distort our view of nature, such that the patterns we see within a surveyed population of interest are often unrepresentative of the truth we seek. Transiting planets currently represent the most informative data set on the ensemble properties of exoplanets within 1 au of their star. However, the transit method is inherently biased due to both geometric and detection-driven effects. In this work, we derive the overall observational biases affecting the most basic transit parameters from first principles. By assuming a trapezoidal transit and using conditional probability, we infer the expected distribution of these terms both as a joint distribution and in a marginalized form. These general analytic results provide a baseline against which to compare trends predicted by mission-tailored injection/recovery simulations and offer a simple way to correct for observational bias. Our results explain why the observed population of transiting planets displays a non-uniform impact parameter distribution, with a bias towards near-equatorial geometries. We also find that the geometric bias towards observed planets transiting near periastron is attenuated by the longer durations which occur near apoastron. Finally, we predict that the observational bias with respect to ratio-of-radii is super-quadratic, scaling as (RP/R⋆)5/2, driven by an enhanced geometric transit probability and modestly longer durations.

1. The Likelihood of Injury Among Bias Crimes: An Analysis of General and Specific Bias Types.

PubMed

Pezzella, Frank S; Fetzer, Matthew D

2015-06-18

In 2009, President Barack Obama signed the Mathew Sheppard and James Byrd Jr. Hate Crimes Protection act and thereby extended the list of previously protected classes of victims from actual or perceived race, color, religion, national origin, disability and sex orientation to gender and gender identity. Over 45 states, the District of Columbia and the federal government now include hate crime statutes that increase penalties when offenders perpetrate hate crimes against protected classes of victims. Penalty enhancement statutes sanction unlawful bias conduct arguably because they result in more severe injuries relative to non-bias conduct. We contend that physical injuries vary by bias type and are not equally injurious. Data on bias crimes was analyzed from the National Incident Based Reporting System. Descriptive patterns of bias crimes were identified by offense type, bias motivation and major and minor injuries. Using Multivariate analyses, we found an escalating trend of violence against racial minorities. Moreover, relative to non-bias crimes, only anti-White and anti-lesbian bias crimes experienced our two prong "animus" criteria of disproportionate prevalence and severity of injury. However, when compared to anti-White bias, anti-Black bias crimes were more prevalent and likely to suffer serious injuries. Implications for hate crime jurisprudence are discussed.

2. Correlates of Perceptual Orientation Biases in Human Primary Visual Cortex.

PubMed

Patten, Matthew L; Mannion, Damien J; Clifford, Colin W G

2017-05-03

Vision can be considered as a process of probabilistic inference. In a Bayesian framework, perceptual estimates from sensory information are combined with prior knowledge, with a stronger influence of the prior when the sensory evidence is less certain. Here, we explored the behavioral and neural consequences of manipulating stimulus certainty in the context of orientation processing. First, we asked participants to judge whether a stimulus was oriented closer to vertical or the clockwise primary oblique (45°) for two stimulus types (spatially filtered noise textures and sinusoidal gratings) and three manipulations of certainty (orientation bandwidth, contrast, and duration). We found that participants consistently had a bias toward reporting orientation as closer to 45° during conditions of high certainty and that this bias was reduced when sensory evidence was less certain. Second, we measured event-related fMRI BOLD responses in human primary visual cortex (V1) and manipulated certainty via stimulus contrast (100% vs 3%). We then trained a multivariate classifier on the pattern of responses in V1 to cardinal and primary oblique orientations. We found that the classifier showed a bias toward classifying orientation as oblique at high contrast but categorized a wider range of orientations as cardinal for low-contrast stimuli. Orientation classification based on data from V1 thus paralleled the perceptual biases revealed through the behavioral experiments. This pattern of bias cannot be explained simply by a prior for cardinal orientations.SIGNIFICANCE STATEMENT Our perception of the world around us is biased through prior expectations rather than necessarily reflecting the true state of our environment. Here, we investigate biases in the visual processing of spatial orientation to understand how prior expectations and current sensory information interact to generate a percept. By degrading visual input in various ways, we are able to quantify the extent to which

3. Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.

PubMed

van Walraven, Carl

2017-04-01

Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias. Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates. Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized. Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

4. Angles of multivariable root loci

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1982-01-01

A generalized eigenvalue problem is demonstrated to be useful for computing the multivariable root locus, particularly when obtaining the arrival angles to finite transmission zeros. The multivariable root loci are found for a linear, time-invariant output feedback problem. The problem is then employed to compute a closed-loop eigenstructure. The method of computing angles on the root locus is demonstrated, and the method is extended to a multivariable optimal root locus.

5. Estimating Bias in Test Items Utilizing Principal Components Analysis and the General Linear Solution.

ERIC Educational Resources Information Center

Merz, William R.

A number of methods have been used to identify potentially biased items within a test. These methods examine one item at a time and do not deal with the complex interrelationships among items or among items and the potentially biasing elements. The use of multivariate procedures to assess whether or not items are biased and to obtain clues about…

6. Assessment of bias for MRI diffusion tensor imaging using SIMEX.

PubMed

Lauzon, Carolyn B; Asman, Andrew J; Crainiceanu, Ciprian; Caffo, Brian C; Landman, Bennett A

2011-01-01

Diffusion Tensor Imaging (DTI) is a Magnetic Resonance Imaging method for measuring water diffusion in vivo. One powerful DTI contrast is fractional anisotropy (FA). FA reflects the strength of water's diffusion directional preference and is a primary metric for neuronal fiber tracking. As with other DTI contrasts, FA measurements are obscured by the well established presence of bias. DTI bias has been challenging to assess because it is a multivariable problem including SNR, six tensor parameters, and the DTI collection and processing method used. SIMEX is a modem statistical technique that estimates bias by tracking measurement error as a function of added noise. Here, we use SIMEX to assess bias in FA measurements and show the method provides; i) accurate FA bias estimates, ii) representation of FA bias that is data set specific and accessible to non-statisticians, and iii) a first time possibility for incorporation of bias into DTI data analysis.

7. Cognitive bias modification for interpretation with and without prior repetitive negative thinking to reduce worry and rumination in generalised anxiety disorder and depression: protocol for a multisession experimental study with an active control condition.

PubMed

Krahé, Charlotte; Mathews, Andrew; Whyte, Jessica; Hirsch, Colette R

2016-12-16

Worry and rumination are two forms of repetitive thinking characterised by their negative content and apparently uncontrollable nature. Although worry and rumination share common features and have been conceptualised as part of a transdiagnostic repetitive negative thinking (RNT) process, it remains unclear whether they share the same underlying cognitive mechanisms. This multisession experimental study investigates the tendency to make negative interpretations regarding ambiguous information as a cognitive mechanism underlying RNT. We compare multisession cognitive bias modification for interpretations (CBM-I) with an active control condition to examine whether repeatedly training positive interpretations reduces worry and rumination in individuals with generalised anxiety disorder or depression, respectively. Further, we examine the potential modulatory effects of engaging in RNT immediately prior to CBM-I. A community sample of individuals meeting diagnostic criteria for either generalised anxiety disorder (n=60) or current major depressive episode (n=60) will be randomly allocated to CBM-I with prior RNT, CBM-I without prior RNT (ie, standard CBM-I), or an active control (no resolution of ambiguity) condition. All conditions receive a 3-week internet-based intervention consisting of one initial session at the first study visit and nine home-based sessions of CBM-I training (or active control). We will assess and compare the effects of CBM-I with and without prior RNT on 'near-transfer' measures of interpretation bias closely related to the training as well as 'far-transfer' outcomes related to RNT and emotional distress. Impact on questionnaire measures will additionally be assessed at 1-month follow-up. Multigroup analyses will be conducted to assess the impact of CBM-I on near-transfer and far-transfer outcome measures. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

8. Cognitive bias modification for interpretation with and without prior repetitive negative thinking to reduce worry and rumination in generalised anxiety disorder and depression: protocol for a multisession experimental study with an active control condition

PubMed Central

Mathews, Andrew; Whyte, Jessica; Hirsch, Colette R

2016-01-01

Introduction Worry and rumination are two forms of repetitive thinking characterised by their negative content and apparently uncontrollable nature. Although worry and rumination share common features and have been conceptualised as part of a transdiagnostic repetitive negative thinking (RNT) process, it remains unclear whether they share the same underlying cognitive mechanisms. This multisession experimental study investigates the tendency to make negative interpretations regarding ambiguous information as a cognitive mechanism underlying RNT. We compare multisession cognitive bias modification for interpretations (CBM-I) with an active control condition to examine whether repeatedly training positive interpretations reduces worry and rumination in individuals with generalised anxiety disorder or depression, respectively. Further, we examine the potential modulatory effects of engaging in RNT immediately prior to CBM-I. Design, methods and analysis A community sample of individuals meeting diagnostic criteria for either generalised anxiety disorder (n=60) or current major depressive episode (n=60) will be randomly allocated to CBM-I with prior RNT, CBM-I without prior RNT (ie, standard CBM-I), or an active control (no resolution of ambiguity) condition. All conditions receive a 3-week internet-based intervention consisting of one initial session at the first study visit and nine home-based sessions of CBM-I training (or active control). We will assess and compare the effects of CBM-I with and without prior RNT on ‘near-transfer’ measures of interpretation bias closely related to the training as well as ‘far-transfer’ outcomes related to RNT and emotional distress. Impact on questionnaire measures will additionally be assessed at 1-month follow-up. Multigroup analyses will be conducted to assess the impact of CBM-I on near-transfer and far-transfer outcome measures. PMID:27986741

9. Biases in the production and reception of collective knowledge: the case of hindsight bias in Wikipedia.

PubMed

Oeberst, Aileen; von der Beck, Ina; D Back, Mitja; Cress, Ulrike; Nestler, Steffen

2017-04-17

The Web 2.0 enabled collaboration at an unprecedented level. In one of the flagships of mass collaboration-Wikipedia-a large number of authors socially negotiate the world's largest compendium of knowledge. Several guidelines in Wikipedia restrict contributions to verifiable information from reliable sources to ensure recognized knowledge. Much psychological research demonstrates, however, that individual information processing is biased. This poses the question whether individual biases translate to Wikipedia articles or whether they are prevented by its guidelines. The present research makes use of hindsight bias to examine this question. To this end, we analyzed foresight and hindsight versions of Wikipedia articles regarding a broad variety of events (Study 1). We found the majority of articles not to contain traces of hindsight bias-contrary to prior individual research. However, for a particular category of events-disasters-we found robust evidence for hindsight bias. In a lab experiment (Study 2), we then examined whether individuals' hindsight bias is translated into articles under controlled conditions and tested whether collaborative writing-as present in Wikipedia-affects the resultant bias (vs. individual writing). Finally, we investigated the impact of biased Wikipedia articles on readers (Study 3). As predicted, biased articles elicited a hindsight bias in readers, who had not known of the event previously. Moreover, biased articles also affected individuals who knew about the event already, and who had already developed a hindsight bias: biased articles further increased their hindsight.

10. Modular multivariable control improves hydrocracking

SciTech Connect

Chia, T.L.; Lefkowitz, I.; Tamas, P.D.

1996-10-01

Modular multivariable control (MMC), a system of interconnected, single process variable controllers, can be a user-friendly, reliable and cost-effective alternative to centralized, large-scale multivariable control packages. MMC properties and features derive directly from the properties of the coordinated controller which, in turn, is based on internal model control technology. MMC was applied to a hydrocracking unit involving two process variables and three controller outputs. The paper describes modular multivariable control, MMC properties, tuning considerations, application at the DCS level, constraints handling, and process application and results.

11. Solar array/spacecraft biasing

NASA Technical Reports Server (NTRS)

Fitzgerald, D. J.

1981-01-01

Biasing techniques and their application to the control of spacecraft potential is discussed. Normally when a spacecraft is operated with ion thrusters, the spacecraft will be 10-20 volts negative of the surrounding plasma. This will affect scientific measurements and will allow ions from the charge-exchange plasma to bombard the spacecraft surfaces with a few tens of volts of energy. This condition may not be tolerable. A proper bias system is described that can bring the spacecraft to or near the potential of the surrounding plasma.

12. Solar array/spacecraft biasing

NASA Technical Reports Server (NTRS)

Fitzgerald, D. J.

1981-01-01

Biasing techniques and their application to the control of spacecraft potential is discussed. Normally when a spacecraft is operated with ion thrusters, the spacecraft will be 10-20 volts negative of the surrounding plasma. This will affect scientific measurements and will allow ions from the charge-exchange plasma to bombard the spacecraft surfaces with a few tens of volts of energy. This condition may not be tolerable. A proper bias system is described that can bring the spacecraft to or near the potential of the surrounding plasma.

ERIC Educational Resources Information Center

Yancey, George

2012-01-01

Whether political and/or religious academic bias exists is a question with important ramifications for the educational institutions. Those arguing for the presence of such bias contend that political conservatives and the highly religious in academia are marginalized and face discrimination. The question of academic bias tends to be cast in a…

14. Oaths and hypothetical bias.

PubMed

Stevens, T H; Tabatabaei, Maryam; Lass, Daniel

2013-09-30

Results from experiments using an oath to eliminate hypothetical bias in stated preference valuation are presented. An oath has several potential advantages relative to other methods for reducing hypothetical bias. Our empirical results suggest that with an oath, mean hypothetical payments are not different from mean actual payments and that when controlling for experimental participants' characteristics using regression analyses, the oath eliminated hypothetical bias.

15. Demonstrating the Correspondence Bias

ERIC Educational Resources Information Center

Howell, Jennifer L.; Shepperd, James A.

2011-01-01

Among the best-known and most robust biases in person perception is the correspondence bias--the tendency for people to make dispositional, rather than situational, attributions for an actor's behavior. The correspondence bias appears in virtually every social psychology textbook and in many introductory psychology textbooks, yet the authors'…

16. Demonstrating the Correspondence Bias

ERIC Educational Resources Information Center

Howell, Jennifer L.; Shepperd, James A.

2011-01-01

Among the best-known and most robust biases in person perception is the correspondence bias--the tendency for people to make dispositional, rather than situational, attributions for an actor's behavior. The correspondence bias appears in virtually every social psychology textbook and in many introductory psychology textbooks, yet the authors'…

17. Multivariate Granger causality and generalized variance.

PubMed

Barrett, Adam B; Barnett, Lionel; Seth, Anil K

2010-04-01

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

18. Multivariate Granger causality and generalized variance

Barrett, Adam B.; Barnett, Lionel; Seth, Anil K.

2010-04-01

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or “ensembles” of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke’s seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define “partial” Granger causality in the multivariate context and we also motivate reformulations of “causal density” and “Granger autonomy.” Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

19. MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES

PubMed Central

Zhu, Hongtu; Li, Runze; Kong, Linglong

2012-01-01

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment. PMID:23645942

20. MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES

PubMed Central

Zhu, Hongtu; Li, Runze; Kong, Linglong

2012-01-01

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment. PMID:12926711

1. MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES.

PubMed

Zhu, Hongtu; Li, Runze; Kong, Linglong

2012-10-01

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.

2. Outcome predictability biases learning.

PubMed

Griffiths, Oren; Mitchell, Chris J; Bethmont, Anna; Lovibond, Peter F

2015-01-01

Much of contemporary associative learning research is focused on understanding how and when the associative history of cues affects later learning about those cues. Very little work has investigated the effects of the associative history of outcomes on human learning. Three experiments extended the "learned irrelevance" paradigm from the animal conditioning literature to examine the influence of an outcome's prior predictability on subsequent learning of relationships between cues and that outcome. All 3 experiments found evidence for the idea that learning is biased by the prior predictability of the outcome. Previously predictable outcomes were readily associated with novel predictive cues, whereas previously unpredictable outcomes were more readily associated with novel nonpredictive cues. This finding highlights the importance of considering the associative history of outcomes, as well as cues, when interpreting multistage designs. Associative and cognitive explanations of this certainty matching effect are discussed.

3. An Iterative Item Bias Detection Method.

ERIC Educational Resources Information Center

Van Der Flier, Henk; And Others

1984-01-01

Two strategies for assessing item bias are discussed: methods comparing item difficulties unconditional on ability and methods comparing probabilities of response conditional on ability. Results suggest that the iterative logit method is an improvement on the noniterative one and is efficient in detecting biased and unbiased items. (Author/DWH)

4. Queries for Bias Testing

NASA Technical Reports Server (NTRS)

Gordon, Diana F.

1992-01-01

Selecting a good bias prior to concept learning can be difficult. Therefore, dynamic bias adjustment is becoming increasingly popular. Current dynamic bias adjustment systems, however, are limited in their ability to identify erroneous assumptions about the relationship between the bias and the target concept. Without proper diagnosis, it is difficult to identify and then remedy faulty assumptions. We have developed an approach that makes these assumptions explicit, actively tests them with queries to an oracle, and adjusts the bias based on the test results.

5. Multivariate analysis in thoracic research

PubMed Central

Mengual-Macenlle, Noemí; Marcos, Pedro J.; Golpe, Rafael

2015-01-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use. PMID:25922743

6. Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

7. Observations and Models of Galaxy Assembly Bias

Campbell, Duncan A.

2017-01-01

The assembly history of dark matter haloes imparts various correlations between a halo’s physical properties and its large scale environment, i.e. assembly bias. It is common for models of the galaxy-halo connection to assume that galaxy properties are only a function of halo mass, implicitly ignoring how assembly bias may affect galaxies. Recently, programs to model and constrain the degree to which galaxy properties are influenced by assembly bias have been undertaken; however, the extent and character of galaxy assembly bias remains a mystery. Nevertheless, characterizing and modeling galaxy assembly bias is an important step in understanding galaxy evolution and limiting any systematic effects assembly bias may pose in cosmological measurements using galaxy surveys.I will present work on modeling and constraining the effect of assembly bias in two galaxy properties: stellar mass and star-formation rate. Conditional abundance matching allows for these galaxy properties to be tied to halo formation history to a variable degree, making studies of the relative strength of assembly bias possible. Galaxy-galaxy clustering and galactic conformity, the degree to which galaxy color is correlated between neighbors, are sensitive observational measures of galaxy assembly bias. I will show how these measurements can be used to constrain galaxy assembly bias and the peril of ignoring it.

8. Selection bias in rheumatic disease research

PubMed Central

Choi, Hyon K.; Nguyen, Uyen-Sa; Niu, Jingbo; Danaei, Goodarz; Zhang, Yuqing

2014-01-01

The identification of modifiable risk factors for the development of rheumatic conditions and their sequelae is crucial for reducing the substantial worldwide burden of these diseases. However, the validity of such research can be threatened by sources of bias, including confounding, measurement and selection biases. In this Review, we discuss potentially major issues of selection bias—a type of bias frequently overshadowed by other bias and feasibility issues, despite being equally or more problematic—in key areas of rheumatic disease research. We present index event bias (a type of selection bias) as one of the potentially unifying reasons behind some unexpected findings, such as the ‘risk factor paradox’—a phenomenon exemplified by the discrepant effects of certain risk factors on the development versus the progression of osteoarthritis (OA) or rheumatoid arthritis (RA). We also discuss potential selection biases owing to differential loss to follow-up in RA and OA research, as well as those due to the depletion of susceptibles (prevalent user bias) and immortal time bias. The lesson remains that selection bias can be ubiquitous and, therefore, has the potential to lead the field astray. Thus, we conclude with suggestions to help investigators avoid such issues and limit the impact on future rheumatology research. PMID:24686510

9. "Catching" Social Bias.

PubMed

Skinner, Allison L; Meltzoff, Andrew N; Olson, Kristina R

2017-02-01

Identifying the origins of social bias is critical to devising strategies to overcome prejudice. In two experiments, we tested the hypothesis that young children can catch novel social biases from brief exposure to biased nonverbal signals demonstrated by adults. Our results are consistent with this hypothesis. In Experiment 1, we found that children who were exposed to a brief video depicting nonverbal bias in favor of one individual over another subsequently explicitly preferred, and were more prone to behave prosocially toward, the target of positive nonverbal signals. Moreover, in Experiment 2, preschoolers generalized such bias to other individuals. The spread of bias observed in these experiments lays a critical foundation for understanding the way that social biases may develop and spread early in childhood.

10. Multivariate Visual Explanation for High Dimensional Datasets

PubMed Central

Barlowe, Scott; Zhang, Tianyi; Liu, Yujie; Yang, Jing; Jacobs, Donald

2010-01-01

Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach. PMID:20694164

11. Recursive bias estimation for high dimensional smoothers

SciTech Connect

Hengartner, Nicolas W; Matzner-lober, Eric; Cornillon, Pierre - Andre

2008-01-01

In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to biased smoothers. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting smoother has a small variance but a substantial bias. In this paper, we propose to iteratively correct the bias initial estimator by an estimate of the latter obtained by smoothing the residuals. We examine in detail the convergence of the iterated procedure for classical smoothers and relate our procedure to L{sub 2}-Boosting. We apply our method to simulated and real data and show that our method compares favorably with existing procedures.

12. Assessing attentional biases with stuttering.

PubMed

Lowe, Robyn; Menzies, Ross; Packman, Ann; O'Brian, Sue; Jones, Mark; Onslow, Mark

2016-01-01

Many adults who stutter presenting for speech treatment experience social anxiety disorder. The presence of mental health disorders in adults who stutter has been implicated in a failure to maintain speech treatment benefits. Contemporary theories of social anxiety disorder propose that the condition is maintained by negative cognitions and information processing biases. Consistent with cognitive theories, the probe detection task has shown that social anxiety is associated with an attentional bias to avoid social information. This information processing bias is suggested to be involved in maintaining anxiety. Evidence is emerging for information processing biases being involved with stuttering. This study investigated information processing in adults who stutter using the probe detection task. Information processing biases have been implicated in anxiety maintenance in social anxiety disorder and therefore may have implications for the assessment and treatment of stuttering. It was hypothesized that stuttering participants compared with control participants would display an attentional bias to avoid attending to social information. Twenty-three adults who stutter and 23 controls completed a probe detection task in which they were presented with pairs of photographs: a face displaying an emotional expression-positive, negative or neutral-and an everyday household object. All participants were subjected to a mild social threat induction being told they would speak to a small group of people on completion of the task. The stuttering group scored significantly higher than controls for trait anxiety, but did not differ from controls on measures of social anxiety. Non-socially anxious adults who stutter did not display an attentional bias to avoid looking at photographs of faces relative to everyday objects. Higher scores on trait anxiety were positively correlated with attention towards photographs of negative faces. Attentional biases as assessed by the probe

13. On the power of the test for cluster bias.

PubMed

Jak, Suzanne; Oort, Frans J

2015-11-01

Cluster bias refers to measurement bias with respect to the clustering variable in multilevel data. The absence of cluster bias implies absence of bias with respect to any cluster-level (level 2) variable. The variables that possibly cause the bias do not have to be measured to test for cluster bias. Therefore, the test for cluster bias serves as a global test of measurement bias with respect to any level 2 variable. However, the validity of the global test depends on the Type I and Type II error rates of the test. We compare the performance of the test for cluster bias with the restricted factor analysis (RFA) test, which can be used if the variable that leads to measurement bias is measured. It appeared that the RFA test has considerably more power than the test for cluster bias. However, the false positive rates of the test for cluster bias were generally around the expected values, while the RFA test showed unacceptably high false positive rates in some conditions. We conclude that if no significant cluster bias is found, still significant bias with respect to a level 2 violator can be detected with an RFA model. Although the test for cluster bias is less powerful, an advantage of the test is that the cause of the bias does not need to be measured, or even known.

14. Bias in clinical chemistry.

PubMed

Theodorsson, Elvar; Magnusson, Bertil; Leito, Ivo

2014-01-01

Clinical chemistry uses automated measurement techniques and medical knowledge in the interest of patients and healthy subjects. Automation has reduced repeatability and day-to-day variation considerably. Bias has been reduced to a lesser extent by reference measurement systems. It is vital to minimize clinically important bias, in particular bias within conglomerates of laboratories that measure samples from the same patients. Small and variable bias components will over time show random error properties and conventional random-error based methods for calculating measurement uncertainty can then be applied. The present overview of bias presents the general principles of error and uncertainty concepts, terminology and analysis, and suggests methods to minimize bias and measurement uncertainty in the interest of healthcare.

15. Bias in research.

PubMed

Simundić, Ana-Maria

2013-01-01

By writing scientific articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any deviation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research.

16. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

PubMed Central

Dunson, David B.

2013-01-01

Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563

17. Multivariate stochastic simulation with subjective multivariate normal distributions

Treesearch

P. J. Ince; J. Buongiorno

1991-01-01

In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

18. Adaptable history biases in human perceptual decisions

PubMed Central

Abrahamyan, Arman; Silva, Laura Luz; Dakin, Steven C.; Gardner, Justin L.

2016-01-01

19. Interpretation biases in paranoia.

PubMed

Savulich, George; Freeman, Daniel; Shergill, Sukhi; Yiend, Jenny

2015-01-01

Information in the environment is frequently ambiguous in meaning. Emotional ambiguity, such as the stare of a stranger, or the scream of a child, encompasses possible good or bad emotional consequences. Those with elevated vulnerability to affective disorders tend to interpret such material more negatively than those without, a phenomenon known as "negative interpretation bias." In this study we examined the relationship between vulnerability to psychosis, measured by trait paranoia, and interpretation bias. One set of material permitted broadly positive/negative (valenced) interpretations, while another allowed more or less paranoid interpretations, allowing us to also investigate the content specificity of interpretation biases associated with paranoia. Regression analyses (n=70) revealed that trait paranoia, trait anxiety, and cognitive inflexibility predicted paranoid interpretation bias, whereas trait anxiety and cognitive inflexibility predicted negative interpretation bias. In a group comparison those with high levels of trait paranoia were negatively biased in their interpretations of ambiguous information relative to those with low trait paranoia, and this effect was most pronounced for material directly related to paranoid concerns. Together these data suggest that a negative interpretation bias occurs in those with elevated vulnerability to paranoia, and that this bias may be strongest for material matching paranoid beliefs. We conclude that content-specific biases may be important in the cause and maintenance of paranoid symptoms.

20. Mindfulness reduces the correspondence bias.

PubMed

Hopthrow, Tim; Hooper, Nic; Mahmood, Lynsey; Meier, Brian P; Weger, Ulrich

2017-03-01

The correspondence bias (CB) refers to the idea that people sometimes give undue weight to dispositional rather than situational factors when explaining behaviours and attitudes. Three experiments examined whether mindfulness, a non-judgmental focus on the present moment, could reduce the CB. Participants engaged in a brief mindfulness exercise (the raisin task), a control task, or an attention to detail task before completing a typical CB measure involving an attitude-attribution paradigm. The results indicated that participants in the mindfulness condition experienced a significant reduction in the CB compared to participants in the control or attention to detail conditions. These results suggest that mindfulness training can play a unique role in reducing social biases related to person perception.

1. Identification of multivariate linear systems

SciTech Connect

Griffith, J.M.

1981-01-01

This paper considers the problem of modeling multivariate linear systems where noisy output measurements are the only available data. The techniques presented are valid for a class of canonical forms. Results from several simulations demonstrate the capability for structure and parameter estimation.

2. Multivariate Model of Infant Competence.

ERIC Educational Resources Information Center

Kierscht, Marcia Selland; Vietze, Peter M.

This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…

3. The Bias Fallacy

ERIC Educational Resources Information Center

Linvill, Darren L.

2013-01-01

Do those who complain about liberal bias in higher education have any actionable point at all? Critics of the politicization of higher education claim that political partisanship in the classroom is pervasive and that it affects student learning. Although the existence of such partisanship has not been empirically proven, allegations of bias are…

4. Adaptive Variable Bias Magnetic Bearing Control

NASA Technical Reports Server (NTRS)

Johnson, Dexter; Brown, Gerald V.; Inman, Daniel J.

1998-01-01

Most magnetic bearing control schemes use a bias current with a superimposed control current to linearize the relationship between the control current and the force it delivers. With the existence of the bias current, even in no load conditions, there is always some power consumption. In aerospace applications, power consumption becomes an important concern. In response to this concern, an alternative magnetic bearing control method, called Adaptive Variable Bias Control (AVBC), has been developed and its performance examined. The AVBC operates primarily as a proportional-derivative controller with a relatively slow, bias current dependent, time-varying gain. The AVBC is shown to reduce electrical power loss, be nominally stable, and provide control performance similar to conventional bias control. Analytical, computer simulation, and experimental results are presented in this paper.

5. Sampling Bias on Cup Anemometer Mean Winds

Kristensen, L.; Hansen, O. F.; Højstrup, J.

2003-10-01

The cup anemometer signal can be sampled in several ways to obtain the mean wind speed. Here we discuss the sampling of series of mean wind speeds from consecutive rotor rotations, followed by unweighted and weighted averaging. It is shown that the unweighted averaging creates a positive bias on the long-term mean wind speed, which is at least one order of magnitude larger than the positive bias from the weighted averaging, also known as the sample-and-hold method. For a homogeneous, neutrally stratified flow the first biases are 1%-2%. For comparison the biases due to fluctuations of the three wind velocity components and due to calibration non-linearity are determined under the same conditions. The largest of these is the v-bias from direction fluctuations. The calculations pertain to the Risø P2546A model cup anemometer.

6. Cognitive bias modification approaches to anxiety.

PubMed

MacLeod, Colin; Mathews, Andrew

2012-01-01

Clinical anxiety disorders and elevated levels of anxiety vulnerability are characterized by cognitive biases, and this processing selectivity has been implicated in theoretical accounts of these conditions. We review research that has sought to evaluate the causal contributions such biases make to anxiety dysfunction and to therapeutically alleviate anxiety using cognitive-bias modification (CBM) procedures. After considering the purpose and nature of CBM methodologies, we show that variants designed to modify selective attention (CBM-A) or interpretation (CBM-I) have proven capable of reducing anxiety vulnerability and ameliorating dysfunctional anxiety. In addition to supporting the causal role of cognitive bias in anxiety vulnerability and dysfunction and illuminating the mechanisms that underpin such bias, the findings suggest that CBM procedures may have therapeutic promise within clinical settings. We discuss key issues within this burgeoning field of research and suggest future directions CBM research should take to maximize its theoretical and applied value.

7. Multivariate Analog of Hays Omega-Squared.

ERIC Educational Resources Information Center

Sachdeva, Darshan

The multivariate analog of Hays omega-squared for estimating the strength of the relationship in the multivariate analysis of variance has been proposed in this paper. The multivariate omega-squared is obtained through the use of Wilks' lambda test criterion. Application of multivariate omega-squared to a numerical example has been provided so as…

8. Multivariate semiparametric spatial methods for imaging data.

PubMed

Chen, Huaihou; Cao, Guanqun; Cohen, Ronald A

2017-04-01

Univariate semiparametric methods are often used in modeling nonlinear age trajectories for imaging data, which may result in efficiency loss and lower power for identifying important age-related effects that exist in the data. As observed in multiple neuroimaging studies, age trajectories show similar nonlinear patterns for the left and right corresponding regions and for the different parts of a big organ such as the corpus callosum. To incorporate the spatial similarity information without assuming spatial smoothness, we propose a multivariate semiparametric regression model with a spatial similarity penalty, which constrains the variation of the age trajectories among similar regions. The proposed method is applicable to both cross-sectional and longitudinal region-level imaging data. We show the asymptotic rates for the bias and covariance functions of the proposed estimator and its asymptotic normality. Our simulation studies demonstrate that by borrowing information from similar regions, the proposed spatial similarity method improves the efficiency remarkably. We apply the proposed method to two neuroimaging data examples. The results reveal that accounting for the spatial similarity leads to more accurate estimators and better functional clustering results for visualizing brain atrophy pattern.Functional clustering; Longitudinal magnetic resonance imaging (MRI); Penalized B-splines; Region of interest (ROI); Spatial penalty.

9. Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

10. Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

11. Biased predecision processing.

PubMed

Brownstein, Aaron L

2003-07-01

Decision makers conduct biased predecision processing when they restructure their mental representation of the decision environment to favor one alternative before making their choice. The question of whether biased predecision processing occurs has been controversial since L. Festinger (1957) maintained that it does not occur. The author reviews relevant research in sections on theories of cognitive dissonance, decision conflict, choice certainty, action control, action phases, dominance structuring, differentiation and consolidation, constructive processing, motivated reasoning, and groupthink. Some studies did not find evidence of biased predecision processing, but many did. In the Discussion section, the moderators are summarized and used to assess the theories.

12. Asymptotic Biases in Exploratory Factor Analysis and Structural Equation Modeling

ERIC Educational Resources Information Center

Ogasawara, Haruhiko

2004-01-01

Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas…

13. Remote Impact of Extratropical Thermal Bias on Tropical Biases in the Norwegian Earth System Model

Koseki, Shunya; Losada, Teresa; Keenlyside, Noel; Toniazzo, Thomas; Castano-Tierno, Antonio; Rodriguez-Fonseca, Belen; Demissie, Teferi; Mechoso, Roberto

2016-04-01

One of large biases exhibited by most state-of-the-art coupled general circulation models (CGCMs) is warm sea surface temperature (SST) in the tropical ocean. Due to the warm SST bias, CGCMs fails to represent the location of intertropical convergence zone (ITCZ) realistically. Other common bias is warm SST over the Southern Ocean partly because of less reproduction of stratocumulus over the Southern Ocean. Some previous studies show that the ITCZ position is affected by the extratropical thermal condition. In this study, we explore a connection between the extratropical warm SST bias and tropical biases in the Norwegian Earth System Model (NorESM). The control simulation of NorESM has the common tropical biases and warm bias over the Southern Ocean. NorESM overestimates the downward shortwave radiation flux over the Southern Ocean and underestimates the low-level cloud formation (in particular, between 40S and 30S). The more incoming shortwave radiation is consistent with the warm SST bias over the Southern Ocean. We conduct a sensitivity experiment in which the incoming shortwave radiation at the top of atmosphere is reduced artificially only between 30S and 60S. The reduced shortwave radiation cools the SST in the Southern Ocean. Interestingly, the annual-mean rainfall over the tropics is reduced (amplified) to the south (north) of the equator. Especially, the double-ITCZ over the tropical Pacific Ocean is diminished in the sensitivity experiment. Moreover, warm SST biases in the tropical ocean are also reduced. Over the tropical Atlantic, the reduction of biases is more remarkable in MAM and JJA: westerly bias over the equatorial Atlantic is reduced and SST is cooler compared to control simulation. Consequently, the rainfall increases (decreases) in the north (south) of the equator, that is, the sensitivity experiment shows more realistic climatological state. This result indicates that a part of tropical biases in NorESM is associated with the warm SST bias in

14. Clines Arc through Multivariate Morphospace.

PubMed

Lohman, Brian K; Berner, Daniel; Bolnick, Daniel I

2017-04-01

Evolutionary biologists typically represent clines as spatial gradients in a univariate character (or a principal-component axis) whose mean changes as a function of location along a transect spanning an environmental gradient or ecotone. This univariate approach may obscure the multivariate nature of phenotypic evolution across a landscape. Clines might instead be plotted as a series of vectors in multidimensional morphospace, connecting sequential geographic sites. We present a model showing that clines may trace nonlinear paths that arc through morphospace rather than elongating along a single major trajectory. Arcing clines arise because different characters diverge at different rates or locations along a geographic transect. We empirically confirm that some clines arc through morphospace, using morphological data from threespine stickleback sampled along eight independent transects from lakes down their respective outlet streams. In all eight clines, successive vectors of lake-stream divergence fluctuate in direction and magnitude in trait space, rather than pointing along a single phenotypic axis. Most clines exhibit surprisingly irregular directions of divergence as one moves downstream, although a few clines exhibit more directional arcs through morphospace. Our results highlight the multivariate complexity of clines that cannot be captured with the traditional graphical framework. We discuss hypotheses regarding the causes, and implications, of such arcing multivariate clines.

15. Attention bias modification training under working memory load increases the magnitude of change in attentional bias.

PubMed

Clarke, Patrick J F; Branson, Sonya; Chen, Nigel T M; Van Bockstaele, Bram; Salemink, Elske; MacLeod, Colin; Notebaert, Lies

2017-12-01

16. LPV decoupling for control of multivariable systems

2011-08-01

This article investigates methods for decoupling multivariable linear parameter varying (LPV) systems and proposes a new interaction measure for decoupled proportional-integral (PI) feedback control design in LPV systems. The proposed approach seeks to benefit the multivariable control of multi-input multi-output (MIMO) systems with variable operating conditions, variable parameters or nonlinear behaviour. This method can improve the tracking performance and reduce the operating conditions variability of such systems with significant coupling in the system dynamics. We design MIMO decoupling feedback LPV controllers to address loop interaction effects. The proposed method uses a parameter-dependent static inversion or SVD decomposition of the system to minimise the effects of the off-diagonal terms in the MIMO system transfer function matrix. A new parameter-dependent interaction measure is introduced based on the SVD decomposition and static inversion which is subsequently utilised for tuning multi-loop PI controller gains. Numerical examples are presented to illustrate the validity of the proposed LPV decoupling methods, as well as the use of the proposed interaction measures for a decoupled multi-loop PI control design.

17. Multichannel hierarchical image classification using multivariate copulas

Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane

2012-03-01

This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.

18. Estimation and correction of model bias in the NASA/GMAO GEOS5 data assimilation system: Sequential implementation

Zhang, Banglin; Tallapragada, Vijay; Weng, Fuzhong; Sippel, Jason; Ma, Zaizhong

2016-06-01

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background (OmB) residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis (OmA) are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

19. Multivariate Adaptive Regression Splines (Preprint)

DTIC Science & Technology

1990-08-01

situations, but as with the previous examples, the variance of the ratio (GCV/PSE) dominates this small bias. . 4.5. Portuguese Olive Oil . For this...example MARS is applied to data from analytical chemistry. The observations consist of 417 samples of olive oil from Portugal (Forina, et al., 1983). On...extent to which olive oil from northeastern Portugal (Dour0 Valley - 90 samples) differed from that of the rest of Portugal (327 samples). One way to

20. Introduction to Unconscious Bias

Schmelz, Joan T.

2010-05-01

We all have biases, and we are (for the most part) unaware of them. In general, men and women BOTH unconsciously devalue the contributions of women. This can have a detrimental effect on grant proposals, job applications, and performance reviews. Sociology is way ahead of astronomy in these studies. When evaluating identical application packages, male and female University psychology professors preferred 2:1 to hire "Brian” over "Karen” as an assistant professor. When evaluating a more experienced record (at the point of promotion to tenure), reservations were expressed four times more often when the name was female. This unconscious bias has a repeated negative effect on Karen's career. This talk will introduce the concept of unconscious bias and also give recommendations on how to address it using an example for a faculty search committee. The process of eliminating unconscious bias begins with awareness, then moves to policy and practice, and ends with accountability.

1. Estimating Bias Error Distributions

NASA Technical Reports Server (NTRS)

Liu, Tian-Shu; Finley, Tom D.

2001-01-01

This paper formulates the general methodology for estimating the bias error distribution of a device in a measuring domain from less accurate measurements when a minimal number of standard values (typically two values) are available. A new perspective is that the bias error distribution can be found as a solution of an intrinsic functional equation in a domain. Based on this theory, the scaling- and translation-based methods for determining the bias error distribution arc developed. These methods are virtually applicable to any device as long as the bias error distribution of the device can be sufficiently described by a power series (a polynomial) or a Fourier series in a domain. These methods have been validated through computational simulations and laboratory calibration experiments for a number of different devices.

2. Increasingly minimal bias routing

DOEpatents

Bataineh, Abdulla; Court, Thomas; Roweth, Duncan

2017-02-21

A system and algorithm configured to generate diversity at the traffic source so that packets are uniformly distributed over all of the available paths, but to increase the likelihood of taking a minimal path with each hop the packet takes. This is achieved by configuring routing biases so as to prefer non-minimal paths at the injection point, but increasingly prefer minimal paths as the packet proceeds, referred to herein as Increasing Minimal Bias (IMB).

3. The development of comparative bias index

2017-08-01

Structural Equation Modeling (SEM) is a second generation statistical analysis techniques developed for analyzing the inter-relationships among multiple variables in a model simultaneously. There are two most common used methods in SEM namely Covariance-Based Structural Equation Modeling (CB-SEM) and Partial Least Square Path Modeling (PLS-PM). There have been continuous debates among researchers in the use of PLS-PM over CB-SEM. While there is few studies were conducted to test the performance of CB-SEM and PLS-PM bias in estimating simulation data. This study intends to patch this problem by a) developing the Comparative Bias Index and b) testing the performance of CB-SEM and PLS-PM using developed index. Based on balanced experimental design, two multivariate normal simulation data with of distinct specifications of size 50, 100, 200 and 500 are generated and analyzed using CB-SEM and PLS-PM.

4. The interaction of perceptual biases in bistable perception

PubMed Central

Zhang, Xue; Xu, Qian; Jiang, Yi; Wang, Ying

2017-01-01

When viewing ambiguous stimuli, people tend to perceive some interpretations more frequently than others. Such perceptual biases impose various types of constraints on visual perception, and accordingly, have been assumed to serve distinct adaptive functions. Here we demonstrated the interaction of two functionally distinct biases in bistable biological motion perception, one regulating perception based on the statistics of the environment – the viewing-from-above (VFA) bias, and the other with the potential to reduce costly errors resulting from perceptual inference – the facing-the-viewer (FTV) bias. When compatible, the two biases reinforced each other to enhance the bias strength and induced less perceptual reversals relative to when they were in conflict. Whereas in the conflicting condition, the biases competed with each other, with the dominant percept varying with visual cues that modulate the two biases separately in opposite directions. Crucially, the way the two biases interact does not depend on the dominant bias at the individual level, and cannot be accounted for by a single bias alone. These findings provide compelling evidence that humans robustly integrate biases with different adaptive functions in visual perception. It may be evolutionarily advantageous to dynamically reweight diverse biases in the sensory context to resolve perceptual ambiguity. PMID:28165061

5. Topics in Multivariate Approximation Theory.

DTIC Science & Technology

1982-05-01

of the Bramble -Hilbert lemma (see Bramble & H𔃻hert (13ŕ). Kergin’s scheme raises some questions. In .ontrast £.t its univar- iate antecedent, it...J. R. Rice (19791# An adaptive algorithm for multivariate approximation giving optimal convergence rates, J.Approx. Theory 25, 337-359. J. H. Bramble ...J.Numer.Anal. 7, 112-124. J. H. Bramble & S. R. Hilbert (19711, BoUnds for a class of linear functionals with applications to Hermite interpolation

6. Multivariate residues and maximal unitarity

2013-12-01

We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.

7. Software For Multivariate Bayesian Classification

NASA Technical Reports Server (NTRS)

Saul, Ronald; Laird, Philip; Shelton, Robert

1996-01-01

PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.

8. Biases in Visual, Auditory, and Audiovisual Perception of Space

PubMed Central

Odegaard, Brian; Wozny, David R.; Shams, Ladan

2015-01-01

Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1) if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors), and (2) whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli). Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers) was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s) driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only improves the

9. Minimal inversion, command matching and disturbance decoupling in multivariable systems

NASA Technical Reports Server (NTRS)

Seraji, H.

1989-01-01

The present treatment of the related problems of minimal inversion and perfect output control in linear multivariable systems uses a simple analytical expression for the inverse of a square multivariate system's transfer-function matrix to construct a minimal-order inverse of the system. Because the poles of the minimal-order inverse are the transmission zeros of the system, necessary and sufficient conditions for the inverse system's stability are simply stated in terms of the zero polynomial of the original system. A necessary and sufficient condition for the existence of the required controllers is that the plant zero polynomial be neither identical to zero nor unstable.

10. Multivariate image analysis in biomedicine.

PubMed

Nattkemper, Tim W

2004-10-01

In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.

11. Weight bias among dietetics students: implications for treatment practices.

PubMed

Puhl, Rebecca; Wharton, Christopher; Heuer, Chelsea

2009-03-01

Several studies have examined attitudes about obesity among food and nutrition professionals, yielding mixed results, and no experimental research has tested the impact of dietitians' attitudes on their treatment practices or health evaluations with obese patients. This study investigated attitudes of dietetics students toward obese persons and tested whether a patient's body weight influences students' treatment decisions and health evaluations within a randomized experiment. Between the months of September and December 2007, a convenience sample of 182 dietetics undergraduate students (92% women; mean age 23.1+/-5.4 years) from colleges throughout the United States completed online self-report surveys to assess weight bias (using the Fat Phobia Scale). Participants were also randomly assigned to read one of four mock health profiles of patients who varied only by weight-related characteristics (eg, obese or average weight) and sex (male or female), and asked to make judgments about the patient's health status and participation in treatment. To compare group data, multiple analysis of variance was used to test for an effect of the patient's body mass index on participants' health evaluations and their perceptions of patients in each of the four experimental conditions. Correlations were calculated between mean fat phobia scores and perceptions of patients. Participants in all conditions expressed a moderate amount of fat phobia (mean=3.7), and students rated obese patients as being less likely to comply with treatment recommendations compared with nonobese patients (P<0.05). Results from multivariate analysis of variance tests showed students also evaluated obese patients' diet quality and health status to be poorer than nonobese patients, despite equivalent nutritional and health information across weight categories for each sex in patient profiles. In contrast, obese and nonobese patients were rated to be similarly motivated, receptive, and successful in

12. Interval Estimates of Multivariate Effect Sizes: Coverage and Interval Width Estimates under Variance Heterogeneity and Nonnormality

ERIC Educational Resources Information Center

Hess, Melinda R.; Hogarty, Kristine Y.; Ferron, John M.; Kromrey, Jeffrey D.

2007-01-01

Monte Carlo methods were used to examine techniques for constructing confidence intervals around multivariate effect sizes. Using interval inversion and bootstrapping methods, confidence intervals were constructed around the standard estimate of Mahalanobis distance (D[superscript 2]), two bias-adjusted estimates of D[superscript 2], and Huberty's…

13. A semiparametric multivariate and multisite weather generator

Apipattanavis, Somkiat; Podestá, Guillermo; Rajagopalan, Balaji; Katz, Richard W.

2007-11-01

We propose a semiparametric multivariate weather generator with greater ability to reproduce the historical statistics, especially the wet and dry spells. The proposed approach has two steps: (1) a Markov Chain for generating the precipitation state (i.e., no rain, rain, or heavy rain), and (2) a k-nearest neighbor (k-NN) bootstrap resampler for generating the multivariate weather variables. The Markov Chain captures the spell statistics while the k-NN bootstrap captures the distributional and lag-dependence statistics of the weather variables. Traditional k-NN generators tend to under-simulate the wet and dry spells that are keys to watershed and agricultural modeling for water planning and management; hence the motivation for this research. We demonstrate the utility of the proposed approach and its improvement over the traditional k-NN approach through an application to daily weather data from Pergamino in the Pampas region of Argentina. We show the applicability of the proposed framework in simulating weather scenarios conditional on the seasonal climate forecast and also at multiple sites in the Pampas region.

14. An integrated multivariable artificial pancreas control system.

PubMed

Turksoy, Kamuran; Quinn, Lauretta T; Littlejohn, Elizabeth; Cinar, Ali

2014-05-01

The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system.

15. Thinking in Black and White: Conscious thought increases racially biased judgments through biased face memory.

PubMed

Strick, Madelijn; Stoeckart, Peter F; Dijksterhuis, Ap

2015-11-01

It is a common research finding that conscious thought helps people to avoid racial discrimination. These three experiments, however, illustrate that conscious thought may increase biased face memory, which leads to increased judgment bias (i.e., preferring White to Black individuals). In Experiments 1 and 2, university students formed impressions of Black and White housemate candidates. They judged the candidates either immediately (immediate decision condition), thought about their judgments for a few minutes (conscious thought condition), or performed an unrelated task for a few minutes (unconscious thought condition). Conscious thinkers and immediate decision-makers showed a stronger face memory bias than unconscious thinkers, and this mediated increased judgment bias, although not all results were significant. Experiment 3 used a new, different paradigm and showed that a Black male was remembered as darker after a period of conscious thought than after a period of unconscious thought. Implications for racial prejudice are discussed.

16. Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

PubMed

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

17. Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression

PubMed Central

2015-01-01

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost. PMID:26574437

18. Residual bias in a multiphase flow model calibration and prediction

USGS Publications Warehouse

Poeter, E.P.; Johnson, R.H.

2002-01-01

When calibrated models produce biased residuals, we assume it is due to an inaccurate conceptual model and revise the model, choosing the most representative model as the one with the best-fit and least biased residuals. However, if the calibration data are biased, we may fail to identify an acceptable model or choose an incorrect model. Conceptual model revision could not eliminate biased residuals during inversion of simulated DNAPL migration under controlled conditions at the Borden Site near Ontario Canada. This paper delineates hypotheses for the source of bias, and explains the evolution of the calibration and resulting model predictions.

19. Galaxy bias and primordial non-Gaussianity

SciTech Connect

Assassi, Valentin; Baumann, Daniel; Schmidt, Fabian E-mail: D.D.Baumann@uva.nl

2015-12-01

We present a systematic study of galaxy biasing in the presence of primordial non-Gaussianity. For a large class of non-Gaussian initial conditions, we define a general bias expansion and prove that it is closed under renormalization, thereby showing that the basis of operators in the expansion is complete. We then study the effects of primordial non-Gaussianity on the statistics of galaxies. We show that the equivalence principle enforces a relation between the scale-dependent bias in the galaxy power spectrum and that in the dipolar part of the bispectrum. This provides a powerful consistency check to confirm the primordial origin of any observed scale-dependent bias. Finally, we also discuss the imprints of anisotropic non-Gaussianity as motivated by recent studies of higher-spin fields during inflation.

20. Quasi-conscious multivariate systems

Mason, Jonathan W. D.

2016-09-01

Conscious experience is awash with underlying relationships. Moreover, for various brain regions such as the visual cortex, the system is biased toward some states. Representing this bias using a probability distribution shows that the system can define expected quantities. The mathematical theory in the present paper links these facts by using expected float entropy (efe), which is a measure of the expected amount of information needed, to specify the state of the system, beyond what is already known about the system from relationships that appear as parameters. Under the requirement that the relationship parameters minimise efe, the brain defines relationships. It is proposed that when a brain state is interpreted in the context of these relationships the brain state acquires meaning in the form of the relational content of the associated experience. For a given set, the theory represents relationships using weighted relations which assign continuous weights, from 0 to 1, to the elements of the Cartesian product of that set. The relationship parameters include weighted relations on the nodes of the system and on their set of states. Examples obtained using Monte-Carlo methods (where relationship parameters are chosen uniformly at random) suggest that efe distributions with long left tails are most important.

1. Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

2. Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

3. Distinguishing Selection Bias and Confounding Bias in Comparative Effectiveness Research.

PubMed

Haneuse, Sebastien

2016-04-01

Comparative effectiveness research (CER) aims to provide patients and physicians with evidence-based guidance on treatment decisions. As researchers conduct CER they face myriad challenges. Although inadequate control of confounding is the most-often cited source of potential bias, selection bias that arises when patients are differentially excluded from analyses is a distinct phenomenon with distinct consequences: confounding bias compromises internal validity, whereas selection bias compromises external validity. Despite this distinction, however, the label "treatment-selection bias" is being used in the CER literature to denote the phenomenon of confounding bias. Motivated by an ongoing study of treatment choice for depression on weight change over time, this paper formally distinguishes selection and confounding bias in CER. By formally distinguishing selection and confounding bias, this paper clarifies important scientific, design, and analysis issues relevant to ensuring validity. First is that the 2 types of biases may arise simultaneously in any given study; even if confounding bias is completely controlled, a study may nevertheless suffer from selection bias so that the results are not generalizable to the patient population of interest. Second is that the statistical methods used to mitigate the 2 biases are themselves distinct; methods developed to control one type of bias should not be expected to address the other. Finally, the control of selection and confounding bias will often require distinct covariate information. Consequently, as researchers plan future studies of comparative effectiveness, care must be taken to ensure that all data elements relevant to both confounding and selection bias are collected.

4. Implicit bias, awareness and imperfect cognitions.

PubMed

Holroyd, Jules

2015-05-01

Are individuals responsible for behaviour that is implicitly biased? Implicitly biased actions are those which manifest the distorting influence of implicit associations. That they express these 'implicit' features of our cognitive and motivational make up has been appealed to in support of the claim that, because individuals lack the relevant awareness of their morally problematic discriminatory behaviour, they are not responsible for behaving in ways that manifest implicit bias. However, the claim that such influences are implicit is, in fact, not straightforwardly related to the claim that individuals lack awareness of the morally problematic dimensions of their behaviour. Nor is it clear that lack of awareness does absolve from responsibility. This may depend on whether individuals culpably fail to know something that they should know. I propose that an answer to this question, in turn, depends on whether other imperfect cognitions are implicated in any lack of the relevant kind of awareness. In this paper I clarify our understanding of 'implicitly biased actions' and then argue that there are three different dimensions of awareness that might be at issue in the claim that individuals lack awareness of implicit bias. Having identified the relevant sense of awareness I argue that only one of these senses is defensibly incorporated into a condition for responsibility, rejecting recent arguments from Washington & Kelly for an 'externalist' epistemic condition. Having identified what individuals should - and can - know about their implicitly biased actions, I turn to the question of whether failures to know this are culpable. This brings us to consider the role of implicit biases in relation to other imperfect cognitions. I conclude that responsibility for implicitly biased actions may depend on answers to further questions about their relationship to other imperfect cognitions.

5. Method of multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2004-01-06

A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

6. Optically biased laser gyro

SciTech Connect

Anderson, D.Z.; Chow, W.W.; Scully, M.O.; Sanders, V.E.

1980-10-01

We describe a four-mode ring laser that exhibits none of the mode-locking characteristics that plague laser gyros. This laser is characterized by a bias that changes sign with a change in the direction of rotation and prevents the counterpropagating modes from locking. A theoretical analysis explaining the experimental results is outlined.

7. A significant bias

2013-09-01

While I do not wish to belittle the unfortunate conclusions that may be drawn from your news article "Gender bias judges research by women more critically" (May p12), I do want to comment on the way the article is presented.

8. Sex Bias in Children.

ERIC Educational Resources Information Center

Zalk, Sue Rosenberg; And Others

This study investigated children's sex biased attitudes as a function of the sex, age, and race of the child as well as a geographical-SES factor. Two attitudes were measured on a 55-item questionnaire: Sex Pride (attributing positive characteristics to a child of the same sex) and Sex Prejudice (attributing negative characteristics to a child of…

9. Own Variety Bias

PubMed Central

García, Andrea Ariza

2015-01-01

In a language identification task, native Belgian French and native Swiss French speakers identified French from France as their own variety. However, Canadian French was not subject to this bias. Canadian and French listeners didn’t claim a different variety as their own. PMID:27648211

10. The Optimization of Multivariate Generalizability Studies with Budget Constraints.

ERIC Educational Resources Information Center

Marcoulides, George A.; Goldstein, Zvi

1992-01-01

A method is presented for determining the optimal number of conditions to use in multivariate-multifacet generalizability designs when resource constraints are imposed. A decision maker can determine the number of observations needed to obtain the largest possible generalizability coefficient. The procedure easily applies to the univariate case.…

11. Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

12. Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.

13. Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

14. Berkson’s bias, selection bias, and missing data

PubMed Central

Westreich, Daniel

2011-01-01

While Berkson’s bias is widely recognized in the epidemiologic literature, it remains underappreciated as a model of both selection bias and bias due to missing data. Simple causal diagrams and 2×2 tables illustrate how Berkson’s bias connects to collider bias and selection bias more generally, and show the strong analogies between Berksonian selection bias and bias due to missing data. In some situations, considerations of whether data are missing at random or missing not at random is less important than the causal structure of the missing-data process. While dealing with missing data always relies on strong assumptions about unobserved variables, the intuitions built with simple examples can provide a better understanding of approaches to missing data in real-world situations. PMID:22081062

15. A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

16. Temperature trend biases

Venema, Victor; Lindau, Ralf

2016-04-01

In an accompanying talk we show that well-homogenized national dataset warm more than temperatures from global collections averaged over the region of common coverage. In this poster we want to present auxiliary work about possible biases in the raw observations and on how well relative statistical homogenization can remove trend biases. There are several possible causes of cooling biases, which have not been studied much. Siting could be an important factor. Urban stations tend to move away from the centre to better locations. Many stations started inside of urban areas and are nowadays more outside. Even for villages the temperature difference between the centre and edge can be 0.5°C. When a city station moves to an airport, which often happened around WWII, this takes the station (largely) out of the urban heat island. During the 20th century the Stevenson screen was established as the dominant thermometer screen. This screen protected the thermometer much better against radiation than earlier designs. Deficits of earlier measurement methods have artificially warmed the temperatures in the 19th century. Newer studies suggest we may have underestimated the size of this bias. Currently we are in a transition to Automatic Weather Stations. The net global effect of this transition is not clear at this moment. Irrigation on average decreases the 2m-temperature by about 1 degree centigrade. At the same time, irrigation has increased significantly during the last century. People preferentially live in irrigated areas and weather stations serve agriculture. Thus it is possible that there is a higher likelihood that weather stations are erected in irrigated areas than elsewhere. In this case irrigation could lead to a spurious cooling trend. In the Parallel Observations Science Team of the International Surface Temperature Initiative (ISTI-POST) we are studying influence of the introduction of Stevenson screens and Automatic Weather Stations using parallel measurements

17. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique.

PubMed

Faes, Luca; Nollo, Giandomenico; Porta, Alberto

2011-05-01

We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected according to the Granger notion of predictability improvement, and is quantified in terms of information transfer. We apply the approach to simulations of deterministic and stochastic systems, showing its superiority over standard uniform embedding. Effects of quantization, data length, and noise contamination are investigated. As practical applications, we consider the assessment of cardiovascular regulatory mechanisms from the analysis of heart period, arterial pressure, and respiration time series, and the investigation of the information flow across brain areas from multichannel scalp electroencephalographic recordings.

18. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique

Faes, Luca; Nollo, Giandomenico; Porta, Alberto

2011-05-01

We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected according to the Granger notion of predictability improvement, and is quantified in terms of information transfer. We apply the approach to simulations of deterministic and stochastic systems, showing its superiority over standard uniform embedding. Effects of quantization, data length, and noise contamination are investigated. As practical applications, we consider the assessment of cardiovascular regulatory mechanisms from the analysis of heart period, arterial pressure, and respiration time series, and the investigation of the information flow across brain areas from multichannel scalp electroencephalographic recordings.

19. The Psychological Price of Media Bias

ERIC Educational Resources Information Center

2005-01-01

Media bias was investigated through the effects of a TV interviewer's preferential behavior on the image of the interviewee in the eyes of the viewers. Judges viewed a political interview with either a friendly or a hostile interviewer then rated their impressions of the interviewed politician, whose behavior was identical in all conditions. The…

20. The Psychological Price of Media Bias

ERIC Educational Resources Information Center

2005-01-01

Media bias was investigated through the effects of a TV interviewer's preferential behavior on the image of the interviewee in the eyes of the viewers. Judges viewed a political interview with either a friendly or a hostile interviewer then rated their impressions of the interviewed politician, whose behavior was identical in all conditions. The…

1. Sex Bias in Traditionally Male Occupational Programs.

ERIC Educational Resources Information Center

Bakshis, Robert; Godshalk, James

To evaluate potential sources of female sex bias and sex stereotyping within traditionally male occupational programs at the College of DuPage, programs with low female enrollment were selected for study: air conditioning and refrigeration, architectural drafting, auto service, building construction, criminal justice, electronics, fire science,…

2. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

3. Multivariate pluvial flood damage models

SciTech Connect

Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom

2015-09-15

Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.

4. Detrended fluctuation analysis of multivariate time series

Xiong, Hui; Shang, P.

2017-01-01

In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.

5. Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice.

6. Audibility and visual biasing in speech perception

Clement, Bart Richard

Although speech perception has been considered a predominantly auditory phenomenon, large benefits from vision in degraded acoustic conditions suggest integration of audition and vision. More direct evidence of this comes from studies of audiovisual disparity that demonstrate vision can bias and even dominate perception (McGurk & MacDonald, 1976). It has been observed that hearing-impaired listeners demonstrate more visual biasing than normally hearing listeners (Walden et al., 1990). It is argued here that stimulus audibility must be equated across groups before true differences can be established. In the present investigation, effects of visual biasing on perception were examined as audibility was degraded for 12 young normally hearing listeners. Biasing was determined by quantifying the degree to which listener identification functions for a single synthetic auditory /ba-da-ga/ continuum changed across two conditions: (1)an auditory-only listening condition; and (2)an auditory-visual condition in which every item of the continuum was synchronized with visual articulations of the consonant-vowel (CV) tokens /ba/ and /ga/, as spoken by each of two talkers. Audibility was altered by presenting the conditions in quiet and in noise at each of three signal-to- noise (S/N) ratios. For the visual-/ba/ context, large effects of audibility were found. As audibility decreased, visual biasing increased. A large talker effect also was found, with one talker eliciting more biasing than the other. An independent lipreading measure demonstrated that this talker was more visually intelligible than the other. For the visual-/ga/ context, audibility and talker effects were less robust, possibly obscured by strong listener effects, which were characterized by marked differences in perceptual processing patterns among participants. Some demonstrated substantial biasing whereas others demonstrated little, indicating a strong reliance on audition even in severely degraded acoustic

7. The distinct effects of internalizing weight bias: An experimental study.

PubMed

Pearl, Rebecca L; Puhl, Rebecca M

2016-06-01

Both experiencing and internalizing weight bias are associated with negative mental and physical health outcomes, but internalization may be a more potent predictor of these outcomes. The current study aimed to differentiate between causal effects of experiencing versus internalizing weight bias on emotional responses and psychological well-being. Adults with overweight/obesity (N=260) completed an online experiment in which they were randomly assigned to focus on either the experience or internalization of weight bias, and completed measures of affect, self-esteem, and body dissatisfaction. Results indicated that the Internalization condition led to more negative affect, less positive affect, and lower self-esteem than the Experience condition. The Internalization condition also led to heightened body dissatisfaction among men, but not women. These findings suggest that weight bias internalization may be a stronger predictor of poor mental and physical health than experiences alone, and carry implications for developing weight bias interventions.

8. Reduced susceptibility to confirmation bias in schizophrenia

PubMed Central

Doll, Bradley B.; Waltz, James A.; Cockburn, Jeffrey; Brown, Jaime K.; Frank, Michael J.; Gold, James M.

2014-01-01

9. Distinguishing selection bias and confounding bias in comparative effectiveness research

PubMed Central

Haneuse, Sebastien

2014-01-01

Comparative effectiveness research (CER) aims to provide patients and physicians with evidence-based guidance on treatment decisions. As researchers conduct CER they face myriad challenges. While inadequate control of confounding is the most-often cited source of potential bias, selection bias which arises when patients are differentially excluded from analyses is a distinct phenomenon with distinct consequences: confounding bias compromises internal validity while selection bias compromises external validity. Despite this distinction, however, the label “treatment-selection bias” is being used in the CER literature to denote the phenomenon of confounding bias. Motivated by an on-going study of treatment choice for depression on weight change over time, we formally distinguish confounding and selection bias in CER. By formally distinguishing selection and confounding bias we clarify important scientific, design and analysis issues relevant to ensuring validity. First is that the two types of bias may arise simultaneously in any given study; even if confounding bias is completely controlled, a study may nevertheless suffer from selection bias so that the results are not generalizable to the patient population of interest. Second is that statistical methods used to mitigate the two biases are themselves distinct; methods developed to control one type of bias should not be expected to address the other. Finally, the control of selection and confounding bias will often require distinct covariate information. Consequently, as researchers plan future studies of comparative effectiveness, care must be taken to ensure that all data elements relevant to both confounding and selection bias are collected. PMID:24309675

10. Detecting Bias in Selection for Higher Education: Three Different Methods

ERIC Educational Resources Information Center

Kennet-Cohen, Tamar; Turvall, Elliot; Oren, Carmel

2014-01-01

This study examined selection bias in Israeli university admissions with respect to test language and gender, using three approaches for the detection of such bias: Cleary's model of differential prediction, boundary conditions for differential prediction and difference between "d's" (the Constant Ratio Model). The university admissions…

11. Invalidity of True Experiments: Self-Report Pretest Biases.

ERIC Educational Resources Information Center

Aiken, Leona S.; West, Stephen G.

1990-01-01

The validity of true experiments is threatened by a class of self-report biases that affect all respondents at pretest, but which are diminished by treatment. Four of these inaccurate self-evaluation biases are discussed. Means of detection include external criteria, special conditions of measurement, and retrospective pretests. (TJH)

12. A retrieval-based approach to eliminating hindsight bias.

PubMed

Van Boekel, Martin; Varma, Keisha; Varma, Sashank

2017-03-01

Individuals exhibit hindsight bias when they are unable to recall their original responses to novel questions after correct answers are provided to them. Prior studies have eliminated hindsight bias by modifying the conditions under which original judgments or correct answers are encoded. Here, we explored whether hindsight bias can be eliminated by manipulating the conditions that hold at retrieval. Our retrieval-based approach predicts that if the conditions at retrieval enable sufficient discrimination of memory representations of original judgments from memory representations of correct answers, then hindsight bias will be reduced or eliminated. Experiment 1 used the standard memory design to replicate the hindsight bias effect in middle-school students. Experiments 2 and 3 modified the retrieval phase of this design, instructing participants beforehand that they would be recalling both their original judgments and the correct answers. As predicted, this enabled participants to form compound retrieval cues that discriminated original judgment traces from correct answer traces, and eliminated hindsight bias. Experiment 4 found that when participants were not instructed beforehand that they would be making both recalls, they did not form discriminating retrieval cues, and hindsight bias returned. These experiments delineate the retrieval conditions that produce-and fail to produce-hindsight bias.

13. Meta-Regression Approximations to Reduce Publication Selection Bias

ERIC Educational Resources Information Center

Stanley, T. D.; Doucouliagos, Hristos

2014-01-01

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…

14. Detecting Bias in Selection for Higher Education: Three Different Methods

ERIC Educational Resources Information Center

Kennet-Cohen, Tamar; Turvall, Elliot; Oren, Carmel

2014-01-01

This study examined selection bias in Israeli university admissions with respect to test language and gender, using three approaches for the detection of such bias: Cleary's model of differential prediction, boundary conditions for differential prediction and difference between "d's" (the Constant Ratio Model). The university admissions…

15. Self-Consciousness and Bias in Social Interaction.

ERIC Educational Resources Information Center

Sandelands, Lloyd E.; Stablein, Ralph E.

1986-01-01

Investigated whether trait differences in self-consciousness would account for egocentric attribution bias in social interaction. Bias was greater for high public self-consciousness. Public self-consciousness had no effect in the Interaction Unimportant Condition where social interaction was not salient. Contrary to prediction, however, the…

16. Meta-Regression Approximations to Reduce Publication Selection Bias

ERIC Educational Resources Information Center

Stanley, T. D.; Doucouliagos, Hristos

2014-01-01

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…

17. Sex-biased dispersal of human ancestors.

PubMed

Sugiyama, Yukimaru

2017-07-01

Some anthropologists and primatologists have argued that, judging by extant chimpanzees and humans, which are female-biased dispersers, the common ancestors of humans and chimpanzees were also female-biased dispersers. It has been thought that sex-biased dispersal patterns have been genetically transmitted for millions of years. However, this character has changed many times with changes in environment and life-form during human evolution and historical times. I examined life-form and social organization of nonhuman primates, among them gatherers (foragers), hunter-gatherers, agriculturalists, industrialists, and modern and extant humans. I conclude that dispersal patterns changed in response to environmental conditions during primate and human evolution. © 2017 Wiley Periodicals, Inc.

18. Recognition bias and the physical attractiveness stereotype.

PubMed

Rohner, Jean-Christophe; Rasmussen, Anders

2012-06-01

Previous studies have found a recognition bias for information consistent with the physical attractiveness stereotype (PAS), in which participants believe that they remember that attractive individuals have positive qualities and that unattractive individuals have negative qualities, regardless of what information actually occurred. The purpose of this research was to examine whether recognition bias for PAS congruent information is replicable and invariant across a variety of conditions (i.e. generalizable). The effects of nine different moderator variables were examined in two experiments. With a few exceptions, the effect of PAS congruence on recognition bias was independent of the moderator variables. The results suggest that the tendency to believe that one remembers information consistent with the physical attractiveness stereotype is a robust phenomenon. © 2012 The Authors. Scandinavian Journal of Psychology © 2012 The Scandinavian Psychological Associations.

19. Mardia's Multivariate Kurtosis with Missing Data

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T.

2004-01-01

Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…

20. Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

1. Bias in Markov models of disease.

PubMed

Faissol, Daniel M; Griffin, Paul M; Swann, Julie L

2009-08-01

We examine bias in Markov models of diseases, including both chronic and infectious diseases. We consider two common types of Markov disease models: ones where disease progression changes by severity of disease, and ones where progression of disease changes in time or by age. We find sufficient conditions for bias to exist in models with aggregated transition probabilities when compared to models with state/time dependent transition probabilities. We also find that when aggregating data to compute transition probabilities, bias increases with the degree of data aggregation. We illustrate by examining bias in Markov models of Hepatitis C, Alzheimer's disease, and lung cancer using medical data and find that the bias is significant depending on the method used to aggregate the data. A key implication is that by not incorporating state/time dependent transition probabilities, studies that use Markov models of diseases may be significantly overestimating or underestimating disease progression. This could potentially result in incorrect recommendations from cost-effectiveness studies and incorrect disease burden forecasts.

2. Hindsight and confirmation biases in an exercise in telepathy.

PubMed

Rudski, Jeffrey M

2002-12-01

Belief in the paranormal or claims of paranormal experiences may be, at least in part, associated with systematic cognitive biases. 48 undergraduate college students engaged in an exercise in telepathy in which the color of cards was 'sent' to them by the experimenter under two conditions. In a Hindsight-possible condition, participants recorded whether their choice was correct following the revelation of the color. In the Control condition participants committed to a particular response by writing it down before receiving feedback, thus eliminating ability to alter retrospectively what 'was known all along'. Consistent with a hindsight bias, participants performed significantly better under the Hindsight-possible condition. Moreover, a statisically significant correlation was found between paranormal belief assessed on Tobacyk's 1988 Revised Paranormal Belief Scale in the Hindsight-possible but not in the Control condition, suggesting a confirmation bias. Results are discussed in terms of interactions between hindsight and confirmation biases and how they might relate to paranormal beliefs.

3. Investigating bias in maximum-likelihood quantum-state tomography

Silva, G. B.; Glancy, S.; Vasconcelos, H. M.

2017-02-01

Maximum-likelihood quantum-state tomography yields estimators that are consistent, provided that the likelihood model is correct, but the maximum-likelihood estimators may have bias for any finite data set. The bias of an estimator is the difference between the expected value of the estimate and the true value of the parameter being estimated. This paper investigates bias in the widely used maximum-likelihood quantum-state tomography. Our goal is to understand how the amount of bias depends on factors such as the purity of the true state, the number of measurements performed, and the number of different bases in which the system is measured. For this, we perform numerical experiments that simulate optical homodyne tomography of squeezed thermal states under various conditions, perform tomography, and estimate bias in the purity of the estimated state. We find that estimates of higher purity states exhibit considerable bias, such that the estimates have lower purities than the true states.

4. Modifying threat-related interpretive bias in adolescents.

PubMed

Salemink, Elske; Wiers, Reinout W

2011-10-01

Socially anxious feelings sharply increase during adolescence and such feelings have been associated with interpretive biases. Studies in adults have shown that interpretive biases can be modified using Cognitive Bias Modification procedures (CBM-I) and subsequent effects on anxiety have been observed. The current study was designed to examine whether the CBM-I procedure has similar effects in adolescents. Unselected adolescents were randomly allocated to either a positive interpretation training (n = 88) or a placebo-control condition (n = 82). Results revealed that the training was successful in modifying interpretations and effects generalized to a new task. The interpretive bias effects were most pronounced in individuals with a threat-related interpretive bias at pre-test. No effects on state anxiety were observed. The current findings are promising with regard to applying bias modification procedures to adolescents, while further research is warranted regarding emotional effects.

5. "L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

6. Biasing GPCR signaling from inside.

PubMed

Shukla, Arun K

2014-01-28

The discovery of "functional selectivity" or "biased signaling" through G protein-coupled receptors (GPCRs) has redefined the classical GPCR signaling paradigm. Moreover, the therapeutic potential of biased signaling by and biased ligands for GPCRs is changing the landscape of GPCR drug discovery. The concept of biased signaling has primarily been developed and discussed in the context of ligands that bind to the extracellular regions of GPCRs. However, two recent reports demonstrate that it is also possible to bias GPCR signaling from inside the cell by targeting intracellular regions of these receptors. These findings present a novel handle for delineating the functional outcomes of biased signaling by GPCRs. Moreover, these approaches also uncover a previously unexplored framework for biasing GPCR signaling for drug discovery.

7. Evaluating solutions to sponsorship bias.

PubMed

Doucet, M; Sismondo, S

2008-08-01

8. Basics of Multivariate Analysis in Neuroimaging Data

PubMed Central

Habeck, Christian Georg

2010-01-01

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic

9. Two success-biased social learning strategies.

PubMed

Baldini, Ryan

2013-06-01

I compare the evolutionary dynamics of two success-biased social learning strategies, which, by definition, use the success of others to inform one's social learning decisions. The first, "Compare Means", causes a learner to adopt cultural variants with highest mean payoff in her sample. The second, "Imitate the Best", causes a learner to imitate the single most successful individual in her sample. I summarize conditions under which each strategy performs well or poorly, and investigate their evolution via a gene-culture coevolutionary model. Despite the adaptive appeal of these strategies, both encounter conditions under which they systematically perform worse than simply imitating at random. Compare Means performs worst when the optimal cultural variant is usually at high frequency, while Imitate the Best performs worst when suboptimal variants sometimes produce high payoffs. The extent to which it is optimal to use success-biased social learning depends strongly on the payoff distributions and environmental conditions that human social learners face.

10. Modeling and monitoring of a high pressure polymerization process using multivariate statistical techniques

Sharmin, Rumana

This thesis explores the use of multivariate statistical techniques in developing tools for property modeling and monitoring of a high pressure ethylene polymerization process. In polymer industry, many researchers have shown, mainly in simulation studies, the potential of multivariate statistical methods in identification and control of polymerization process. However, very few, if any, of these strategies have been implemented. This work was done using data collected from a commercial high pressure LDPE/EVA reactor located at AT Plastics, Edmonton. The models or methods developed in the course of this research have been validated with real data and in most cases, implemented in real time. One main objective of this PhD project was to develop and implement a data based inferential sensor to estimate the melt flow index of LDPE and EVA resins using regularly measured process variables. Steady state PLS method was used to develop the soft sensor model. A detailed description of the data preprocessing steps are given that should be followed in the analysis of industrial data. Models developed for two of the most frequently produced polymer grades at AT Plastics have been implemented. The models were tested for many sets of data and showed acceptable performance when applied with an online bias updating scheme. One observation from many validation exercises was that the model prediction becomes poorer with time as operators use new process conditions in the plant to produce the same resin with the same specification. During the implementation of the soft sensors, we suggested a simple bias update scheme as a remedy to this problem. An alternative and more rigorous approach is to recursively update the model with new data, which is also more suitable to handle grade transition. Two existing recursive PLS methods, one based on NIPALS algorithm and the other based on kernel algorithm were reviewed. In addition, we proposed a novel RPLS algorithm which is based on the

11. Harnessing Historical Climate Variability to Assess Multivariate Climate Changes

Mahony, C. R.; Cannon, A. J.; Aitken, S. N.

2015-12-01

Climate is intrinsically multivariate—the collective influence of various aspects of weather at different times of year. A central challenge of climate change impact analysis is therefore to characterize changes in multiple temperature and precipitation variables simultaneously. Historical climate variability provides key context for relating climate variables to each other and assessing collective deviations from historical climate conditions. We have developed a Mahalanobian probability metric to describe spatial and temporal climatic dissimilarity in terms of local interannual climatic variability. Our approach is particularly suited to evaluation of climate analogs in space and time, but also facilitates multivariate extensions to several prominent indices of climate change. We use this metric to detect the departure of multivariate climate conditions from the historical range of local variability across North America and to identify regions that are particularly susceptible to emergence of no-analog climates. With respect to interpreting climate extremes, some critical considerations emerge from this research. In particular, we highlight the potential for temporal aggregation to exaggerate the statistical significance of extreme conditions, and the dilemma of identifying an appropriate statistical distribution for precipitation across both space and time. Despite the challenges of interpreting the specific impacts associated with multivariate climate changes and extremes, expressing these conditions relative to historical climate variability provides a useful first approximation of their ecological and socioeconomic significance. Figure Caption: Demonstration of the use of the chi distribution to measure spatial climatic dissimilarity in terms of local interannual climatic variability.

12. Multivariate gene-set testing based on graphical models.

PubMed

Städler, Nicolas; Mukherjee, Sach

2015-01-01

The identification of predefined groups of genes ("gene-sets") which are differentially expressed between two conditions ("gene-set analysis", or GSA) is a very popular analysis in bioinformatics. GSA incorporates biological knowledge by aggregating over genes that are believed to be functionally related. This can enhance statistical power over analyses that consider only one gene at a time. However, currently available GSA approaches are based on univariate two-sample comparison of single genes. This means that they cannot test for multivariate hypotheses such as differences in covariance structure between the two conditions. Yet interplay between genes is a central aspect of biological investigation and it is likely that such interplay may differ between conditions. This paper proposes a novel approach for gene-set analysis that allows for truly multivariate hypotheses, in particular differences in gene-gene networks between conditions. Testing hypotheses concerning networks is challenging due the nature of the underlying estimation problem. Our starting point is a recent, general approach for high-dimensional two-sample testing. We refine the approach and show how it can be used to perform multivariate, network-based gene-set testing. We validate the approach in simulated examples and show results using high-throughput data from several studies in cancer biology. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

13. Multivariate Models of Adult Pacific Salmon Returns

PubMed Central

Burke, Brian J.; Peterson, William T.; Beckman, Brian R.; Morgan, Cheryl; Daly, Elizabeth A.; Litz, Marisa

2013-01-01

Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586

14. Multivariate models of adult Pacific salmon returns.

PubMed

Burke, Brian J; Peterson, William T; Beckman, Brian R; Morgan, Cheryl; Daly, Elizabeth A; Litz, Marisa

2013-01-01

Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon.

15. Attention bias modification produces no changes to appearance-related bias, state or trait body dissatisfaction in nonclinical women

PubMed Central

Loughnan, Siobhan A; Mulgrew, Kate E; Lane, Ben R

2015-01-01

The potential of attention bias modification to reduce appearance-related attentional biases and female body dissatisfaction has not been investigated. Immediate and short-term effects were therefore examined across attentional biases, state and trait body dissatisfaction in a randomised controlled trial consisting of 62 female participants aged 18–35 years. The results show no changes to attentional bias across either the experimental or control condition and no significant changes in body dissatisfaction immediately post-training or at 1–2 weeks follow-up. Single-session attention bias modification protocols may therefore not be sufficient in modifying appearance-based biases and associated disordered body schemas within a nonclinical sample. PMID:28070375

16. Attention bias modification produces no changes to appearance-related bias, state or trait body dissatisfaction in nonclinical women.

PubMed

Loughnan, Siobhan A; Mulgrew, Kate E; Lane, Ben R

2015-07-01

The potential of attention bias modification to reduce appearance-related attentional biases and female body dissatisfaction has not been investigated. Immediate and short-term effects were therefore examined across attentional biases, state and trait body dissatisfaction in a randomised controlled trial consisting of 62 female participants aged 18-35 years. The results show no changes to attentional bias across either the experimental or control condition and no significant changes in body dissatisfaction immediately post-training or at 1-2 weeks follow-up. Single-session attention bias modification protocols may therefore not be sufficient in modifying appearance-based biases and associated disordered body schemas within a nonclinical sample.

17. Auditory perception bias in speech imitation

PubMed Central

Postma-Nilsenová, Marie; Postma, Eric

2013-01-01

In an experimental study, we explored the role of auditory perception bias in vocal pitch imitation. Psychoacoustic tasks involving a missing fundamental indicate that some listeners are attuned to the relationship between all the higher harmonics present in the signal, which supports their perception of the fundamental frequency (the primary acoustic correlate of pitch). Other listeners focus on the lowest harmonic constituents of the complex sound signal which may hamper the perception of the fundamental. These two listener types are referred to as fundamental and spectral listeners, respectively. We hypothesized that the individual differences in speakers' capacity to imitate F0 found in earlier studies, may at least partly be due to the capacity to extract information about F0 from the speech signal. Participants' auditory perception bias was determined with a standard missing fundamental perceptual test. Subsequently, speech data were collected in a shadowing task with two conditions, one with a full speech signal and one with high-pass filtered speech above 300 Hz. The results showed that perception bias toward fundamental frequency was related to the degree of F0 imitation. The effect was stronger in the condition with high-pass filtered speech. The experimental outcomes suggest advantages for fundamental listeners in communicative situations where F0 imitation is used as a behavioral cue. Future research needs to determine to what extent auditory perception bias may be related to other individual properties known to improve imitation, such as phonetic talent. PMID:24204361

18. Auditory perception bias in speech imitation.

PubMed

Postma-Nilsenová, Marie; Postma, Eric

2013-01-01

In an experimental study, we explored the role of auditory perception bias in vocal pitch imitation. Psychoacoustic tasks involving a missing fundamental indicate that some listeners are attuned to the relationship between all the higher harmonics present in the signal, which supports their perception of the fundamental frequency (the primary acoustic correlate of pitch). Other listeners focus on the lowest harmonic constituents of the complex sound signal which may hamper the perception of the fundamental. These two listener types are referred to as fundamental and spectral listeners, respectively. We hypothesized that the individual differences in speakers' capacity to imitate F 0 found in earlier studies, may at least partly be due to the capacity to extract information about F 0 from the speech signal. Participants' auditory perception bias was determined with a standard missing fundamental perceptual test. Subsequently, speech data were collected in a shadowing task with two conditions, one with a full speech signal and one with high-pass filtered speech above 300 Hz. The results showed that perception bias toward fundamental frequency was related to the degree of F 0 imitation. The effect was stronger in the condition with high-pass filtered speech. The experimental outcomes suggest advantages for fundamental listeners in communicative situations where F 0 imitation is used as a behavioral cue. Future research needs to determine to what extent auditory perception bias may be related to other individual properties known to improve imitation, such as phonetic talent.

19. Self-biased converse magnetoelectric effect

Chul Yang, Su; Cho, Kyung-Hoon; Park, Chee-Sung; Priya, Shashank

2011-11-01

In this letter, we investigate the direct magnetoelectric (DME) and converse magnetoelectric (CME) effects in three-phase metal-ceramic laminate composites. Longitudinally poled and transversely magnetized (L-T) laminate was fabricated by bonding nickel plates between the two particulate magnetoelectric (ME) composite layers of composition 0.8 (0.948 K0.5Na0.5NbO3 - 0.052 LiSbO3) - 0.2 (Ni0.8Zn0.2Fe2O4) (KNNLS-NZF). Under off-resonance condition, the laminates exhibited hysteretic DME and CME responses as a function of applied bias field (Hbias). Self-biased effect characterized by non-zero ME response at zero Hbias was observed. The self-biased DME and CME properties were found to be enhanced under resonance conditions. Without external Hbias, magnetic induction switching was possible by applying AC voltage. These results provide the possibility of using self-biased CME effect in electrically controlled memory devices and magnetic flux control devices.

20. Multivariate normative comparisons using an aggregated database.

PubMed

Agelink van Rentergem, Joost A; Murre, Jaap M J; Huizenga, Hilde M

2017-01-01

In multivariate normative comparisons, a patient's profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R.

1. Multivariate normative comparisons using an aggregated database

PubMed Central

Murre, Jaap M. J.; Huizenga, Hilde M.

2017-01-01

In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

2. Editorial bias in scientific publications.

PubMed

Matías-Guiu, J; García-Ramos, R

2011-01-01

3. Sequential biases in accumulating evidence.

PubMed

Kulinskaya, Elena; Huggins, Richard; Dogo, Samson Henry

2016-09-01

Whilst it is common in clinical trials to use the results of tests at one phase to decide whether to continue to the next phase and to subsequently design the next phase, we show that this can lead to biased results in evidence synthesis. Two new kinds of bias associated with accumulating evidence, termed 'sequential decision bias' and 'sequential design bias', are identified. Both kinds of bias are the result of making decisions on the usefulness of a new study, or its design, based on the previous studies. Sequential decision bias is determined by the correlation between the value of the current estimated effect and the probability of conducting an additional study. Sequential design bias arises from using the estimated value instead of the clinically relevant value of an effect in sample size calculations. We considered both the fixed-effect and the random-effects models of meta-analysis and demonstrated analytically and by simulations that in both settings the problems due to sequential biases are apparent. According to our simulations, the sequential biases increase with increased heterogeneity. Minimisation of sequential biases arises as a new and important research area necessary for successful evidence-based approaches to the development of science. © 2015 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd. © 2015 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd.

4. A novel bias correction methodology for climate impact simulations

Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Thonicke, K.; Mahecha, M. D.

2016-02-01

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere-atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes.

5. Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

6. Multivariate data assimilation in an integrated hydrological modelling system

Madsen, Henrik; Zhang, Donghua; Ridler, Marc; Refsgaard, Jens Christian; Høgh Jensen, Karsten

2016-04-01

The immensely increasing availability of in-situ and remotely sensed hydrological data has offered new opportunities for monitoring and forecasting water resources by combining observation data with hydrological modelling. Efficient multivariate data assimilation in integrated groundwater - surface water hydrological modelling systems are required to fully utilize and optimally combine the different types of observation data. A particular challenge is the assimilation of observation data of different hydrological variables from different monitoring instruments, representing a wide range of spatial and temporal scales and different levels of uncertainty. A multivariate data assimilation framework has been implemented in the MIKE SHE integrated hydrological modelling system by linking the MIKE SHE code with a generic data assimilation library. The data assimilation library supports different state-of-the-art ensemble-based Kalman filter methods, and includes procedures for localisation, joint state, parameter and model error estimation, and bias-aware filtering. Furthermore, it supports use of different stochastic error models to describe model and measurement errors. Results are presented that demonstrate the use of the data assimilation framework for assimilation of different data types in a catchment-scale MIKE SHE model.

7. Mark-specific hazard ratio model with missing multivariate marks.

PubMed

2016-10-01

An objective of randomized placebo-controlled preventive HIV vaccine efficacy (VE) trials is to assess the relationship between vaccine effects to prevent HIV acquisition and continuous genetic distances of the exposing HIVs to multiple HIV strains represented in the vaccine. The set of genetic distances, only observed in failures, is collectively termed the 'mark.' The objective has motivated a recent study of a multivariate mark-specific hazard ratio model in the competing risks failure time analysis framework. Marks of interest, however, are commonly subject to substantial missingness, largely due to rapid post-acquisition viral evolution. In this article, we investigate the mark-specific hazard ratio model with missing multivariate marks and develop two inferential procedures based on (i) inverse probability weighting (IPW) of the complete cases, and (ii) augmentation of the IPW estimating functions by leveraging auxiliary data predictive of the mark. Asymptotic properties and finite-sample performance of the inferential procedures are presented. This research also provides general inferential methods for semiparametric density ratio/biased sampling models with missing data. We apply the developed procedures to data from the HVTN 502 'Step' HIV VE trial.

8. A High-Dimensional Nonparametric Multivariate Test for Mean Vector.

PubMed

Wang, Lan; Peng, Bo; Li, Runze

This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T(2) test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This paper proposes a novel high-dimensional nonparametric test for the population mean vector for a general class of multivariate distributions. With the aid of new tools in modern probability theory, we proved that the limiting null distribution of the proposed test is normal under mild conditions when p is substantially larger than n. We further study the local power of the proposed test and compare its relative efficiency with a modified Hotelling T(2) test for high-dimensional data. An interesting finding is that the newly proposed test can have even more substantial power gain with large p than the traditional nonparametric multivariate test does with finite fixed p. We study the finite sample performance of the proposed test via Monte Carlo simulations. We further illustrate its application by an empirical analysis of a genomics data set.

9. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2016-01-01

Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189

10. A High-Dimensional Nonparametric Multivariate Test for Mean Vector

PubMed Central

Wang, Lan; Peng, Bo; Li, Runze

2015-01-01

This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T2 test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This paper proposes a novel high-dimensional nonparametric test for the population mean vector for a general class of multivariate distributions. With the aid of new tools in modern probability theory, we proved that the limiting null distribution of the proposed test is normal under mild conditions when p is substantially larger than n. We further study the local power of the proposed test and compare its relative efficiency with a modified Hotelling T2 test for high-dimensional data. An interesting finding is that the newly proposed test can have even more substantial power gain with large p than the traditional nonparametric multivariate test does with finite fixed p. We study the finite sample performance of the proposed test via Monte Carlo simulations. We further illustrate its application by an empirical analysis of a genomics data set. PMID:26848205

11. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data.

PubMed

Liu, Zitao; Hauskrecht, Milos

2016-02-01

Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy.

12. The intentionality bias and schizotypy.

PubMed

Moore, J W; Pope, A

2014-01-01

The "intentionality bias" refers to our automatic tendency to judge other people's actions to be intentional. In this experiment we extended research on this effect in two key ways. First, we developed a novel nonlinguistic task for assessing the intentionality bias. This task used video stimuli of ambiguous movements. Second, we investigated the relationship between the strength of this bias and schizotypy (schizophrenia-like symptoms in healthy individuals). Our results showed that the intentionality bias was replicated for the video stimuli and also that this bias is stronger in those individuals scoring higher on the schizotypy rating scales. Overall these findings lend further support for the existence of the intentionality bias. We also discuss the possible relevance of these findings for our understanding of certain symptoms of schizophrenic illness.

13. Quantum Communication and Quantum Multivariate Polynomial Interpolation

Diep, Do Ngoc; Giang, Do Hoang

2017-09-01

The paper is devoted to the problem of multivariate polynomial interpolation and its application to quantum secret sharing. We show that using quantum Fourier transform one can produce the protocol for quantum secret sharing distribution.

14. Multivariate Voronoi Outlier Detection for Time Series.

PubMed

Zwilling, Chris E; Wang, Michelle Yongmei

2014-10-01

Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

15. Multivariate Statistical Mapping of Spectroscopic Imaging Data

PubMed Central

Young, K.; Govind, V.; Sharma, K.; Studholme, C.; Maudsley, A.A; Schuff, N.

2010-01-01

For magnetic resonance spectroscopic imaging (MRSI) studies of the brain it is important to measure the distribution of metabolites in a regionally unbiased way - that is without restrictions to apriori defined regions of interest (ROI). Since MRSI provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of MRSI for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues but it has not been previously employed for SI studies of brain. The aims of this study were to: 1) develop and validate multivariate voxel based statistical mapping for MRSI and 2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons. PMID:19953514

16. Size Bias in Galaxy Surveys

Schmidt, Fabian; Rozo, Eduardo; Dodelson, Scott; Hui, Lam; Sheldon, Erin

2009-07-01

Only certain galaxies are included in surveys: those bright and large enough to be detectable as extended sources. Because gravitational lensing can make galaxies appear both brighter and larger, the presence of foreground inhomogeneities can scatter galaxies across not only magnitude cuts but also size cuts, changing the statistical properties of the resulting catalog. Here we explore this size bias and how it combines with magnification bias to affect galaxy statistics. We demonstrate that photometric galaxy samples from current and upcoming surveys can be even more affected by size bias than by magnification bias.

17. Summary of relationships between exchangeability, biasing paths and bias.

PubMed

Flanders, William Dana; Eldridge, Ronald Curtis

2015-10-01

Definitions and conceptualizations of confounding and selection bias have evolved over the past several decades. An important advance occurred with development of the concept of exchangeability. For example, if exchangeability holds, risks of disease in an unexposed group can be compared with risks in an exposed group to estimate causal effects. Another advance occurred with the use of causal graphs to summarize causal relationships and facilitate identification of causal patterns that likely indicate bias, including confounding and selection bias. While closely related, exchangeability is defined in the counterfactual-model framework and confounding paths in the causal-graph framework. Moreover, the precise relationships between these concepts have not been fully described. Here, we summarize definitions and current views of these concepts. We show how bias, exchangeability and biasing paths interrelate and provide justification for key results. For example, we show that absence of a biasing path implies exchangeability but that the reverse implication need not hold without an additional assumption, such as faithfulness. The close links shown are expected. However confounding, selection bias and exchangeability are basic concepts, so comprehensive summarization and definitive demonstration of links between them is important. Thus, this work facilitates and adds to our understanding of these important biases.

18. Understanding bias in provenance studies

Garzanti, Eduardo; Andò, Sergio; Malusà, Marco; Vezzoli, Giovanni

2010-05-01

Innumerable pieces of information are stored in the sedimentary archive. Each single sediment layer contains billions of detrital grains, and every grain preserves imprints of its geological story. If we learn to read, compare, and combine these messages properly, through a deeper understanding of physical and chemical processes that modify sediment composition during the sedimentary cycle, provenance analysis may eventually enable us to reconstruct more accurately the geological processes that shaped the Earth's crust in the past. Interpreting detrital modes is not straightforward because provenance signals issued from source rocks become progressively blurred by multiple noises in the sedimentary environment ("environmental bias"; Komar, 2007), and finally during post-depositional history ("diagenetic bias"; Morton and Hallsworth, 2007). During transport and deposition, detrital minerals are segregated in different size fractions and environments according to their size, density and shape (Rubey, 1933; Garzanti et al., 2008). Heavy-mineral concentration can increase by an order of magnitude due to selective-entrainment effects, with potentially overwhelming impact on chemical composition and provenance estimates based on detrital-geochronology data (Garzanti et al., 2009). Conversely, heavy-mineral concentration is typically reduced by an order of magnitude in Alpine and Himalayan foreland-basin deposits older than the Pleistocene (Garzanti and Andò, 2007). Extensive chemical dissolution can occur even prior to deposition during weathering in hot humid climates (Velbel, 2007). Primary provenance signals can be isolated and assessed by studying first modern sediments in hyperarid settings (i.e., free from diagenetic and weathering bias). Next, weathering, hydraulic-sorting, and diagenetic effects can be singled out by analysing sediments of similar provenance produced in contrasting climatic conditions, sediments transported in diverse modes and deposited in

19. Coupling GIS and multivariate approaches to reference site selection for wadeable stream monitoring.

PubMed

Collier, Kevin J; Haigh, Andy; Kelly, Johlene

2007-04-01

Geographic Information System (GIS) was used to identify potential reference sites for wadeable stream monitoring, and multivariate analyses were applied to test whether invertebrate communities reflected a priori spatial and stream type classifications. We identified potential reference sites in segments with unmodified vegetation cover adjacent to the stream and in >85% of the upstream catchment. We then used various landcover, amenity and environmental impact databases to eliminate sites that had potential anthropogenic influences upstream and that fell into a range of access classes. Each site identified by this process was coded by four dominant stream classes and seven zones, and 119 candidate sites were randomly selected for follow-up assessment. This process yielded 16 sites conforming to reference site criteria using a conditional-probabilistic design, and these were augmented by an additional 14 existing or special interest reference sites. Non-metric multidimensional scaling (NMS) analysis of percent abundance invertebrate data indicated significant differences in community composition among some of the zones and stream classes identified a priori providing qualified support for this framework in reference site selection. NMS analysis of a range standardised condition and diversity metrics derived from the invertebrate data indicated a core set of 26 closely related sites, and four outliers that were considered atypical of reference site conditions and subsequently dropped from the network. Use of GIS linked to stream typology, available spatial databases and aerial photography greatly enhanced the objectivity and efficiency of reference site selection. The multi-metric ordination approach reduced variability among stream types and bias associated with non-random site selection, and provided an effective way to identify representative reference sites.

20. Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

1. Statistical biases in stellar astronomy: the Malmquist bias revisited

Butkevich, A. G.; Berdyugin, A. V.; Teerikorpi, P.

2005-09-01

A unified analytical treatment of the Malmquist bias is presented. Depending on the data under consideration and on the way the mean values of absolute magnitude are calculated, three different types of Malmquist bias appear: integral bias, and magnitude- and distance-dependent ones. An analytical consideration of the distance-dependent bias, previously studied in extragalactic astronomy, is given in terms of the trigonometric parallax. In a quantitative treatment of the Spaenhauer diagram, in which the derived absolute magnitude is plotted versus the true parallax, we developed an easy-to-use method for determination of the region unaffected by the bias (`unbiased plateau'). Considering spectroscopic distance indicators, we point out that for any magnitude-limited sample spectroscopic distance and parallax approach constant limits as the true distance increases. We draw some examples from luminosity calibrations of stellar classes. A brief outline is also given of a tentative course of investigations, anticipating future space astrometry missions.

2. Diagnostic biases in translational bioinformatics.

PubMed

Han, Henry

2015-08-01

With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine. In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases. In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results. Our studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a

3. Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data

4. Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data

5. Assessing Projection Bias in Consumers’ Food Preferences

PubMed Central

de-Magistris, Tiziana; Gracia, Azucena

2016-01-01

The aim of this study is to test whether projection bias exists in consumers’ purchasing decisions for food products. To achieve our aim, we used a non-hypothetical experiment (i.e., experimental auction), where hungry and non-hungry participants were incentivized to reveal their willingness to pay (WTP). The results confirm the existence of projection bias when consumers made their decisions on food products. In particular, projection bias existed because currently hungry participants were willing to pay a higher price premium for cheeses than satiated ones, both in hungry and satiated future states. Moreover, participants overvalued the food product more when they were delivered in the future hungry condition than in the satiated one. Our study provides clear, quantitative and meaningful evidence of projection bias because our findings are based on economic valuation of food preferences. Indeed, the strength of this study is that findings are expressed in terms of willingness to pay which is an interpretable amount of money. PMID:26828930

6. Cognitive Bias Modification (CBM) of obsessive compulsive beliefs.

PubMed

Williams, Alishia D; Grisham, Jessica R

2013-10-09

Cognitive bias modification (CBM) protocols have been developed to help establish the causal role of biased cognitive processing in maintaining psychopathology and have demonstrated therapeutic benefits in a range of disorders. The current study evaluated a cognitive bias modification training paradigm designed to target interpretation biases (CBM-I) associated with obsessive compulsive disorder (OCD). We evaluated the impact of CBM-I on measures of interpretation bias, distress, and on responses to three OC stressor tasks designed to tap the core belief domains of Importance of Thoughts/Control, Perfectionism/Intolerance of Uncertainty, and Contamination/Estimation of Threat in a selected sample of community members reporting obsessive compulsive (OC) symptoms (N = 89). Participants randomly assigned to the Positive condition evidenced a change in interpretation bias towards more positive and less negative OC-relevant interpretations following CBM-I compared to participants assigned to the Control condition. Importantly, a positivity bias was not observed for foil scenarios unrelated to the core OC belief domains. Further, participants in the Positive condition reported less distress and urge to neutralize following an OC stressor task designed to tap Importance of Thoughts/Control. No significant difference emerged on the indices of behavioural response to the OC stressor tasks. Severity of OC symptoms did not moderate the effects of positive CBM-I training. CBM-I appears effective in selectively targeting OC beliefs. Results need to be replicated in clinical samples in order for potential therapeutic benefit to be demonstrated.

7. Cognitive Bias Modification (CBM) of obsessive compulsive beliefs

PubMed Central

2013-01-01

Background Cognitive bias modification (CBM) protocols have been developed to help establish the causal role of biased cognitive processing in maintaining psychopathology and have demonstrated therapeutic benefits in a range of disorders. The current study evaluated a cognitive bias modification training paradigm designed to target interpretation biases (CBM-I) associated with obsessive compulsive disorder (OCD). Methods We evaluated the impact of CBM-I on measures of interpretation bias, distress, and on responses to three OC stressor tasks designed to tap the core belief domains of Importance of Thoughts/Control, Perfectionism/Intolerance of Uncertainty, and Contamination/Estimation of Threat in a selected sample of community members reporting obsessive compulsive (OC) symptoms (N = 89). Results Participants randomly assigned to the Positive condition evidenced a change in interpretation bias towards more positive and less negative OC-relevant interpretations following CBM-I compared to participants assigned to the Control condition. Importantly, a positivity bias was not observed for foil scenarios unrelated to the core OC belief domains. Further, participants in the Positive condition reported less distress and urge to neutralize following an OC stressor task designed to tap Importance of Thoughts/Control. No significant difference emerged on the indices of behavioural response to the OC stressor tasks. Severity of OC symptoms did not moderate the effects of positive CBM-I training. Conclusions CBM-I appears effective in selectively targeting OC beliefs. Results need to be replicated in clinical samples in order for potential therapeutic benefit to be demonstrated. PMID:24106918

8. Gender bias affects forests worldwide

Treesearch

Marlène Elias; Susan S Hummel; Bimbika S Basnett; Carol J.P. Colfer

2017-01-01

Gender biases persist in forestry research and practice. These biases result in reduced scientific rigor and inequitable, ineffective, and less efficient policies, programs, and interventions. Drawing from a two-volume collection of current and classic analyses on gender in forests, we outline five persistent and inter-related themes: gendered governance, tree tenure,...

9. Sequential biases in accumulating evidence

PubMed Central

Huggins, Richard; Dogo, Samson Henry

2015-01-01

Whilst it is common in clinical trials to use the results of tests at one phase to decide whether to continue to the next phase and to subsequently design the next phase, we show that this can lead to biased results in evidence synthesis. Two new kinds of bias associated with accumulating evidence, termed ‘sequential decision bias’ and ‘sequential design bias’, are identified. Both kinds of bias are the result of making decisions on the usefulness of a new study, or its design, based on the previous studies. Sequential decision bias is determined by the correlation between the value of the current estimated effect and the probability of conducting an additional study. Sequential design bias arises from using the estimated value instead of the clinically relevant value of an effect in sample size calculations. We considered both the fixed‐effect and the random‐effects models of meta‐analysis and demonstrated analytically and by simulations that in both settings the problems due to sequential biases are apparent. According to our simulations, the sequential biases increase with increased heterogeneity. Minimisation of sequential biases arises as a new and important research area necessary for successful evidence‐based approaches to the development of science. © 2015 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd. PMID:26626562

10. A multivariate time-series approach to marital interaction

PubMed Central

Kupfer, Jörg; Brosig, Burkhard; Brähler, Elmar

2005-01-01

Time-series analysis (TSA) is frequently used in order to clarify complex structures of mutually interacting panel data. The method helps in understanding how the course of a dependent variable is predicted by independent time-series with no time lag, as well as by previous observations of that dependent variable (autocorrelation) and of independent variables (cross-correlation). The study analyzes the marital interaction of a married couple under clinical conditions over a period of 144 days by means of TSA. The data were collected within a course of couple therapy. The male partner was affected by a severe condition of atopic dermatitis and the woman suffered from bulimia nervosa. Each of the partners completed a mood questionnaire and a body symptom checklist. After the determination of auto- and cross-correlations between and within the parallel data sets, multivariate time-series models were specified. Mutual and individual patterns of emotional reactions explained 14% (skin) and 33% (bulimia) of the total variance in both dependent variables (adj. R², p<0.0001 for the multivariate models). The question was discussed whether multivariate TSA-models represent a suitable approach to the empirical exploration of clinical marital interaction. PMID:19742066

11. A multivariate time-series approach to marital interaction.

PubMed

Kupfer, Jörg; Brosig, Burkhard; Brähler, Elmar

2005-08-02

Time-series analysis (TSA) is frequently used in order to clarify complex structures of mutually interacting panel data. The method helps in understanding how the course of a dependent variable is predicted by independent time-series with no time lag, as well as by previous observations of that dependent variable (autocorrelation) and of independent variables (cross-correlation).The study analyzes the marital interaction of a married couple under clinical conditions over a period of 144 days by means of TSA. The data were collected within a course of couple therapy. The male partner was affected by a severe condition of atopic dermatitis and the woman suffered from bulimia nervosa.Each of the partners completed a mood questionnaire and a body symptom checklist. After the determination of auto- and cross-correlations between and within the parallel data sets, multivariate time-series models were specified. Mutual and individual patterns of emotional reactions explained 14% (skin) and 33% (bulimia) of the total variance in both dependent variables (adj. R(2), p<0.0001 for the multivariate models).The question was discussed whether multivariate TSA-models represent a suitable approach to the empirical exploration of clinical marital interaction.

12. Classifying sex biased congenital anomalies

SciTech Connect

Lubinsky, M.S.

1997-03-31

The reasons for sex biases in congenital anomalies that arise before structural or hormonal dimorphisms are established has long been unclear. A review of such disorders shows that patterning and tissue anomalies are female biased, and structural findings are more common in males. This suggests different gender dependent susceptibilities to developmental disturbances, with female vulnerabilities focused on early blastogenesis/determination, while males are more likely to involve later organogenesis/morphogenesis. A dual origin for some anomalies explains paradoxical reductions of sex biases with greater severity (i.e., multiple rather than single malformations), presumably as more severe events increase the involvement of an otherwise minor process with opposite biases to those of the primary mechanism. The cause for these sex differences is unknown, but early dimorphisms, such as differences in growth or presence of H-Y antigen, may be responsible. This model provides a useful rationale for understanding and classifying sex-biased congenital anomalies. 42 refs., 7 tabs.

13. Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

PubMed Central

Montzka, Carsten; Pauwels, Valentijn R. N.; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-01-01

simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required. PMID:23443380

14. Multivariate and multiscale data assimilation in terrestrial systems: a review.

PubMed

Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-11-26

simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

15. Stochastic bias in multidimensional excursion set approaches

Castorina, Emanuele; Sheth, Ravi K.

2013-08-01

We describe a simple fully analytic model of the excursion set approach associated with two Gaussian random walks: the first walk represents the initial overdensity around a protohalo, and the second is a crude way of allowing for other factors which might influence halo formation. This model is richer than that based on a single walk, because it yields a distribution of heights at first crossing. We provide explicit expressions for the unconditional first crossing distribution which is usually used to model the halo mass function, the progenitor distributions from which merger rates are usually estimated and the conditional distributions from which correlations with environment are usually estimated. These latter exhibit perhaps the simplest form of what is often called non-local bias, and which we prefer to call stochastic bias, since the new bias effects arise from `hidden variables' other than density, but these may still be defined locally. We provide explicit expressions for these new bias factors. We also provide formulae for the distribution of heights at first crossing in the unconditional and conditional cases. In contrast to the first crossing distribution, these are exact, even for moving barriers, and for walks with correlated steps. The conditional distributions yield predictions for the distribution of halo concentrations at fixed mass and formation redshift. They also exhibit assembly bias like effects, even when the steps in the walks themselves are uncorrelated. Our formulae show that without prior knowledge of the physical origin of the second walk, the naive estimate of the critical density required for halo formation which is based on the statistics of the first crossing distribution will be larger than that based on the statistical distribution of walk heights at first crossing; both will be biased low compared to the value associated with the physics. Finally, we show how the predictions are modified if we add the requirement that haloes form

16. Enhancing scientific reasoning by refining students' models of multivariable causality

Keselman, Alla

Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained

17. Perceptual Other-Race Training Reduces Implicit Racial Bias

PubMed Central

Lebrecht, Sophie; Pierce, Lara J.; Tarr, Michael J.; Tanaka, James W.

2009-01-01

Background Implicit racial bias denotes socio-cognitive attitudes towards other-race groups that are exempt from conscious awareness. In parallel, other-race faces are more difficult to differentiate relative to own-race faces – the “Other-Race Effect.” To examine the relationship between these two biases, we trained Caucasian subjects to better individuate other-race faces and measured implicit racial bias for those faces both before and after training. Methodology/Principal Findings Two groups of Caucasian subjects were exposed equally to the same African American faces in a training protocol run over 5 sessions. In the individuation condition, subjects learned to discriminate between African American faces. In the categorization condition, subjects learned to categorize faces as African American or not. For both conditions, both pre- and post-training we measured the Other-Race Effect using old-new recognition and implicit racial biases using a novel implicit social measure – the “Affective Lexical Priming Score” (ALPS). Subjects in the individuation condition, but not in the categorization condition, showed improved discrimination of African American faces with training. Concomitantly, subjects in the individuation condition, but not the categorization condition, showed a reduction in their ALPS. Critically, for the individuation condition only, the degree to which an individual subject's ALPS decreased was significantly correlated with the degree of improvement that subject showed in their ability to differentiate African American faces. Conclusions/Significance Our results establish a causal link between the Other-Race Effect and implicit racial bias. We demonstrate that training that ameliorates the perceptual Other-Race Effect also reduces socio-cognitive implicit racial bias. These findings suggest that implicit racial biases are multifaceted, and include malleable perceptual skills that can be modified with relatively little training. PMID

18. Recursive bias estimation for high dimensional regression smoothers

SciTech Connect

Hengartner, Nicolas W; Cornillon, Pierre - Andre; Matzner - Lober, Eric

2009-01-01

In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to biased smoother. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting smoother has a small variance but a substantial bias. In this paper, we propose to iteratively correct of the bias initial estimator by an estimate of the latter obtained by smoothing the residuals. We examine in details the convergence of the iterated procedure for classical smoothers and relate our procedure to L{sub 2}-Boosting, For multivariate thin plate spline smoother, we proved that our procedure adapts to the correct and unknown order of smoothness for estimating an unknown function m belonging to H({nu}) (Sobolev space where m should be bigger than d/2). We apply our method to simulated and real data and show that our method compares favorably with existing procedures.

19. Snow multivariable data assimilation for hydrological predictions in mountain areas

Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

2016-04-01

The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground

20. A Ku-band laboratory experiment on the electromagnetic bias

SciTech Connect

Branger, H.; Ramamonjiarisoa, A.; Bliven, L.F.

1993-11-01

Sea-surface electromagnetic bias (EM bias), the difference between the mean reflecting surface and the geometric mean sea level, must be accurately determined to realize the full potential of satellite altimeters. A uniformly valid algorithm relating the normalized (or nondimensional) EM bias, i.e., ``bias/significant wave height,`` to physical variables has not yet been established, so the authors conducted laboratory experiments to guide model development. Dimensional relations seldom yield robust algorithms and in fact, although rather high correlation is found between normalized EM bias and either wind speed or wave height, the laboratory coefficients are considerably greater than those of in situ algorithms. Nondimensional parameterization is more useful for deriving scaling laws, and when the normalized EM bias is displayed as a function of wave height skewness or wave age, laboratory and field data converge into consistent trends. In particular, normalized bias decreases with wave age, but unfortunately, even the wave age model does not account for the effects of mechanically generated waves, which produce appreciable scatter relative to the pure wind cases. Thus, they propose a two-parameter model using (1) a nondimensional wave height, which is computed for local winds, and (2) a significant slope, which is computed for nonlocally generated waves. Analysis of the laboratory data shows that the normalized EM bias for mixed conditions is well modeled as a product of these two parameters.

1. Direction specific biases in human visual and vestibular heading perception.

PubMed

Crane, Benjamin T

2012-01-01

2. Schmidt decomposition and multivariate statistical analysis

Bogdanov, Yu. I.; Bogdanova, N. A.; Fastovets, D. V.; Luckichev, V. F.

2016-12-01

The new method of multivariate data analysis based on the complements of classical probability distribution to quantum state and Schmidt decomposition is presented. We considered Schmidt formalism application to problems of statistical correlation analysis. Correlation of photons in the beam splitter output channels, when input photons statistics is given by compound Poisson distribution is examined. The developed formalism allows us to analyze multidimensional systems and we have obtained analytical formulas for Schmidt decomposition of multivariate Gaussian states. It is shown that mathematical tools of quantum mechanics can significantly improve the classical statistical analysis. The presented formalism is the natural approach for the analysis of both classical and quantum multivariate systems and can be applied in various tasks associated with research of dependences.

3. A multivariable control scheme for robot manipulators

NASA Technical Reports Server (NTRS)

Tarokh, M.; Seraji, H.

1991-01-01

The article puts forward a simple scheme for multivariable control of robot manipulators to achieve trajectory tracking. The scheme is composed of an inner loop stabilizing controller and an outer loop tracking controller. The inner loop utilizes a multivariable PD controller to stabilize the robot by placing the poles of the linearized robot model at some desired locations. The outer loop employs a multivariable PID controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated on-line to cope with variations in the robot model during gross motion and for payload change. Alternatively, the use of high gain controllers for gross motion and payload change is discussed. Computer simulation results are given for illustration.

4. Multivariate analysis: A statistical approach for computations

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

5. Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

6. Cognitive Bias in Systems Verification

NASA Technical Reports Server (NTRS)

Larson, Steve

2012-01-01

Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

7. Bias in occupational epidemiology studies

PubMed Central

Pearce, Neil; Checkoway, Harvey; Kriebel, David

2007-01-01

The design of occupational epidemiology studies should be based on the need to minimise random and systematic error. The latter is the focus of this paper, and includes selection bias, information bias and confounding. Selection bias can be minimised by obtaining a high response rate (and by appropriate selection of the control group in a case‐control study). In general, it is important to ensure that information bias is minimised and is also non‐differential (for example, that the misclassification of exposure is not related to disease status) by collecting data in a standardised manner. A major concern in occupational epidemiology studies usually relates to confounding, because exposure has not been randomly allocated, and the groups under study may therefore have different baseline disease risks. For each of these types of bias, the goal should be to avoid the bias by appropriate study design and/or appropriate control in the analysis. However, it is also important to attempt to assess the likely direction and strength of biases that cannot be avoided or controlled. PMID:17053019

8. Multivariate Statistical Modelling of Drought and Heat Wave Events

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A

9. Do horses with poor welfare show `pessimistic' cognitive biases?

Henry, S.; Fureix, C.; Rowberry, R.; Bateson, M.; Hausberger, M.

2017-02-01

This field study tested the hypothesis that domestic horses living under putatively challenging-to-welfare conditions (for example involving social, spatial, feeding constraints) would present signs of poor welfare and co-occurring pessimistic judgement biases. Our subjects were 34 horses who had been housed for over 3 years in either restricted riding school situations ( e.g. kept in single boxes, with limited roughage, ridden by inexperienced riders; N = 25) or under more naturalistic conditions ( e.g. access to free-range, kept in stable social groups, leisure riding; N = 9). The horses' welfare was assessed by recording health-related, behavioural and postural indicators. Additionally, after learning a location task to discriminate a bucket containing either edible food (`positive' location) or unpalatable food (`negative' location), the horses were presented with a bucket located near the positive position, near the negative position and halfway between the positive and negative positions to assess their judgement biases. The riding school horses displayed the highest levels of behavioural and health-related problems and a pessimistic judgment bias, whereas the horses living under more naturalistic conditions displayed indications of good welfare and an optimistic bias. Moreover, pessimistic bias data strongly correlated with poor welfare data. This suggests that a lowered mood impacts a non-human species' perception of its environment and highlights cognitive biases as an appropriate tool to assess the impact of chronic living conditions on horse welfare.

10. Do horses with poor welfare show 'pessimistic' cognitive biases?

PubMed

Henry, S; Fureix, C; Rowberry, R; Bateson, M; Hausberger, M

2017-02-01

This field study tested the hypothesis that domestic horses living under putatively challenging-to-welfare conditions (for example involving social, spatial, feeding constraints) would present signs of poor welfare and co-occurring pessimistic judgement biases. Our subjects were 34 horses who had been housed for over 3 years in either restricted riding school situations (e.g. kept in single boxes, with limited roughage, ridden by inexperienced riders; N = 25) or under more naturalistic conditions (e.g. access to free-range, kept in stable social groups, leisure riding; N = 9). The horses' welfare was assessed by recording health-related, behavioural and postural indicators. Additionally, after learning a location task to discriminate a bucket containing either edible food ('positive' location) or unpalatable food ('negative' location), the horses were presented with a bucket located near the positive position, near the negative position and halfway between the positive and negative positions to assess their judgement biases. The riding school horses displayed the highest levels of behavioural and health-related problems and a pessimistic judgment bias, whereas the horses living under more naturalistic conditions displayed indications of good welfare and an optimistic bias. Moreover, pessimistic bias data strongly correlated with poor welfare data. This suggests that a lowered mood impacts a non-human species' perception of its environment and highlights cognitive biases as an appropriate tool to assess the impact of chronic living conditions on horse welfare.

11. Hindsight bias around the world.

PubMed

Pohl, Rüdiger F; Bender, Michael; Lachmann, Gregor

2002-01-01

Hindsight bias refers to the tendency to overestimate in hindsight what one has known in foresight. Recently, two experiments extended the research to include samples from different cultures (Choi & Nisbett, 2000; Heine & Lehman, 1996). Asking their participants what they would have guessed before they knew the outcome ("hypothetical design"), Choi and Nisbett (2000) found that Koreans, in comparison to North Americans, exhibited more hindsight bias. Heine and Lehman (1996), however, reported that Japanese people in comparison to Canadians showed marginally less hindsight bias. In a second study, in which participants were asked to recall what they had estimated before they knew the outcome ("memory design"), the latter authors found no difference in hindsight bias between Japanese people and Canadians. We extended these studies with 225 Internet participants, in a hypothetical design, from four different continents (Asia, Australia, Europe, and North America). Hindsight bias was large and similar for all samples except for German and Dutch participants who showed no hindsight bias at all. While the latter effect may be based on peculiarities of the material and of the participants, the former underscores the worldwide stability of the phenomenon. In addition a follow-up surprise rating (paper and pencil) in China (35 participants) and Germany (20 participants) revealed that only less surprising items led to hindsight bias while more surprising ones did not. We suggest that the basic cognitive processes leading to hindsight bias are by-products of the evolutionary-evolved capacity of adaptive learning. On top of these basic processes, individual meta-cognitions (e.g., elicited by surprise) or motives (e.g., a self-serving motive) may further moderate the amount of bias, thus explaining the diverging results of Choi and Nisbett (2000), Heine and Lehman (1996), and our own study.

12. Automated Confocal Microscope Bias Correction

Dorval, Thierry; Genovesio, Auguste

2006-10-01

Illumination artifacts systematically occur in 2D cross-section confocal microscopy imaging . These bias can strongly corrupt an higher level image processing such as a segmentation, a fluorescence evaluation or even a pattern extraction/recognition. This paper presents a new fully automated bias correction methodology based on large image database preprocessing. This method is very appropriate to the High Content Screening (HCS), method dedicated to drugs discovery. Our method assumes that the amount of pictures available is large enough to allow a reliable statistical computation of an average bias image. A relevant segmentation evaluation protocol and experimental results validate our correction algorithm by outperforming object extraction on non corrupted images.

13. On precision and accuracy (bias) statements for measurement procedures

SciTech Connect

Bruckner, L.A.; Hume, M.W.; Delvin, W.L.

1988-01-01

Measurement procedures are often required to contain precision and accuracy of precision and bias statements. This paper contains a glossary that explains various terms that often appear in these statements as well as an example illustrating such statements for a specific set of data. Precision and bias statements are shown to vary according to the conditions under which the data were collected. This paper emphasizes that the error model (an algebraic expression that describes how the various sources of variation affect the measurement) is an important consideration in the formation of precision and bias statements.

14. Attention Bias Modification for Major Depressive Disorder: Effects on Attention Bias, Resting State Connectivity, and Symptom Change

PubMed Central

Beevers, Christopher G.; Clasen, Peter C.; Enock, Philip M.; Schnyer, David M.

2015-01-01

Cognitive theories of depression posit that selective attention for negative information contributes to the maintenance of depression. The current study experimentally tested this idea by randomly assigning adults with Major Depressive Disorder (MDD) to four weeks of computer-based attention bias modification designed to reduce negative attention bias or four weeks of placebo attention training. Findings indicate that compared to placebo training, attention bias modification reduced negative attention bias and increased resting-state connectivity within a neural circuit (i.e., middle frontal gyrus and dorsal anterior cingulate cortex) that supports control over emotional information. Further, pre- to post-training change in negative attention bias was significantly correlated with depression symptom change only in the active training condition. Exploratory analyses indicated that pre- to post-training changes in resting state connectivity within a circuit associated with sustained attention to visual information (i.e., precuenus and middle frontal gyrus) contributed to symptom improvement in the placebo condition. Importantly, depression symptoms did not change differentially between the training groups—overall, a 40% decrease in symptoms was observed across attention training conditions. Findings suggest that negative attention bias is associated with the maintenance of depression; however, general attentional control may also maintain depression symptoms, as evidenced by resting state connectivity and depression symptom improvement in the placebo training condition. PMID:25894440

15. Attention bias modification for major depressive disorder: Effects on attention bias, resting state connectivity, and symptom change.

PubMed

Beevers, Christopher G; Clasen, Peter C; Enock, Philip M; Schnyer, David M

2015-08-01

Cognitive theories of depression posit that selective attention for negative information contributes to the maintenance of depression. The current study experimentally tested this idea by randomly assigning adults with Major Depressive Disorder (MDD) to 4 weeks of computer-based attention bias modification designed to reduce negative attention bias or 4 weeks of placebo attention training. Findings indicate that compared to placebo training, attention bias modification reduced negative attention bias and increased resting-state connectivity within a neural circuit (i.e., middle frontal gyrus and dorsal anterior cingulate cortex) that supports control over emotional information. Further, pre- to post-training change in negative attention bias was significantly correlated with depression symptom change only in the active training condition. Exploratory analyses indicated that pre- to post-training changes in resting state connectivity within a circuit associated with sustained attention to visual information (i.e., precuenus and middle frontal gyrus) contributed to symptom improvement in the placebo condition. Importantly, depression symptoms did not change differentially between the training groups-overall, a 40% decrease in symptoms was observed across attention training conditions. Findings suggest that negative attention bias is associated with the maintenance of depression; however, deficits in general attentional control may also maintain depression symptoms, as evidenced by resting state connectivity and depression symptom improvement in the placebo training condition.

16. Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery∗

PubMed Central

Liu, Han; Wang, Lie; Zhao, Tuo

2016-01-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/ϵ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/. PMID:28316509

17. Magnetic bearings with zero bias

NASA Technical Reports Server (NTRS)

Brown, Gerald V.; Grodsinsky, Carlos M.

1991-01-01

A magnetic bearing operating without a bias field has supported a shaft rotating at speeds up to 12,000 rpm with the usual four power supplies and with only two. A magnetic bearing is commonly operated with a bias current equal to half of the maximum current allowable in its coils. This linearizes the relation between net force and control current and improves the force slewing rate and hence the band width. The steady bias current dissipates power, even when no force is required from the bearing. The power wasted is equal to two-thirds of the power at maximum force output. Examined here is the zero bias idea. The advantages and disadvantages are noted.

18. Implicit and Explicit Weight Bias in a National Sample of 4732 Medical Students: The Medical Student CHANGES Study

PubMed Central

Phelan, Sean M.; Dovidio, John F.; Puhl, Rebecca M.; Burgess, Diana J.; Nelson, David B.; Yeazel, Mark W.; Hardeman, Rachel; Perry, Sylvia; van Ryn, Michelle

2014-01-01

Objective To examine the magnitude of explicit and implicit weight biases compared to biases against other groups; and identify student factors predicting bias in a large national sample of medical students. Design and Methods A web-based survey was completed by 4732 1st year medical students from 49 medical schools as part of a longitudinal study of medical education. The survey included a validated measure of implicit weight bias, the implicit association test, and 2 measures of explicit bias: a feeling thermometer and the anti-fat attitudes test. Results A majority of students exhibited implicit (74%) and explicit (67%) weight bias. Implicit weight bias scores were comparable to reported bias against racial minorities. Explicit attitudes were more negative toward obese people than toward racial minorities, gays, lesbians, and poor people. In multivariate regression models, implicit and explicit weight bias was predicted by lower BMI, male sex, and non-Black race. Either implicit or explicit bias was also predicted by age, SES, country of birth, and specialty choice. Conclusions Implicit and explicit weight bias is common among 1st year medical students, and varies across student factors. Future research should assess implications of biases and test interventions to reduce their impact. PMID:24375989

19. Implicit and explicit weight bias in a national sample of 4,732 medical students: the medical student CHANGES study.

PubMed

Phelan, Sean M; Dovidio, John F; Puhl, Rebecca M; Burgess, Diana J; Nelson, David B; Yeazel, Mark W; Hardeman, Rachel; Perry, Sylvia; van Ryn, Michelle

2014-04-01

To examine the magnitude of explicit and implicit weight biases compared to biases against other groups; and identify student factors predicting bias in a large national sample of medical students. A web-based survey was completed by 4,732 1st year medical students from 49 medical schools as part of a longitudinal study of medical education. The survey included a validated measure of implicit weight bias, the implicit association test, and 2 measures of explicit bias: a feeling thermometer and the anti-fat attitudes test. A majority of students exhibited implicit (74%) and explicit (67%) weight bias. Implicit weight bias scores were comparable to reported bias against racial minorities. Explicit attitudes were more negative toward obese people than toward racial minorities, gays, lesbians, and poor people. In multivariate regression models, implicit and explicit weight bias was predicted by lower BMI, male sex, and non-Black race. Either implicit or explicit bias was also predicted by age, SES, country of birth, and specialty choice. Implicit and explicit weight bias is common among 1st year medical students, and varies across student factors. Future research should assess implications of biases and test interventions to reduce their impact. Copyright © 2013 The Obesity Society.

20. Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

1. Multivariate And Phylogenetic Analyses Of Galaxies

Fraix-Burnet, Didier; Chattopadhyay, Tanuka; D'Onofrio, Mauro; Marziani, Paula; Mondal, Saptarshi

2017-06-01

Investigating the formation and evolution of galaxies is becoming a complicated process with the increased availability of huge databases as a result of instrumental improvements. In this poster we present preliminary results on two statistical studies using multivariate partitioning and cladistic analyses to find homogeneous groups and their evolutionary relationships.

2. DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

3. DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

4. Using Matlab in a Multivariable Calculus Course.

ERIC Educational Resources Information Center

Schlatter, Mark D.

The benefits of high-level mathematics packages such as Matlab include both a computer algebra system and the ability to provide students with concrete visual examples. This paper discusses how both capabilities of Matlab were used in a multivariate calculus class. Graphical user interfaces which display three-dimensional surfaces, contour plots,…

5. Some Recent Advances in Multivariate Polynomial Interpolation

Carnicer, J. M.; Gasca, M.

2007-09-01

Multivariate polynomial interpolation has received much attention in the last part of the 20th century. In this talk we comment on some recent advances in the last decade, with special emphasis in distributions of points which give rise to unisolvent (or poised) problems in the space of polynomials of a given total degree and simple interpolation formulae.

6. Multivariate polynomial interpolation under projectivities part I

Mühlbach, G.; Gasca, M.

1991-10-01

In this note interpolation by real polynomials of several real variables is treated. Existence and unicity of the interpolant for knot systems being the perspective images of certain regular knot systems is discussed. Moreover, for such systems a Newton interpolation formula is derived allowing a recursive computation of the interpolant via multivariate divided differences. A numerical example is given.

7. Multivariate polynomial interpolation under projectivities III

Mühlbach, G.; Gasca, M.

1994-03-01

This is the third part of a note on multivariate interpolation. Some remainder formulas for interpolation on knot sets that are perspective images of standard lower data sets are given. They apply to all knot systems considered in parts I and II.

8. On the history of multivariate polynomial interpolation

Gasca, Mariano; Sauer, Thomas

2000-10-01

Multivariate polynomial interpolation is a basic and fundamental subject in Approximation Theory and Numerical Analysis, which has received and continues receiving not deep but constant attention. In this short survey, we review its development in the first 75 years of this century, including a pioneering paper by Kronecker in the 19th century.

9. Multivariable Control System Design for a Submarine,

DTIC Science & Technology

1984-05-01

Open Loop Singular Values for the 5 and 1S Knot Linear Modelo *~~* b % % V’ , * % ~ .%~ C 9 ~ V. --.- V. V.-.--.--46..- S. 77’ Model S20R5 20- 10- -0...Control, Addison-Wesley, 1976, pp 65-86. 14. Kevin Boettcher, Analysis of Multivariable Control Systems with Structured Uncertainty, Area Examination

10. Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

11. Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd.

12. Comparison of the single channel and multichannel (multivariate) concepts of selectivity in analytical chemistry.

PubMed

Dorkó, Zsanett; Verbić, Tatjana; Horvai, George

2015-07-01

Different measures of selectivity are in use for single channel and multichannel linear analytical measurements, respectively. It is important to understand that these two measures express related but still distinctly different features of the respective measurements. These relationships are clarified by introducing new arguments. The most widely used selectivity measure of multichannel linear methods (which is based on the net analyte signal, NAS, concept) expresses the sensitivity to random errors of a determination where all bias from interferents is computationally eliminated using pure component spectra. The conventional selectivity measure of single channel linear measurements, on the other hand, helps to estimate the bias caused by an interferent in a biased measurement. In single channel methods expert knowledge about the samples is used to limit the possible range of interferent concentrations. The same kind of expert knowledge allows improved (lower mean squared error, MSE) analyte determinations also in "classical" multichannel measurements if those are intractable due to perfect collinearity or to high noise inflation. To achieve this goal bias variance tradeoff is employed, hence there remains some bias in the results and therefore the concept of single channel selectivity can be extended in a natural way to multichannel measurements. This extended definition and the resulting selectivity measure can also be applied to the so-called inverse multivariate methods like partial least squares regression (PLSR), principal component regression (PCR) and ridge regression (RR).

13. Approach bias modification in inpatient psychiatric smokers.

PubMed

Machulska, Alla; Zlomuzica, Armin; Rinck, Mike; Assion, Hans-Jörg; Margraf, Jürgen

2016-05-01

Drug-related automatic approach tendencies contribute to the development and maintenance of addictive behavior. The present study investigated whether a nicotine-related approach bias can be modified in smokers undergoing inpatient psychiatric treatment by using a novel training variant of the nicotine Approach-Avoidance-Task (AAT). Additionally, we assessed whether the AAT-training would affect smoking behavior. Inpatient smokers were randomly assigned to either an AAT-training or a sham-training condition. In the AAT-training condition, smokers were indirectly instructed to make avoidance movements in response to nicotine-related pictures and to make approach movements in response to tooth-cleaning pictures. In the sham-training condition, no contingency between picture content und arm movements existed. Trainings were administered in four sessions, accompanied by a brief smoking-cessation intervention. Smoking-related self-report measures and automatic approach biases toward smoking cues were measured before and after training. Three months after training, daily nicotine consumption was obtained. A total of 205 participants were recruited, and data from 139 participants were considered in the final analysis. Prior to the trainings, smokers in both conditions exhibited a stronger approach bias for nicotine-related pictures than for tooth-cleaning pictures. After both trainings, this difference was no longer evident. Although reduced smoking behavior at posttest was observed after both trainings, only the AAT-training led to a larger reduction of nicotine consumption at a three-month follow-up. Our preliminary data partially support the conclusion that the AAT might be a feasible tool to reduce smoking in the long-term in psychiatric patients, albeit its effect on other smoking-related measures remains to be explored. Copyright © 2015 Elsevier Ltd. All rights reserved.

14. Expectancy bias in anxious samples

PubMed Central

Cabeleira, Cindy M.; Steinman, Shari A.; Burgess, Melissa M.; Bucks, Romola S.; MacLeod, Colin; Melo, Wilson; Teachman, Bethany A.

2014-01-01

While it is well documented that anxious individuals have negative expectations about the future, it is unclear what cognitive processes give rise to this expectancy bias. Two studies are reported that use the Expectancy Task, which is designed to assess expectancy bias and illuminate its basis. This task presents individuals with valenced scenarios (Positive Valence, Negative Valence, or Conflicting Valence), and then evaluates their tendency to expect subsequent future positive relative to negative events. The Expectancy Task was used with low and high trait anxious (Study 1: N = 32) and anxiety sensitive (Study 2: N = 138) individuals. Results suggest that in the context of physical concerns, both high anxious samples display a less positive expectancy bias. In the context of social concerns, high trait anxious individuals display a negative expectancy bias only when negatively valenced information was previously presented. Overall, this suggests that anxious individuals display a less positive expectancy bias, and that the processes that give rise to this bias may vary by type of situation (e.g., social or physical) or anxiety difficulty. PMID:24798678

15. Influence of SST biases on future climate change projections

SciTech Connect

Ashfaq, Moetasim; Skinner, Chris B; Cherkauer, Keith

2010-01-01

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977 1999 in the historical period and 2077 2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean atmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.

16. Reducing bias in survival under non-random temporary emigration

USGS Publications Warehouse

Peñaloza, Claudia L.; Kendall, William L.; Langtimm, Catherine Ann

2014-01-01

Despite intensive monitoring, temporary emigration from the sampling area can induce bias severe enough for managers to discard life-history parameter estimates toward the terminus of the times series (terminal bias). Under random temporary emigration unbiased parameters can be estimated with CJS models. However, unmodeled Markovian temporary emigration causes bias in parameter estimates and an unobservable state is required to model this type of emigration. The robust design is most flexible when modeling temporary emigration, and partial solutions to mitigate bias have been identified, nonetheless there are conditions were terminal bias prevails. Long-lived species with high adult survival and highly variable non-random temporary emigration present terminal bias in survival estimates, despite being modeled with the robust design and suggested constraints. Because this bias is due to uncertainty about the fate of individuals that are undetected toward the end of the time series, solutions should involve using additional information on survival status or location of these individuals at that time. Using simulation, we evaluated the performance of models that jointly analyze robust design data and an additional source of ancillary data (predictive covariate on temporary emigration, telemetry, dead recovery, or auxiliary resightings) in reducing terminal bias in survival estimates. The auxiliary resighting and predictive covariate models reduced terminal bias the most. Additional telemetry data was effective at reducing terminal bias only when individuals were tracked for a minimum of two years. High adult survival of long-lived species made the joint model with recovery data ineffective at reducing terminal bias because of small-sample bias. The naïve constraint model (last and penultimate temporary emigration parameters made equal), was the least efficient, though still able to reduce terminal bias when compared to an unconstrained model. Joint analysis of several

17. New dominance characteristics for the multivariable Nyquist array method

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1979-01-01

Three new dominance characteristics are introduced to the multivariable Nyquist array methods. The first characteristic utilizes a conformal mapping procedure to establish a new set of bands in the 'image' plane to assist in feedback gain selection and stability assessment. The second characteristic uses the 'image' band concept to provide a theoretical foundation for finite frequency dominance considerations, thus removing the restrictive requirement of dominance for all s on the Nyquist D contour. The third characteristic provides for the sharing of system dominance among the feedback control loops. The dominance sharing concept may be used to establish dominance and/or to improve the dominance condition in prespecified feedback loops.

18. Multivariable control systems with saturating actuators antireset windup strategies

NASA Technical Reports Server (NTRS)

Kapasouris, P.; Athans, M.

1985-01-01

Preliminary, promising, results for introducing antireset windup (ARW) properties in multivariable feedback control systems with multiple saturating actuator nonlinearities and integrating actions are presented. The ARW method introduces simple nonlinear feedback around the integrators. The multiloop circle criterion is used to derive sufficient conditions for closed-loop stability that employ frequency-domain singular value tests. The improvement in transient response due to the ARW feedback is demonstrated using a 2-input 2-outpurt control system based upon F-404 jet engine dynamics.

19. Quantifying Multivariate Classification Performance - the Problem of Overfitting

SciTech Connect

Stallard, Brian R.; Taylor, John G.

1999-08-09

We have been studying the use of spectral imagery to locate targets in spectrally interfering backgrounds. In making performance estimates for various sensors it has become evident that some calculations are unreliable because of overflying. Hence, we began a thorough study of the problem of overfitting in multivariate classification. In this paper we present some model based results describing the problem. From the model we know the ideal covariance matrix, the ideal discriminant vector, and the ideal classification performance. We then investigate how experimental conditions such as noise, number of bands, and number of samples cause discrepancies from the ideal results. We also suggest ways to discover and alleviate overfitting.

20. Cognitive Bias Modification for Interpretation in Major Depression: Effects on Memory and Stress Reactivity

PubMed Central

Joormann, Jutta; Waugh, Christian E.; Gotlib, Ian H.

2014-01-01

Interpreting ambiguous stimuli in a negative manner is a core bias associated with depression. Investigators have used cognitive bias modification for interpretation (CBM-I) to demonstrate that it is possible to experimentally induce and modify these biases. This study extends previous research by examining whether CBM-I affects not only interpretation, but also memory and physiological stress response in individuals diagnosed with Major Depressive Disorder (MDD). We found that CBM-I was effective in inducing an interpretive bias. Participants also exhibited memory biases that corresponded to their training condition and demonstrated differential physiological responding in a stress task. These results suggest that interpretation biases in depression can be modified, and that this training can lead to corresponding changes in memory and to decreases in stress reactivity. Findings from this study highlight the importance of examining the relations among different cognitive biases in MDD and the possibility of modifying cognitive biases. PMID:25593790

1. Cognitive Bias Modification for Interpretation in Major Depression: Effects on Memory and Stress Reactivity.

PubMed

Joormann, Jutta; Waugh, Christian E; Gotlib, Ian H

2015-01-01

Interpreting ambiguous stimuli in a negative manner is a core bias associated with depression. Investigators have used cognitive bias modification for interpretation (CBM-I) to demonstrate that it is possible to experimentally induce and modify these biases. This study extends previous research by examining whether CBM-I affects not only interpretation, but also memory and physiological stress response in individuals diagnosed with Major Depressive Disorder (MDD). We found that CBM-I was effective in inducing an interpretive bias. Participants also exhibited memory biases that corresponded to their training condition and demonstrated differential physiological responding in a stress task. These results suggest that interpretation biases in depression can be modified, and that this training can lead to corresponding changes in memory and to decreases in stress reactivity. Findings from this study highlight the importance of examining the relations among different cognitive biases in MDD and the possibility of modifying cognitive biases.

2. Thermospheric density model biases at sunspot maximum

Pardini, Carmen; Moe, Kenneth; Anselmo, Luciano

A previous study (Pardini C., Anselmo L, Moe K., Moe M.M., Drag and energy accommodation coefficients during sunspot maximum, Adv. Space Res., 2009, doi:10.1016/j.asr.2009.08.034), including ten satellites with altitudes between 200 and 630 km, has yielded values for the energy accommodation coefficient as well as for the physical drag coefficient as a function of height during solar maximum conditions. The results are consistent with the altitude and solar cycle variation of atomic oxygen, which is known to be adsorbed on satellite surfaces, affecting both the energy accommodation and angular distribution of the reemitted molecules. Taking advantage of these results, an investigation of thermospheric density model biases at sunspot maximum became possible using the recently upgraded CDFIT software code. Specif-ically developed at ISTI/CNR, CDFIT is used to fit the observed satellite semi-major axis decay. All the relevant orbital perturbations are considered and several atmospheric density models have been implemented over the years, including JR-71, MSISE-90, NRLMSISE-00, GOST2004 and JB2006. For this analysis we reused the satellites Cosmos 2265 and Cosmos 2332 (altitude: 275 km), SNOE (altitude: 480 km), and Clementine (altitude: 630 km), spanning the last solar cycle maximum (October 1999 -January 2003). For each satellite, and for each of the above men-tioned atmospheric density models, the fitted drag coefficient was obtained with CDFIT, using the observed orbital decay, and then compared with the corresponding physical drag coefficient estimated in the previous study (Pardini et al., 2009). It was consequently possible to derive the average density biases of the thermospheric models during the considered time span. The average results obtained for the last sunspot maximum can be summarized as follows (the sign "+" means that the atmospheric density is overestimated by the model, while the sign "-" means that the atmospheric density is underestimated

3. Identification of Multivariate Time Series and Multivariate Input-Output Models

Cooper, David M.; Wood, Eric F.

1982-08-01

The problem of linear model structure identification for multivariate time series or multiple input-output models is presented and solved. The identification is obtained using canonical correlations to determine model order. The equivalence between state-space model structure and multivariate autoregressive moving average with exogenous inputs (ARMAX) models is presented. The class of models open to analysis includes rainfall-runoff models, multivariate streamflow models, and time invariant state-space models used in Kaiman filtering. Examples include a rainfall-runoff model using three precipitation inputs, a four-site monthly streamflow model, and a four-season streamflow model.

4. Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R.

PubMed

Asar, Ozgür; Ilk, Ozlem

2014-07-01

Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.

5. Managing Disagreement: A Defense of "Regime Bias"

PubMed

Sabl, Andrew

2011-02-01

Stein Ringen's theory of democratic purpose cannot do the work expected of it. Ringen's own criteria oscillate between being too vague to be useful (i.e. "freedom") or, when specified more fully, conflicting, so that almost all democracies will seem to be potentially at cross-purposes with themselves rather than their purposes or sub-purposes being mutually reinforcing. This reflects a bigger and more theoretical problem. Disagreement about the purpose of democracy is built into democracy itself. The whole point of many (perhaps all) of our democratic institutions is to arrive at conditionally legitimate decisions in spite of such disagreement. So-called regime bias, i.e. the tendency to assess democracies according to the form and stability of their institutions rather than their results or their ability to serve certain purposes, does not in fact arise from bias. It arises on the contrary from a determination to avoid the bias inherent in giving some-inevitably partisan-ideals of what democracies should do pride of place over others in a scheme of measurement or evaluation. And even a regime-based definition of democracy must itself make simplifying assumptions that elide possible normative controversies over how the democratic game is best played. Vindicating one's preferred set of democratic ideals against alternatives is a completely legitimate enterprise and lends richness to debates within and across democracies. But it is an inherently ideological and political enterprise, not a neutral or scholarly one.

6. Reducing bias through directed acyclic graphs

PubMed Central

Shrier, Ian; Platt, Robert W

2008-01-01

Background The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate. Discussion The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view. Summary Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model. PMID:18973665

7. The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.

PubMed

Kendal, Jeremy; Giraldeau, Luc-Alain; Laland, Kevin

2009-09-21

Humans and other animals do not use social learning indiscriminately, rather, natural selection has favoured the evolution of social learning rules that make selective use of social learning to acquire relevant information in a changing environment. We present a gene-culture coevolutionary analysis of a small selection of such rules (unbiased social learning, payoff-biased social learning and frequency-dependent biased social learning, including conformism and anti-conformism) in a population of asocial learners where the environment is subject to a constant probability of change to a novel state. We define conditions under which each rule evolves to a genetically polymorphic equilibrium. We find that payoff-biased social learning may evolve under high levels of environmental variation if the fitness benefit associated with the acquired behaviour is either high or low but not of intermediate value. In contrast, both conformist and anti-conformist biases can become fixed when environment variation is low, whereupon the mean fitness in the population is higher than for a population of asocial learners. Our examination of the population dynamics reveals stable limit cycles under conformist and anti-conformist biases and some highly complex dynamics including chaos. Anti-conformists can out-compete conformists when conditions favour a low equilibrium frequency of the learned behaviour. We conclude that evolution, punctuated by the repeated successful invasion of different social learning rules, should continuously favour a reduction in the equilibrium frequency of asocial learning, and propose that, among competing social learning rules, the dominant rule will be the one that can persist with the lowest frequency of asocial learning.

8. Negativity Bias in Dangerous Drivers

PubMed Central

Chai, Jing; Qu, Weina; Sun, Xianghong; Zhang, Kan; Ge, Yan

2016-01-01

The behavioral and cognitive characteristics of dangerous drivers differ significantly from those of safe drivers. However, differences in emotional information processing have seldom been investigated. Previous studies have revealed that drivers with higher anger/anxiety trait scores are more likely to be involved in crashes and that individuals with higher anger traits exhibit stronger negativity biases when processing emotions compared with control groups. However, researchers have not explored the relationship between emotional information processing and driving behavior. In this study, we examined the emotional information processing differences between dangerous drivers and safe drivers. Thirty-eight non-professional drivers were divided into two groups according to the penalty points that they had accrued for traffic violations: 15 drivers with 6 or more points were included in the dangerous driver group, and 23 drivers with 3 or fewer points were included in the safe driver group. The emotional Stroop task was used to measure negativity biases, and both behavioral and electroencephalograph data were recorded. The behavioral results revealed stronger negativity biases in the dangerous drivers than in the safe drivers. The bias score was correlated with self-reported dangerous driving behavior. Drivers with strong negativity biases reported having been involved in mores crashes compared with the less-biased drivers. The event-related potentials (ERPs) revealed that the dangerous drivers exhibited reduced P3 components when responding to negative stimuli, suggesting decreased inhibitory control of information that is task-irrelevant but emotionally salient. The influence of negativity bias provides one possible explanation of the effects of individual differences on dangerous driving behavior and traffic crashes. PMID:26765225

9. Socially biased learning in monkeys.

PubMed

Fragaszy, D; Visalberghi, E

2004-02-01

We review socially biased learning about food and problem solving in monkeys, relying especially on studies with tufted capuchin monkeys (Cebus apella) and callitrichid monkeys. Capuchin monkeys most effectively learn to solve a new problem when they can act jointly with an experienced partner in a socially tolerant setting and when the problem can be solved by direct action on an object or substrate, but they do not learn by imitation. Capuchin monkeys are motivated to eat foods, whether familiar or novel, when they are with others that are eating, regardless of what the others are eating. Thus, social bias in learning about foods is indirect and mediated by facilitation of feeding. In most respects, social biases in learning are similar in capuchins and callitrichids, except that callitrichids provide more specific behavioral cues to others about the availability and palatability of foods. Callitrichids generally are more tolerant toward group members and coordinate their activity in space and time more closely than capuchins do. These characteristics support stronger social biases in learning in callitrichids than in capuchins in some situations. On the other hand, callitrichids' more limited range of manipulative behaviors, greater neophobia, and greater sensitivity to the risk of predation restricts what these monkeys learn in comparison with capuchins. We suggest that socially biased learning is always the collective outcome of interacting physical, social, and individual factors, and that differences across populations and species in social bias in learning reflect variations in all these dimensions. Progress in understanding socially biased learning in nonhuman species will be aided by the development of appropriately detailed models of the richly interconnected processes affecting learning.

10. The effect of cognitive bias modification for interpretation on avoidance of pain during an acute experimental pain task.

PubMed

Jones, Emma Blaisdale; Sharpe, Louise

2014-08-01

Research confirms that patients with chronic pain show a tendency to interpret ambiguous stimuli as pain related. However, whether modifying these interpretive pain biases impacts pain outcomes is unknown. This study aimed to demonstrate that interpretation biases towards pain can be modified, and that changing these biases influences pain outcomes in the cold pressor task. One hundred and six undergraduate students were randomly allocated to receive either threatening or reassuring information regarding the cold pressor. They also were randomly allocated to 1 of 2 conditions in the Ambiguous Scenarios Task, in which they were trained to have either a threatening interpretation of pain (pain bias condition) or a nonthreatening interpretation of pain (no pain bias condition). Therefore, the study had a 2 (threat/reassuring)×2 (pain bias/no pain bias) design. Analyses showed that a bias was induced contingent on condition, and that the threat manipulation was effective. Participants in the pain bias condition hesitated more before doing the cold pressor task than those in the no pain bias condition, as did those in the threat compared with the reassurance condition. The major finding was that interpretive bias mediated the relationship between bias condition and hesitance time, supporting the causal role of interpretive biases for avoidance behaviors in current chronic pain models. No differences were found on other pain outcomes regarding bias or threat, and the efficacy of the bias modification was not impacted by different levels of threat. These results suggest that cognitive bias modification should be further explored as a potential intervention in pain.

11. Maintenance of Motility Bias during Cyanobacterial Phototaxis

PubMed Central

Chau, Rosanna Man Wah; Ursell, Tristan; Wang, Shuo; Huang, Kerwyn Casey; Bhaya, Devaki

2015-01-01

Signal transduction in bacteria is complex, ranging across scales from molecular signal detectors and effectors to cellular and community responses to stimuli. The unicellular, photosynthetic cyanobacterium Synechocystis sp. PCC6803 transduces a light stimulus into directional movement known as phototaxis. This response occurs via a biased random walk toward or away from a directional light source, which is sensed by intracellular photoreceptors and mediated by Type IV pili. It is unknown how quickly cells can respond to changes in the presence or directionality of light, or how photoreceptors affect single-cell motility behavior. In this study, we use time-lapse microscopy coupled with quantitative single-cell tracking to investigate the timescale of the cellular response to various light conditions and to characterize the contribution of the photoreceptor TaxD1 (PixJ1) to phototaxis. We first demonstrate that a community of cells exhibits both spatial and population heterogeneity in its phototactic response. We then show that individual cells respond within minutes to changes in light conditions, and that movement directionality is conferred only by the current light directionality, rather than by a long-term memory of previous conditions. Our measurements indicate that motility bias likely results from the polarization of pilus activity, yielding variable levels of movement in different directions. Experiments with a photoreceptor (taxD1) mutant suggest a supplementary role of TaxD1 in enhancing movement directionality, in addition to its previously identified role in promoting positive phototaxis. Motivated by the behavior of the taxD1 mutant, we demonstrate using a reaction-diffusion model that diffusion anisotropy is sufficient to produce the observed changes in the pattern of collective motility. Taken together, our results establish that single-cell tracking can be used to determine the factors that affect motility bias, which can then be coupled with

12. A generalized multivariate regression model for modelling ocean wave heights

Wang, X. L.; Feng, Y.; Swail, V. R.

2012-04-01

In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.

13. Learning multivariate distributions by competitive assembly of marginals.

PubMed

Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

2013-02-01

We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

14. Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2002-01-01

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

15. Transfer entropy between multivariate time series

Mao, Xuegeng; Shang, Pengjian

2017-06-01

It is a crucial topic to identify the direction and strength of the interdependence between time series in multivariate systems. In this paper, we propose the method of transfer entropy based on the theory of time-delay reconstruction of a phase space, which is a model-free approach to detect causalities in multivariate time series. This method overcomes the limitation that original transfer entropy only can capture which system drives the transition probabilities of another in scalar time series. Using artificial time series, we show that the driving character is obviously reflected with the increase of the coupling strength between two signals and confirm the effectiveness of the method with noise added. Furthermore, we utilize it to real-world data, namely financial time series, in order to characterize the information flow among different stocks.

16. Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2004-03-23

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

17. Multivariate analysis of endometrial tissue fluorescence spectra

Vaitkuviene, Aurelija; Auksorius, E.; Fuchs, D.; Gavriushin, V.

2002-10-01

Background and Objective: The detailed multivariate analysis of endometrial tissue fluorescence spectra was done. Spectra underlying features and classification algorithm were analyzed. An effort has been made to determine the importance of neopterin component in endometrial premalignization. Study Design/Materials and Methods: Biomedical tissue fluorescence was measured by excitation with the Nd YAG laser third harmonic. Multivariate analysis techniques were used to analyze fluorescence spectra. Biomedical optics group at Vilnius University analyzed the neopterin substance supplied by the Institute of Medical Chemistry and Biochemistry of Innsbruck University. Results: Seven statistically significant spectral compounds were found. The classification algorithm classifying samples to histopathological categories was developed and resulted in sensitivity of 80% and specificity 93% for malignant vs. hyperplastic and normal. Conclusions: Fluorescence spectra could be classified with high accuracy. Spectral variation underlying features can be extracted. Neopterin component might play an important role in endometrial hyperplasia development.

18. Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.

19. Design of multivariable controllers for robot manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1986-01-01

The paper presents a simple method for the design of linear multivariable controllers for multi-link robot manipulators. The control scheme consists of multivariable feedforward and feedback controllers. The feedforward controller is the minimal inverse of the linearized model of robot dynamics and contains only proportional-double-derivative (PD2) terms. This controller ensures that the manipulator joint angles track any reference trajectories. The feedback controller is of proportional-integral-derivative (PID) type and achieves pole placement. This controller reduces any initial tracking error to zero as desired and also ensures that robust steady-state tracking of step-plus-exponential trajectories is achieved by the joint angles. The two controllers are independent of each other and are designed separately based on the linearized robot model and then integrated in the overall control scheme. The proposed scheme is simple and can be implemented for real-time control of robot manipulators.

20. Design of multivariable controllers for robot manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1986-01-01

The paper presents a simple method for the design of linear multivariable controllers for multi-link robot manipulators. The control scheme consists of multivariable feedforward and feedback controllers. The feedforward controller is the minimal inverse of the linearized model of robot dynamics and contains only proportional-double-derivative (PD2) terms. This controller ensures that the manipulator joint angles track any reference trajectories. The feedback controller is of proportional-integral-derivative (PID) type and achieves pole placement. This controller reduces any initial tracking error to zero as desired and also ensures that robust steady-state tracking of step-plus-exponential trajectories is achieved by the joint angles. The two controllers are independent of each other and are designed separately based on the linearized robot model and then integrated in the overall control scheme. The proposed scheme is simple and can be implemented for real-time control of robot manipulators.

1. Multivariate Approaches to Classification in Extragalactic Astronomy

Fraix-Burnet, Didier; Thuillard, Marc; Chattopadhyay, Asis Kumar

2015-08-01

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

2. Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

3. mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.

PubMed

Asar, Özgür; İlk, Özlem

2013-12-01

Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.

4. Exchange biasing of magnetoelectric composites.

PubMed

Lage, Enno; Kirchhof, Christine; Hrkac, Viktor; Kienle, Lorenz; Jahns, Robert; Knöchel, Reinhard; Quandt, Eckhard; Meyners, Dirk

2012-04-22

Magnetoelectric composite materials are promising candidates for highly sensitive magnetic-field sensors. However, the composites showing the highest reported magnetoelectric coefficients require the presence of external d.c. magnetic bias fields, which is detrimental to their use as sensitive high-resolution magnetic-field sensors. Here, we report magnetoelectric composite materials that instead rely on intrinsic magnetic fields arising from exchange bias in the device. Thin-film magnetoelectric two-two composites were fabricated by magnetron sputtering on silicon-cantilever substrates. The composites consist of piezoelectric AlN and multilayers with the sequence Ta/Cu/Mn(70)Ir(30)/Fe(50)Co(50) or Ta/Cu/Mn(70)Ir(30)/Fe(70.2)Co(7.8)Si(12)B(10) serving as the magnetostrictive component. The thickness of the ferromagnetic layers and angle dependency of the exchange bias field are used to adjust the shift of the magnetostriction curve in such a way that the maximum piezomagnetic coefficient occurs at zero magnetic bias field. These self-biased composites show high sensitivity to a.c. magnetic fields with a maximum magnetoelectric coefficient of 96 V cm(-1) Oe(-1) at mechanical resonance.

5. Exchange biasing of magnetoelectric composites

Lage, Enno; Kirchhof, Christine; Hrkac, Viktor; Kienle, Lorenz; Jahns, Robert; Knöchel, Reinhard; Quandt, Eckhard; Meyners, Dirk

2012-06-01

Magnetoelectric composite materials are promising candidates for highly sensitive magnetic-field sensors. However, the composites showing the highest reported magnetoelectric coefficients require the presence of external d.c. magnetic bias fields, which is detrimental to their use as sensitive high-resolution magnetic-field sensors. Here, we report magnetoelectric composite materials that instead rely on intrinsic magnetic fields arising from exchange bias in the device. Thin-film magnetoelectric two-two composites were fabricated by magnetron sputtering on silicon-cantilever substrates. The composites consist of piezoelectric AlN and multilayers with the sequence Ta/Cu/Mn70Ir30/Fe50Co50 or Ta/Cu/Mn70Ir30/Fe70.2Co7.8Si12B10 serving as the magnetostrictive component. The thickness of the ferromagnetic layers and angle dependency of the exchange bias field are used to adjust the shift of the magnetostriction curve in such a way that the maximum piezomagnetic coefficient occurs at zero magnetic bias field. These self-biased composites show high sensitivity to a.c. magnetic fields with a maximum magnetoelectric coefficient of 96 V cm-1 Oe-1 at mechanical resonance.

6. Essentialism promotes children's inter-ethnic bias

PubMed Central

Diesendruck, Gil; Menahem, Roni

2015-01-01

The present study investigated the developmental foundation of the relation between social essentialism and attitudes. Forty-eight Jewish Israeli secular 6-year-olds were exposed to either a story emphasizing essentialism about ethnicity, or stories controlling for the salience of ethnicity or essentialism per se. After listening to a story, children's attitudes were assessed in a drawing and in an IAT task. Compared to the control conditions, children in the ethnic essentialism condition drew a Jewish and an Arab character as farther apart from each other, and the Jewish character with a more positive affect than the Arab character. Moreover, boys in the ethnic essentialism condition manifested a stronger bias in the IAT. These findings reveal an early link between essentialism and inter-group attitudes. PMID:26321992

7. Multivariable PID Controller For Robotic Manipulator

NASA Technical Reports Server (NTRS)

Seraji, Homayoun; Tarokh, Mahmoud

1990-01-01

Gains updated during operation to cope with changes in characteristics and loads. Conceptual multivariable controller for robotic manipulator includes proportional/derivative (PD) controller in inner feedback loop, and proportional/integral/derivative (PID) controller in outer feedback loop. PD controller places poles of transfer function (in Laplace-transform space) of control system for linearized mathematical model of dynamics of robot. PID controller tracks trajectory and decouples input and output.

8. Multi-Variable Analysis and Design Techniques.

DTIC Science & Technology

1981-09-01

by A.G.J.MacFarlane 2 MULTIVARIABLE DESIGN TECHNIQUES BASED ON SINGULAR VALUE GENERALIZATIONS OF CLASSICAL CONTROL by J.C. Doyle 3 LIMITATIONS ON...prototypes to complex mathematical representations. All of these assemblages of information or information generators can loosely be termed "models...non linearities (e.g., control saturation) I neglect of high frequency dynamics. T hese approximations are well understood and in general their impact

9. MvDAT: Multivariate Dependence Analysis Toolbox

Sadegh, M.; Ragno, E.; AghaKouchak, A.

2016-12-01

Hydrologic and climatic variables are interdependent, and it is often necessary to analyze association among variables using multivariate methods. Univariate marginal distributions may not be sufficient to describe hydrologic variables (or events) that bear intrinsic multivariate characteristics. The concept of copula is widely used to model the dependence structure of two (or more) random variables. Multivariate methods and copulas have been used in drought monitoring, frequency analysis, and extreme value analysis, among others. Here, we present a newly developed MultiVariate Dependence Analysis Toolbox (MvDAT) for assessing the dependence structure of target variables using 26 copulas. Copulas included in MvDAT differ in complexity with one to three tunable parameters. The Graphical User Interface (GUI) of this program enables users to conveniently browse the input data, select the desired copula family (one, multiple, or all), and finally choose the optimization approach (local/global) for dependence analysis. The program will automatically plot posterior parameter distributions of selected copula(s), if global optimization is selected, as well as fitted versus empirical probability isolines. Moreover, a summary report is automatically generated that rank the performance of selected copulas based on Maximum Likelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Summary report also details on the best and 95% uncertainty ranges of parameters of each copula, and its best performance in terms of root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) criteria. This package is developed in MATLAB and enables the community to perform dependence analysis using a more rigorous and comprehensive approach.

10. Multivariable root loci on the real axis

NASA Technical Reports Server (NTRS)

Yagle, A. E.; Levy, B. C.

1982-01-01

Some methods for determining the number of branches of multivariable root loci which are located on the real axis at a given point are obtained by using frequency domain methods. An equation for the number of branches is given for the general case, and simpler results for the special cases when the transfer function G(s) has size 2 x 2, and when G(s) is symmetric, are also presented.

11. Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

12. The Evolution of Multivariate Maternal Effects

PubMed Central

Kuijper, Bram; Johnstone, Rufus A.; Townley, Stuart

2014-01-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations. PMID:24722346

13. Multivariable PID Controller For Robotic Manipulator

NASA Technical Reports Server (NTRS)

Seraji, Homayoun; Tarokh, Mahmoud

1990-01-01

Gains updated during operation to cope with changes in characteristics and loads. Conceptual multivariable controller for robotic manipulator includes proportional/derivative (PD) controller in inner feedback loop, and proportional/integral/derivative (PID) controller in outer feedback loop. PD controller places poles of transfer function (in Laplace-transform space) of control system for linearized mathematical model of dynamics of robot. PID controller tracks trajectory and decouples input and output.

14. Assessing causality in multivariate accident models.

PubMed

Elvik, Rune

2011-01-01

This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality. Copyright © 2010 Elsevier Ltd. All rights reserved.

15. PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

16. Control of wastewater using multivariate control chart

Nugraha, Jaka; Fatimah, Is; Prabowo, Rino Galang

2017-03-01

Wastewater treatment is a crucial process in industry cause untreated or improper treatment of wastewater may leads some problems affecting to the other parts of environmental aspects. For many kinds of wastewater treatments, the parameters of Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and the Total Suspend Solid (TSS) are usual parameters to be controlled as a standard. In this paper, the application of multivariate Hotteling T2 Individual was reported to control wastewater treatment. By using wastewater treatment data from PT. ICBP, east Java branch, while the fulfillment of quality standards are based on East Java Governor Regulation No. 72 Year 2013 on Standards of Quality of Waste Water Industry and / or Other Business Activities. The obtained results are COD and TSS has a correlation with BOD values with the correlation coefficient higher than 50%, and it is is also found that influence of the COD and TSS to BOD values are 82% and 1.9% respectively. Based on Multivariate control chart Individual T2 Hotteling, it is found that BOD-COD and BOD-TSS are each one subgroup that are outside the control limits. Thus, it can be said there is a process that is not multivariate controlled, but univariately the variables of BOD, COD and TSS are within specification (standard quality) that has been determined.

17. Multivariable geostatistics in S: the gstat package

Pebesma, Edzer J.

2004-08-01

This paper discusses advantages and shortcomings of the S environment for multivariable geostatistics, in particular when extended with the gstat package, an extension package for the S environments (R, S-Plus). The gstat S package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. In particular, it makes the calculation, simultaneous fitting, and visualisation of a large number of direct and cross (residual) variograms very easy. Gstat was started 10 years ago and was released under the GPL in 1996; gstat.org was started in 1998. Gstat was not initially written for teaching purposes, but for research purposes, emphasising flexibility, scalability and portability. It can deal with a large number of practical issues in geostatistics, including change of support (block kriging), simple/ordinary/universal (co)kriging, fast local neighbourhood selection, flexible trend modelling, variables with different sampling configurations, and efficient simulation of large spatially correlated random fields, indicator kriging and simulation, and (directional) variogram and cross variogram modelling. The formula/models interface of the S language is used to define multivariable geostatistical models. This paper introduces the gstat S package, and discusses a number of design and implementation issues. It also draws attention to a number of papers on integration of spatial statistics software, GIS and the S environment that were presented on the spatial statistics workshop and sessions during the conference Distributed Statistical Computing 2003.

18. Simultaneous confidence regions for multivariate bioequivalence.

PubMed

Pallmann, Philip; Jaki, Thomas

2017-08-30

Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax , that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available. Copyright © 2017 John Wiley & Sons, Ltd.

19. Compressive tracking with incremental multivariate Gaussian distribution

Li, Dongdong; Wen, Gongjian; Zhu, Gao; Zeng, Qiaoling

2016-09-01

Various approaches have been proposed for robust visual tracking, among which compressive tracking (CT) yields promising performance. In CT, Haar-like features are efficiently extracted with a very sparse measurement matrix and modeled as an online updated naïve Bayes classifier to account for target appearance change. The naïve Bayes classifier ignores overlap between Haar-like features and assumes that Haar-like features are independently distributed, which leads to drift in complex scenario. To address this problem, we present an extended CT algorithm, which assumes that all Haar-like features are correlated with each other and have multivariate Gaussian distribution. The mean vector and covariance matrix of multivariate normal distribution are incrementally updated with constant computational complexity to adapt to target appearance change. Each frame is associated with a temporal weight to expend less modeling power on old observation. Based on temporal weight, an update scheme with changing but convergent learning rate is derived with strict mathematic proof. Compared with CT, our extended algorithm achieves a richer representation of target appearance. The incremental multivariate Gaussian distribution is integrated into the particle filter framework to achieve better tracking performance. Extensive experiments on the CVPR2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of-the-art trackers.

20. Perceptual bias, more than age, impacts on eye movements during face processing.

PubMed

Williams, Louise R; Grealy, Madeleine A; Kelly, Steve W; Henderson, Iona; Butler, Stephen H

2016-02-01

1. Cognitive bias modification for anxiety: current evidence and future directions

PubMed Central

Beard, Courtney

2011-01-01

Cognitive bias modification (CBM) is an innovative approach to modifying cognitive biases that confer vulnerability to anxiety. CBM interventions are designed to directly modify attention and interpretation biases via repeated practice on cognitive tasks. Analogue studies have demonstrated that CBM affects cognitive biases and anxiety in a number of anxiety conditions. Multisession CBM treatments have shown preliminary efficacy for generalized social phobia and generalized anxiety disorder, with effect sizes comparable to existing treatments. However, with any newly developing field, there are a number of important limitations of the existing data that need to be addressed before making firm conclusions regarding CBM’s efficacy for anxiety disorders. This article focuses on the theoretical rationale for CBM and the current evidence from analogue and clinical samples. PMID:21306216

2. Cognitive bias modification for anxiety: current evidence and future directions.

PubMed

Beard, Courtney

2011-02-01

Cognitive bias modification (CBM) is an innovative approach to modifying cognitive biases that confer vulnerability to anxiety. CBM interventions are designed to directly modify attention and interpretation biases via repeated practice on cognitive tasks. Analogue studies have demonstrated that CBM affects cognitive biases and anxiety in a number of anxiety conditions. Multisession CBM treatments have shown preliminary efficacy for generalized social phobia and generalized anxiety disorder, with effect sizes comparable to existing treatments. However, with any newly developing field, there are a number of important limitations of the existing data that need to be addressed before making firm conclusions regarding CBM's efficacy for anxiety disorders. This article focuses on the theoretical rationale for CBM and the current evidence from analogue and clinical samples.

3. Multivariate Mapping of Environmental Data Using Extreme Learning Machines

Leuenberger, Michael; Kanevski, Mikhail

2014-05-01

In most real cases environmental data are multivariate, highly variable at several spatio-temporal scales, and are generated by nonlinear and complex phenomena. Mapping - spatial predictions of such data, is a challenging problem. Machine learning algorithms, being universal nonlinear tools, have demonstrated their efficiency in modelling of environmental spatial and space-time data (Kanevski et al. 2009). Recently, a new approach in machine learning - Extreme Learning Machine (ELM), has gained a great popularity. ELM is a fast and powerful approach being a part of the machine learning algorithm category. Developed by G.-B. Huang et al. (2006), it follows the structure of a multilayer perceptron (MLP) with one single-hidden layer feedforward neural networks (SLFNs). The learning step of classical artificial neural networks, like MLP, deals with the optimization of weights and biases by using gradient-based learning algorithm (e.g. back-propagation algorithm). Opposed to this optimization phase, which can fall into local minima, ELM generates randomly the weights between the input layer and the hidden layer and also the biases in the hidden layer. By this initialization, it optimizes just the weight vector between the hidden layer and the output layer in a single way. The main advantage of this algorithm is the speed of the learning step. In a theoretical context and by growing the number of hidden nodes, the algorithm can learn any set of training data with zero error. To avoid overfitting, cross-validation method or "true validation" (by randomly splitting data into training, validation and testing subsets) are recommended in order to find an optimal number of neurons. With its universal property and solid theoretical basis, ELM is a good machine learning algorithm which can push the field forward. The present research deals with an extension of ELM to multivariate output modelling and application of ELM to the real data case study - pollution of the sediments in

4. Heuristic-biased stochastic sampling

SciTech Connect

Bresina, J.L.

1996-12-31

This paper presents a search technique for scheduling problems, called Heuristic-Biased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the realworld problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search.

5. Exchange-biased magnetic vortices.

SciTech Connect

Hoffmann, A.; Sort, J.; Buchanan, K. S.; Nogues, J.; Inst. Catalana de Recerca i Estudis Avancats; Univ. Autonoma de Barcelona

2008-07-01

This paper reviews our work on the interplay between exchange bias due to the coupling of a ferromagnet to an antiferromagnet and the formation of magnetic vortices, which occur due to the patterning of a ferromagnet. Depending on the thermal and magnetic history, a variety of different effects can be observed. Thermal annealing in a saturating magnetic field establishes a spatially homogeneous exchange bias with a uniform unidirectional anisotropy. This results in an angular dependence of the magnetization reversal mode, which can be either via magnetization rotation or vortex nucleation and annihilation. In contrast, thermal annealing in a field smaller than the vortex annihilation field imprints the ferromagnetic vortex configuration in the antiferromagnet with high fidelity resulting in unusual asymmetric hysteresis loops. Furthermore, we discuss how the interfacial nature of the exchange bias can modify the vortex magnetization along the thickness of the ferromagnet.

6. Anchoring bias in online voting

Yang, Zimo; Zhang, Zi-Ke; Zhou, Tao

2012-12-01

Voting online with explicit ratings could largely reflect people's preferences and objects' qualities, but ratings are always irrational, because they may be affected by many unpredictable factors like mood, weather and other people's votes. By analyzing two real systems, this paper reveals a systematic bias embedding in the individual decision-making processes, namely people tend to give a low rating after a low rating, as well as a high rating following a high rating. This so-called anchoring bias is validated via extensive comparisons with null models, and numerically speaking, the extent of bias decays with voting interval in a logarithmic form. Our findings could be applied in the design of recommender systems and considered as important complementary materials to previous knowledge about anchoring effects on financial trades, performance judgments, auctions, and so on.

7. Perceptual and Gaze Biases during Face Processing: Related or Not?

PubMed Central

Samson, Hélène; Fiori-Duharcourt, Nicole; Doré-Mazars, Karine; Lemoine, Christelle; Vergilino-Perez, Dorine

2014-01-01

Previous studies have demonstrated a left perceptual bias while looking at faces, due to the fact that observers mainly use information from the left side of a face (from the observer's point of view) to perform a judgment task. Such a bias is consistent with the right hemisphere dominance for face processing and has sometimes been linked to a left gaze bias, i.e. more and/or longer fixations on the left side of the face. Here, we recorded eye-movements, in two different experiments during a gender judgment task, using normal and chimeric faces which were presented above, below, right or left to the central fixation point or on it (central position). Participants performed the judgment task by remaining fixated on the fixation point or after executing several saccades (up to three). A left perceptual bias was not systematically found as it depended on the number of allowed saccades and face position. Moreover, the gaze bias clearly depended on the face position as the initial fixation was guided by face position and landed on the closest half-face, toward the center of gravity of the face. The analysis of the subsequent fixations revealed that observers move their eyes from one side to the other. More importantly, no apparent link between gaze and perceptual biases was found here. This implies that we do not look necessarily toward the side of the face that we use to make a gender judgment task. Despite the fact that these results may be limited by the absence of perceptual and gaze biases in some conditions, we emphasized the inter-individual differences observed in terms of perceptual bias, hinting at the importance of performing individual analysis and drawing attention to the influence of the method used to study this bias. PMID:24454927

8. Diagonal dominance for the multivariable Nyquist array using function minimization

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1977-01-01

A new technique for the design of multivariable control systems using the multivariable Nyquist array method was developed. A conjugate direction function minimization algorithm is utilized to achieve a diagonal dominant condition over the extended frequency range of the control system. The minimization is performed on the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of either the inverse or direct open loop transfer function matrix. Several new feedback design concepts were also developed, including: (1) dominance control parameters for each control loop; (2) compensator normalization to evaluate open loop conditions for alternative design configurations; and (3) an interaction index to determine the degree and type of system interaction when all feedback loops are closed simultaneously. This new design capability was implemented on an IBM 360/75 in a batch mode but can be easily adapted to an interactive computer facility. The method was applied to the Pratt and Whitney F100 turbofan engine.

9. Using Masculine Generics: Does Generic He' Increase Male Bias in the User's Imagery?

ERIC Educational Resources Information Center

Hamilton, Mykol C.

1988-01-01

Studies the effect of the use of the male generic on imagery. Finds that male bias is higher in the masculine generic condition than in the unbiased condition, and that male subjects are more male-biased than female subjects. Discusses findings in terms of linguistic relativity, prototypicality, and activation of multiple meanings. (FMW)

10. Unpacking the Evidence of Gender Bias

ERIC Educational Resources Information Center

Fulmer, Connie L.

2010-01-01

The purpose of this study was to investigate gender bias in pre-service principals using the Gender-Leader Implicit Association Test. Analyses of student-learning narratives revealed how students made sense of gender bias (biased or not-biased) and how each reacted to evidence (surprised or not-surprised). Two implications were: (1) the need for…

11. Collection Development and the Psychology of Bias

ERIC Educational Resources Information Center

Quinn, Brian

2012-01-01

The library literature addressing the role of bias in collection development emphasizes a philosophical approach. It is based on the notion that bias can be controlled by the conscious act of believing in certain values and adhering to a code of ethics. It largely ignores the psychological research on bias, which suggests that bias is a more…

12. Optimistic bias, sexual assault, and fear.

PubMed

Chapin, John R; Pierce, Mari

2012-01-01

A survey of 431 adults documents optimistic bias regarding people's perceived risk of sexual victimization. The findings extend optimistic bias to crime victimization and contribute to the literature by considering a motivational factor, fear, as a predictor of optimistic bias. The study also yielded significant relationships between optimistic bias and demographic variables, including age, gender, and family structure.

13. Collection Development and the Psychology of Bias

ERIC Educational Resources Information Center

Quinn, Brian

2012-01-01

The library literature addressing the role of bias in collection development emphasizes a philosophical approach. It is based on the notion that bias can be controlled by the conscious act of believing in certain values and adhering to a code of ethics. It largely ignores the psychological research on bias, which suggests that bias is a more…

14. Without Bias: A Guidebook for Nondiscriminatory Communication.

ERIC Educational Resources Information Center

Pickens, Judy E., Ed.; And Others

This guidebook discusses ways to eliminate various types of discrimination from business communications. Separately authored chapters discuss eliminating racial and ethnic bias; eliminating sexual bias; achieving communication sensitive about handicaps of disabled persons; eliminating bias from visual media; eliminating bias from meetings,…

15. Unpacking the Evidence of Gender Bias

ERIC Educational Resources Information Center

Fulmer, Connie L.

2010-01-01

The purpose of this study was to investigate gender bias in pre-service principals using the Gender-Leader Implicit Association Test. Analyses of student-learning narratives revealed how students made sense of gender bias (biased or not-biased) and how each reacted to evidence (surprised or not-surprised). Two implications were: (1) the need for…

16. The Truth and Bias Model of Judgment

ERIC Educational Resources Information Center

West, Tessa V.; Kenny, David A.

2011-01-01

We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of…

17. Measurement Bias Detection through Factor Analysis

ERIC Educational Resources Information Center

Barendse, M. T.; Oort, F. J.; Werner, C. S.; Ligtvoet, R.; Schermelleh-Engel, K.

2012-01-01

Measurement bias is defined as a violation of measurement invariance, which can be investigated through multigroup factor analysis (MGFA), by testing across-group differences in intercepts (uniform bias) and factor loadings (nonuniform bias). Restricted factor analysis (RFA) can also be used to detect measurement bias. To also enable nonuniform…

18. Measurement Bias Detection through Factor Analysis

ERIC Educational Resources Information Center

Barendse, M. T.; Oort, F. J.; Werner, C. S.; Ligtvoet, R.; Schermelleh-Engel, K.

2012-01-01

Measurement bias is defined as a violation of measurement invariance, which can be investigated through multigroup factor analysis (MGFA), by testing across-group differences in intercepts (uniform bias) and factor loadings (nonuniform bias). Restricted factor analysis (RFA) can also be used to detect measurement bias. To also enable nonuniform…

19. The Truth and Bias Model of Judgment

ERIC Educational Resources Information Center

West, Tessa V.; Kenny, David A.

2011-01-01

We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of…

20. The Threshold of Embedded M Collider Bias and Confounding Bias

ERIC Educational Resources Information Center

Kelcey, Benjamin; Carlisle, Joanne

2011-01-01

Of particular import to this study, is collider bias originating from stratification on retreatment variables forming an embedded M or bowtie structural design. That is, rather than assume an M structural design which suggests that "X" is a collider but not a confounder, the authors adopt what they consider to be a more reasonable…

1. Reducing Muslim/Arab stereotypes through evaluative conditioning.

PubMed

French, Andrea R; Franz, Timothy M; Phelan, Laura L; Blaine, Bruce E

2013-01-01

This study replicated and extended Olson and Fazio (2006) by testing whether evaluative conditioning is a means to reduce negative stereotypes about Muslim and other Arab persons. Specifically, evaluative conditioning was hypothesized to lower implicit biases against Muslim and Arab persons. The FreeIAT was used to measure implicit biases. Participants in the evaluative conditioning group showed a significant lowering in implicit biases. Explicit measures of bias were not affected by the conditioning procedure.

2. The Electrically Controlled Exchange Bias

Harper, Jacob

Controlling magnetism via voltage in the virtual absence of electric current is the key to reduce power consumption while enhancing processing speed, integration density and functionality in comparison with present-day information technology. Almost all spintronic devices rely on tailored interface magnetism. Controlling magnetism at thin-film interfaces, preferably by purely electrical means, is therefore a key challenge to better spintronics. However, there is no direct interaction between magnetization and electric fields, thus making voltage control of magnetism in general a scientific challenge. The significance of controlled interface magnetism started with the exchange-bias effect. Exchange bias is a coupling phenomenon at magnetic interfaces that manifests itself prominently in the shift of the ferromagnetic hysteresis loop along the magnetic-field axis. Various attempts on controlling exchange bias via voltage utilizing different scientific principles have been intensively studied recently. The majority of present research is emphasizing on various complex oxides. Our approach can be considered as a paradigm shift away from complex oxides. We focus on a magnetoelectric antiferromagnetic simple oxide Cr2O3. From a combination of experimental and theoretical efforts, we show that the (0001) surface of magnetoelectric Cr2O3 has a roughness-insensitive, electrically switchable magnetization. Using a ferromagnetic Pd/Co multilayer deposited on the (0001) surface of a Cr2O3 single crystal, we achieve reversible, room-temperature isothermal switching of the exchange-bias between positive and negative values by reversing the electric field while maintaining a permanent magnetic field. This is a significant scientific breakthrough providing a new route towards potentially revolutionizing information technology. In addition, a second path of electrically controlled exchange bias is introduced by exploiting the piezoelectric property of BaTiO3. An exchange-bias Co

3. Bias correction with Data Assimilation

Canter, Martin; Barth, Alexander

2015-04-01

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model's equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. Indeed, we were able to estimate and recover an artificial bias that had been added into the model. This bias had a spatial structure and was constant through time. The mean and behaviour of the corrected model corresponded to those the reference model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter

4. Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

5. Biases in comparative analyses of extinction risk: mind the gap.

PubMed

González-Suárez, Manuela; Lucas, Pablo M; Revilla, Eloy

2012-11-01

1. Comparative analyses are used to address the key question of what makes a species more prone to extinction by exploring the links between vulnerability and intrinsic species' traits and/or extrinsic factors. This approach requires comprehensive species data but information is rarely available for all species of interest. As a result comparative analyses often rely on subsets of relatively few species that are assumed to be representative samples of the overall studied group. 2. Our study challenges this assumption and quantifies the taxonomic, spatial, and data type biases associated with the quantity of data available for 5415 mammalian species using the freely available life-history database PanTHERIA. 3. Moreover, we explore how existing biases influence results of comparative analyses of extinction risk by using subsets of data that attempt to correct for detected biases. In particular, we focus on links between four species' traits commonly linked to vulnerability (distribution range area, adult body mass, population density and gestation length) and conduct univariate and multivariate analyses to understand how biases affect model predictions. 4. Our results show important biases in data availability with c.22% of mammals completely lacking data. Missing data, which appear to be not missing at random, occur frequently in all traits (14-99% of cases missing). Data availability is explained by intrinsic traits, with larger mammals occupying bigger range areas being the best studied. Importantly, we find that existing biases affect the results of comparative analyses by overestimating the risk of extinction and changing which traits are identified as important predictors. 5. Our results raise concerns over our ability to draw general conclusions regarding what makes a species more prone to extinction. Missing data represent a prevalent problem in comparative analyses, and unfortunately, because data are not missing at random, conventional approaches to fill

6. Multivariable adaptive control using only input and output measurements for turbojet engines

SciTech Connect

Huang, J.Q.; Sun, J.G.

1995-04-01

Current and future aircraft engines are increasingly relying upon the use of multivariable control approach for meeting advanced performance requirements. A multivariable model reference adaptive control (MRAC) scheme is proposed in this paper. The adaptation law is derived using only input and output (I/O) measurements. Simulation studies are performed for a two-spool turbojet engine. The satisfactory transient responses are obtained at different operating points from idle to maximum dry power level and flight condition. Simulation results also show high effectiveness of reducing interaction in multivariable systems with significant coupling. Using the multivariable MRAC controller, the engine acceleration time is reduced by about 19 percent in comparison with the conventional engine controller.

7. Source memory for unrecognized items: predictions from multivariate signal detection theory.

PubMed

Starns, Jeffrey J; Hicks, Jason L; Brown, Noelle L; Martin, Benjamin A

2008-01-01

We report three experiments investigating source memory for words that were called "new" on a recognition test. In each experiment, participants could accurately specify the source of words that they failed to recognize. Results also demonstrated that source memory for unrecognized items varied with the bias to respond "old" in recognition decisions: Participants displayed unrecognized source memory when they were told that 25% of the recognition test words were old (promoting conservative responding) but not when they were told that 75% of the test words were old (promoting liberal responding). Our results were successfully predicted by a multivariate signal detection approach to recognition/source memory.

8. Adaptation to high throughput batch chromatography enhances multivariate screening.

PubMed

Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

2015-09-01

High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

9. Bias in Dynamic Monte Carlo Alpha Calculations

SciTech Connect

Sweezy, Jeremy Ed; Nolen, Steven Douglas; Adams, Terry R.; Trahan, Travis John

2015-02-06

A 1/N bias in the estimate of the neutron time-constant (commonly denoted as α) has been seen in dynamic neutronic calculations performed with MCATK. In this paper we show that the bias is most likely caused by taking the logarithm of a stochastic quantity. We also investigate the known bias due to the particle population control method used in MCATK. We conclude that this bias due to the particle population control method is negligible compared to other sources of bias.

10. A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods.

PubMed

Ma, Jianming; Kockelman, Kara M; Damien, Paul

2008-05-01

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.

11. A Multivariate Statistical Analysis of Spiral Galaxy Luminosities. I. Data and Results

Shapley, Alice; Fabbiano, G.; Eskridge, P. B.

2001-11-01

We have performed a multiparametric analysis of luminosity data for a sample of 234 normal spiral and irregular galaxies observed in X-rays with the Einstein Observatory. This sample is representative of S and Irr galaxies, with a good coverage of morphological types and absolute magnitudes. In addition to X-ray and optical data, we have compiled H-band magnitudes, IRAS near- and far-infrared, and 6 cm radio continuum observations for the sample from the literature. We have also performed a careful compilation of distance estimates. We have explored the effect of morphology by dividing the sample into early- (S0/a-Sab), intermediate- (Sb-Sbc), and late-type (Sc-Irr) subsamples. The data were analyzed with bivariate and multivariate survival analysis techniques that make full use of all the information available in both detections and limits. We find that most pairs of luminosities are correlated when considered individually, and this is not due to a distance bias. Different luminosity-luminosity correlations follow different power-law relations. Contrary to previous reports, the LX-LB correlation follows a power law with exponent larger than 1. Both the significances of some correlations and their power-law relations are morphology dependent. Our analysis confirms the ``representative'' nature of our sample, by returning well-known results derived from previous analyses of independent samples of galaxies (e.g., the LB-LH, L12-LFIR, and LFIR-L6 cm correlations). Our multivariate analysis suggests that there are two fundamentally strong correlations, regardless of galaxy morphology, when all the wave bands are analyzed together with conditional probability methods. These are the LB-LH and the L12-LFIR correlations. As it is well known, the former links stellar emission processes and points to a basic connection between the initial mass function of low-mass and intermediate- to high-mass stars. The latter may be related to the heating of small and larger size dust

12. Performance of the disease risk score in a cohort study with policy-induced selection bias.

PubMed

2015-11-01

To examine the performance of the disease risk score (DRS) in a cohort study with evidence of policy-induced selection bias. We examined two cohorts of new users of bisphosphonates. Estimates for 1-year hip fracture rates between agents using DRS, exposure propensity scores and traditional multivariable analysis were compared. The results for the cohort with no evidence of policy-induced selection bias showed little variation across analyses (-4.1-2.0%). Analysis of the cohort with evidence of policy-induced selection bias showed greater variation (-13.5-8.1%), with the greatest difference seen with DRS analyses. Our findings suggest that caution may be warranted when using DRS methods in cohort studies with policy-induced selection bias, further research is needed.

13. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas

2016-01-01

An ensemble of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically downscale global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.

14. Combating Anti-Muslim Bias

ERIC Educational Resources Information Center

Shah, Nirvi

2011-01-01

America's 2.5 million Muslims make up less than 1% of the U.S. population, according to the Pew Research Center. Many Muslim students face discrimination and some cases have warranted investigation by the U.S. Department of Education's Office of Civil Rights. Muslim groups have reported widespread bias as well. For many Muslim…

15. Response Bias in Hospice Evaluation.

ERIC Educational Resources Information Center

Hayslip, Bert, Jr.; And Others

1991-01-01

Analyzed response bias among 34 recipients of care in hospice. Found nonrespondents to have better bereavement prognoses and tended to care for patients who were younger, male, and in program for shorter time. Nonrespondents were in contact with staff less than were respondents. Data are consistent with earlier research showing significant…

16. Attributional Bias and Course Evaluations.

ERIC Educational Resources Information Center

Gigliotti, Richard J.; Buchtel, Foster S.

1990-01-01

How self-serving bias affects evaluations of college courses was tested for 691 students by comparing a model predicting that evaluations reflect actual grades with a model predicting that evaluations reflect confirmation or disconfirmation of expectations. Results support course evaluation validity by indicating a minimal effect of self-serving…

17. Test Bias and Construct Validity.

ERIC Educational Resources Information Center

Jensen, Arthur R.

The several statistical methods described for detecting test bias in terms of various internal features of a person's test performances and the test's construct validity can be applied to any groups in the population. But the evidence regarding groups other than U.S. blacks and whites is either lacking or is still too sketchy to permit any strong…

18. Key Words in Instruction. Bias

ERIC Educational Resources Information Center

Callison, Daniel

2005-01-01

Two challenging criteria for judging information involve bias and authority. In both cases, judgments may not be clearly possible. In both cases, there may be degrees or levels of acceptability. For students to gain experience and to demonstrate skills in making judgments, they need opportunities to consider a wide spectrum of resources under a…

19. Cultural Bias in Testing ESL.

ERIC Educational Resources Information Center

Cargill-Power, C.

Although cultural content is unavoidable as a backdrop for good language testing, cultural bias in testing English as a second language presents many dangers. A picture cue calling for a correct grammatical response may evoke an incorrect answer if the pictorial content is culturally coded. The cultural background behind a test must be accurately…

20. The Identification of Biased Items.

ERIC Educational Resources Information Center

Sinnott, Loraine T.

A standard method for exploring item bias is the intergroup comparison of item difficulties. This paper describes a refinement and generalization of this technique. In contrast to prior approaches, the proposed method deletes outlying items from the formulation of a criterion for identifying items as deviant. It also extends the mathematical…

1. Recall bias, MMR, and autism.

PubMed

Andrews, N; Miller, E; Taylor, B; Lingam, R; Simmons, A; Stowe, J; Waight, P

2002-12-01

Parents of autistic children with regressive symptoms who were diagnosed after the publicity alleging a link with measles, mumps, and rubella (MMR) vaccine tended to recall the onset as shortly after MMR more often than parents of similar children who were diagnosed prior to the publicity. This is consistent with the recall bias expected under such circumstances.

2. Gender Bias in the Courts.

ERIC Educational Resources Information Center

Gill, Wanda E.

The term gender bias was coined by the National Judicial Education Program to Promote Equality for Women and Men in the Courts and is defined as the predisposition or tendency to think about and behave toward people primarily on the basis of their sex rather than their status, professional accomplishments, or aspirations. An effective method for…

3. Stereotype Formation: Biased by Association

ERIC Educational Resources Information Center

Le Pelley, Mike E.; Reimers, Stian J.; Calvini, Guglielmo; Spears, Russell; Beesley, Tom; Murphy, Robin A.

2010-01-01

We propose that biases in attitude and stereotype formation might arise as a result of learned differences in the extent to which social groups have previously been predictive of behavioral or physical properties. Experiments 1 and 2 demonstrate that differences in the experienced predictiveness of groups with respect to evaluatively neutral…

4. Attentional bias in math anxiety

PubMed Central

Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly

2015-01-01

Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms. PMID:26528208

5. Misclassification bias in areal estimates

Treesearch

Raymond L. Czaplewski

1992-01-01

In addition to thematic maps, remote sensing provides estimates of area in different thematic categories. Areal estimates are frequently used for resource inventories, management planning, and assessment analyses. Misclassification causes bias in these statistical areal estimates. For example, if a small percentage of a common cover type is misclassified as a rare...

6. Sex Bias in Counseling Materials

ERIC Educational Resources Information Center

Harway, Michele

1977-01-01

This article reviews findings of bias in counseling materials and presents results of three original studies. Indications are that textbooks used by practitioners present the sexes in stereotypical fashion, and a greater proportion of college catalog context is devoted to men than to women. (Author)

7. Combating Anti-Muslim Bias

ERIC Educational Resources Information Center

Shah, Nirvi

2011-01-01

America's 2.5 million Muslims make up less than 1% of the U.S. population, according to the Pew Research Center. Many Muslim students face discrimination and some cases have warranted investigation by the U.S. Department of Education's Office of Civil Rights. Muslim groups have reported widespread bias as well. For many Muslim…

8. Are Culturally Biased Test Useful?

ERIC Educational Resources Information Center

Kelley, H. Paul

1982-01-01

Whether culturally biased tests are useful depends on what is meant by that phrase and the purpose for which the test is to be used. Keeping the distinction between aptitude and achievement in mind, different definitions of fair use of tests come from different sets of societal values. (Author/CM)

9. Stereotype Formation: Biased by Association

ERIC Educational Resources Information Center

Le Pelley, Mike E.; Reimers, Stian J.; Calvini, Guglielmo; Spears, Russell; Beesley, Tom; Murphy, Robin A.

2010-01-01

We propose that biases in attitude and stereotype formation might arise as a result of learned differences in the extent to which social groups have previously been predictive of behavioral or physical properties. Experiments 1 and 2 demonstrate that differences in the experienced predictiveness of groups with respect to evaluatively neutral…

10. Time varying, multivariate volume data reduction

SciTech Connect

Ahrens, James P; Fout, Nathaniel; Ma, Kwan - Liu

2010-01-01

Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

11. Multivariate Analysis of Genotype–Phenotype Association

PubMed Central

Mitteroecker, Philipp; Cheverud, James M.; Pavlicev, Mihaela

2016-01-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated—in terms of effect size—with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype–phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype–phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype–phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype–phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3—the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the

12. Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map

13. Cross-country transferability of multi-variable damage models

Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

2017-04-01

Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

14. Multi-application controls: Robust nonlinear multivariable aerospace controls applications

NASA Technical Reports Server (NTRS)

Enns, Dale F.; Bugajski, Daniel J.; Carter, John; Antoniewicz, Bob

1994-01-01

This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented

15. Effect of the 2010 Chilean Earthquake on Posttraumatic Stress Reducing Sensitivity to Unmeasured Bias Through Study Design

PubMed Central

Zubizarreta, José R.; Cerdá, Magdalena; Rosenbaum, Paul R.

2013-01-01

In 2010, a magnitude 8.8 earthquake hit Chile, devastating parts of the country. Having just completed its national socioeconomic survey, the Chilean government reinterviewed a subsample of respondents, creating unusual longitudinal data about the same persons before and after a major disaster. The follow-up evaluated posttraumatic stress symptoms (PTSS) using Davidson’s Trauma Scale. We use these data with two goals in mind. Most studies of PTSS after disasters rely on recall to characterize the state of affairs before the disaster. We are able to use prospective data on preexposure conditions, free of recall bias, to study the effects of the earthquake. Second, we illustrate recent developments in statistical methodology for the design and analysis of observational studies. In particular, we use new and recent methods for multivariate matching to control 46 covariates that describe demographic variables, housing quality, wealth, health, and health insurance before the earthquake. We use the statistical theory of design sensitivity to select a study design with findings expected to be insensitive to small or moderate biases from failure to control some unmeasured covariate. PTSS were dramatically but unevenly elevated among residents of strongly shaken areas of Chile when compared with similar persons in largely untouched parts of the country. In 96% of exposed-control pairs exhibiting substantial PTSS, it was the exposed person who experienced stronger symptoms (95% confidence interval = 0.91–1.00). PMID:23222557

16. Effect of the 2010 Chilean earthquake on posttraumatic stress: reducing sensitivity to unmeasured bias through study design.

PubMed

Zubizarreta, José R; Cerdá, Magdalena; Rosenbaum, Paul R

2013-01-01

In 2010, a magnitude 8.8 earthquake hit Chile, devastating parts of the country. Having just completed its national socioeconomic survey, the Chilean government reinterviewed a subsample of respondents, creating unusual longitudinal data about the same persons before and after a major disaster. The follow-up evaluated posttraumatic stress symptoms (PTSS) using Davidson's Trauma Scale. We use these data with two goals in mind. Most studies of PTSS after disasters rely on recall to characterize the state of affairs before the disaster. We are able to use prospective data on preexposure conditions, free of recall bias, to study the effects of the earthquake. Second, we illustrate recent developments in statistical methodology for the design and analysis of observational studies. In particular, we use new and recent methods for multivariate matching to control 46 covariates that describe demographic variables, housing quality, wealth, health, and health insurance before the earthquake. We use the statistical theory of design sensitivity to select a study design with findings expected to be insensitive to small or moderate biases from failure to control some unmeasured covariate. PTSS were dramatically but unevenly elevated among residents of strongly shaken areas of Chile when compared with similar persons in largely untouched parts of the country. In 96% of exposed-control pairs exhibiting substantial PTSS, it was the exposed person who experienced stronger symptoms (95% confidence interval = 0.91-1.00).

17. Multivariate Lipschitz optimization: Survey and computational comparison

SciTech Connect

Hansen, P.; Gourdin, E.; Jaumard, B.

1994-12-31

Many methods have been proposed to minimize a multivariate Lipschitz function on a box. They pertain the three approaches: (i) reduction to the univariate case by projection (Pijavskii) or by using a space-filling curve (Strongin); (ii) construction and refinement of a single upper bounding function (Pijavskii, Mladineo, Mayne and Polak, Jaumard Hermann and Ribault, Wood...); (iii) branch and bound with local upper bounding functions (Galperin, Pint{acute e}r, Meewella and Mayne, the present authors). A survey is made, stressing similarities of algorithms, expressed when possible within a unified framework. Moreover, an extensive computational comparison is reported on.

18. Multivariate postprocessing techniques for probabilistic hydrological forecasting

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power

19. Mixtures of multivariate power exponential distributions.

PubMed

Dang, Utkarsh J; Browne, Ryan P; McNicholas, Paul D

2015-12-01

An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness have received much attention in the model-based clustering literature recently, we investigate the use of a distribution that can deal with both varying tail-weight and peakedness of data. A family of parsimonious models is proposed using an eigen-decomposition of the scale matrix. A generalized expectation-maximization algorithm is presented that combines convex optimization via a minorization-maximization approach and optimization based on accelerated line search algorithms on the Stiefel manifold. Lastly, the utility of this family of models is illustrated using both toy and benchmark data.

20. New multivariable capabilities of the INCA program

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

1. Multivariate curve-fitting in GAUSS

USGS Publications Warehouse

Bunck, C.M.; Pendleton, G.W.

1988-01-01

Multivariate curve-fitting techniques for repeated measures have been developed and an interactive program has been written in GAUSS. The program implements not only the one-factor design described in Morrison (1967) but also includes pairwise comparisons of curves and rates, a two-factor design, and other options. Strategies for selecting the appropriate degree for the polynomial are provided. The methods and program are illustrated with data from studies of the effects of environmental contaminants on ducklings, nesting kestrels and quail.

2. Bayesian Transformation Models for Multivariate Survival Data

PubMed Central

DE CASTRO, MÁRIO; CHEN, MING-HUI; IBRAHIM, JOSEPH G.; KLEIN, JOHN P.

2014-01-01

In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported. PMID:24904194

3. Multivariate cubic spline smoothing in multiple prediction.

PubMed

Khamis, Harry; Kepler, Michael

2002-02-01

Given longitudinal data for several variables, including a given outcome variable, it is desired to predict the outcome for a specific individual, or more generally experimental unit, in such a way that the predicted value is both accurate and resistant (i.e. has good cross-validation). There are certain data-analytic difficulties associated with long-term multivariate longitudinal data that must be overcome in the prediction process. This paper provides a program written in the Statistical Analysis System (SAS) programming language, based generally on the Roche-Wainer-Thissen stature prediction model, that enables the researcher to overcome these difficulties.

4. F100 multivariable control synthesis program: A review of full scale engine altitude tests. [F100 engine

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Soeder, J. F.

1980-01-01

The benefits of linear quadratic regulator synthesis methods in designing a multivariable engine control capable of operating an engine throughout its flight envelope were demonstrated. The entire multivariable control synthesis program is reviewed with particular emphasis on engine tests conducted in the NASA Lewis propulsion systems laboratory altitude facility. The multivariable control has basically a proportional plus integral, model following structure with gains scheduled as functions of flight condition. The multivariable control logic design is described, along with control computer implementation aspects. Altitude tests demonstrated that the multivariable control logic could control an engine over a wide range of test conditions. Representative transient responses are presented to demonstrate engine behavior and the functioning of the control logic.

5. Correcting for Visuo-Haptic Biases in 3D Haptic Guidance

PubMed Central

Kuling, Irene A.; Brenner, Eli; Bergmann Tiest, Wouter M.; Kappers, Astrid M. L.

2016-01-01

Visuo-haptic biases are observed when bringing your unseen hand to a visual target. The biases are different between, but consistent within participants. We investigated the usefulness of adjusting haptic guidance to these user-specific biases in aligning haptic and visual perception. By adjusting haptic guidance according to the biases, we aimed to reduce the conflict between the modalities. We first measured the biases using an adaptive procedure. Next, we measured performance in a pointing task using three conditions: 1) visual images that were adjusted to user-specific biases, without haptic guidance, 2) veridical visual images combined with haptic guidance, and 3) shifted visual images combined with haptic guidance. Adding haptic guidance increased precision. Combining haptic guidance with user-specific visual information yielded the highest accuracy and the lowest level of conflict with the guidance at the end point. These results show the potential of correcting for user-specific perceptual biases when designing haptic guidance. PMID:27438009

6. Correcting for Visuo-Haptic Biases in 3D Haptic Guidance.

PubMed

van Beek, Femke E; Kuling, Irene A; Brenner, Eli; Bergmann Tiest, Wouter M; Kappers, Astrid M L

2016-01-01

Visuo-haptic biases are observed when bringing your unseen hand to a visual target. The biases are different between, but consistent within participants. We investigated the usefulness of adjusting haptic guidance to these user-specific biases in aligning haptic and visual perception. By adjusting haptic guidance according to the biases, we aimed to reduce the conflict between the modalities. We first measured the biases using an adaptive procedure. Next, we measured performance in a pointing task using three conditions: 1) visual images that were adjusted to user-specific biases, without haptic guidance, 2) veridical visual images combined with haptic guidance, and 3) shifted visual images combined with haptic guidance. Adding haptic guidance increased precision. Combining haptic guidance with user-specific visual information yielded the highest accuracy and the lowest level of conflict with the guidance at the end point. These results show the potential of correcting for user-specific perceptual biases when designing haptic guidance.

7. The influence of referees' expertise, gender, motivation, and time constraints on decisional bias against women.

PubMed

Souchon, Nicolas; Livingstone, Andrew G; Maio, Gregory R

2013-12-01

The influence of player gender on referees' decision making was experimentally investigated. In Experiment 1, including 145 male handball referees, we investigated (a) the influence of referees' level of expertise on their decisional biases against women and (b) the referees' gender stereotypes. Results revealed that biases against women were powerful regardless of the referees' level of expertise and that male referees' stereotype toward female players tends to be negative. In Experiment 2, including 115 sport science students, we examined the influence of the participants' gender, motivation to control bias, and time constraints on gender bias. Results indicated that participants' gender had no impact on gender bias and that participants were able to reduce this bias in conditions in which they were motivated to control the bias.

8. Experimental modification of interpretation bias regarding social and animal fear in children.

PubMed

Lester, K J; Field, A P; Muris, P

2011-06-01

Using an experimental bias modification task, an interpretation bias towards or away from threat was induced about animal or social situations in a sample of 103 children split into a young (7-10 years) and old age group (11-15 years). Children rapidly learned to select outcomes of ambiguous situations which were congruent with their assigned modification condition. Following positive modification, children's threat interpretation biases significantly decreased, while threat biases increased (non-significantly) after negative modification. Bias modification effects also varied as a function of age with children appearing particularly vulnerable to acquiring biases about stimuli that were congruent with the normative fears for their age group. Weak age-related modification-congruent effects on younger but not older children's anxiety vulnerability in response to a behavioral task were also observed. However, no consistent effects of bias modification on avoidance behavior were found.

9. Acute effects of intoxication and arousal on approach / avoidance biases toward sexual risk stimuli in heterosexual men

PubMed Central

Simons, Jeffrey S.; Maisto, Stephen A.; Wray, Tyler B.; Emery, Noah N.

2015-01-01

This study tested the effects of alcohol intoxication and physiological arousal on cognitive biases toward erotic stimuli and condoms. Ninety-seven heterosexual men were randomized to 1 of 6 independent conditions in a 2 (high arousal or control) × 3 (alcohol target BAC = 0.08), placebo, or juice control) design and then completed a variant of the Approach Avoidance Task (AAT). The AAT assessed reaction times toward approaching and avoiding erotic stimuli and condoms with a joystick. Consistent with hypotheses, the alcohol condition exhibited an approach bias toward erotic stimuli, whereas the control and placebo groups exhibited an approach bias toward condom stimuli. Similarly, the participants in the high arousal condition exhibited an approach bias toward erotic stimuli and the low arousal control condition exhibited an approach bias toward condoms. The results suggest that acute changes in intoxication and physiological arousal independently foster biased responding towards sexual stimuli and these biases are associated with sexual risk intentions. PMID:25808719

10. Cognitive bias modification: A review of meta-analyses.

PubMed

Jones, Emma B; Sharpe, Louise

2017-12-01

Cognitive bias modification (CBM) is a novel, but controversial intervention with considerable divergence amongst conclusions in individual studies and reviews. This systematic review synthesizes meta-analyses of CBM to determine whether CBM is effective, and what parameters most reliably evoke the process of CBM. A systematic literature search resulted in twelve meta-analyses in total, from which the published effect sizes were extracted. Attention bias modification (ABM) shifted targeted biases in adults (ES = 0.24-1.16), was effective as a buffer to stressor vulnerability (ES = 0.33-0.77) and in symptom control (ES = 0.16-0.41). Cognitive bias modification for interpretation (CBM-I) modified targeted biases (ES = 0.52-0.81) but did not reliably reduce stressor vulnerability (ES = 0.01-0.24, p > .05). CBM consistently reduced anxiety symptoms, but effects on depressive symptomatology were less compelling. The long-term efficacy of CBM was only supported in addiction studies. The review included a single CBM-I only meta-analysis, and two meta-analyses with pooled reporting on ABM and CBM-I outcomes. Overall, this synthesis shows CBM is effective in the short-term for anxiety in adults, and highlights some conditions under which CBM is most efficacious. Rather than debating the efficacy of CBM, future research should focus on developing procedures that more reliably induce bias modification and determining the most efficacious clinical applications. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

11. Effects of Cognitive Bias Modification Training via Smartphones.

PubMed

Yang, Ranming; Cui, Lixia; Li, Feng; Xiao, Jing; Zhang, Qin; Oei, Tian P S

2017-01-01

Background and Objectives: Negative cognitive biases have been linked to anxiety and mood problems. Accumulated data from laboratory studies show that positive and negative interpretation styles with accompanying changes in mood can be induced through cognitive bias modification (CBM) paradigms. Despite the therapeutic potential of positive training effects, few studies have explored training paradigms administered via smartphones. The current study aimed to compare the effectiveness of three different types of training programmes (cognitive bias modification-attention, CBM-A; cognitive bias modification-interpretation, CBM-I; attention and interpretation modification, AIM) administered via smart-phones by using a control condition (CC). Methods:Seventy-six undergraduate participants with high social anxiety (Liebowitz Social Anxiety Scale, LSAS ≥ 30) were randomly assigned to four groups: CBM-A (n = 20), CBM-I (n = 20), AIM (n = 16), and CC (n = 20). Results: The results showed that the effects of CBM training, CBM-I training, or AIM training vs. CC for attention yielded no significant differences in dot-probe attention bias scores. The CBM-I group showed significantly less threat interpretation and more benign interpretation than the CC group on interpretation bias scores. Conclusions: The present results supported the feasibility of delivering CBM-I via smartphones, but the effectiveness of CBM-A and AIM training via smartphones was limited.

12. Quantification of collider-stratification bias and the birthweight paradox

PubMed Central

Whitcomb, Brian W.; Schisterman, Enrique F.; Perkins, Neil J.; Platt, Robert W.

2009-01-01

Summary The ‘birthweight paradox’ describes the phenomenon whereby birthweight-specific mortality curves cross when stratified on other exposures, most notably cigarette smoking. The paradox has been noted widely in the literature and numerous explanations and corrections have been suggested. Recently, causal diagrams have been used to illustrate the possibility for collider-stratification bias in models adjusting for birthweight. When two variables share a common effect, stratification on the variable representing that effect induces a statistical relation between otherwise independent factors. This bias has been proposed to explain the birthweight paradox. Causal diagrams may illustrate sources of bias, but are limited to describing qualitative effects. In this paper, we provide causal diagrams that illustrate the birthweight paradox and use a simulation study to quantify the collider-stratification bias under a range of circumstances. Considered circumstances include exposures with and without direct effects on neonatal mortality, as well as with and without indirect effects acting through birthweight on neonatal mortality. The results of these simulations illustrate that when the birthweight-mortality relation is subject to substantial uncontrolled confounding, the bias on estimates of effect adjusted for birthweight may be sufficient to yield opposite causal conclusions, i.e. a factor that poses increased risk appears protective. Effects on stratum-specific birthweight-mortality curves were considered to illustrate the connection between collider-stratification bias and the crossing of the curves. The simulations demonstrate the conditions necessary to give rise to empirical evidence of the paradox. PMID:19689488

13. Quantification of collider-stratification bias and the birthweight paradox.

PubMed

Whitcomb, Brian W; Schisterman, Enrique F; Perkins, Neil J; Platt, Robert W

2009-09-01

The 'birthweight paradox' describes the phenomenon whereby birthweight-specific mortality curves cross when stratified on other exposures, most notably cigarette smoking. The paradox has been noted widely in the literature and numerous explanations and corrections have been suggested. Recently, causal diagrams have been used to illustrate the possibility for collider-stratification bias in models adjusting for birthweight. When two variables share a common effect, stratification on the variable representing that effect induces a statistical relation between otherwise independent factors. This bias has been proposed to explain the birthweight paradox. Causal diagrams may illustrate sources of bias, but are limited to describing qualitative effects. In this paper, we provide causal diagrams that illustrate the birthweight paradox and use a simulation study to quantify the collider-stratification bias under a range of circumstances. Considered circumstances include exposures with and without direct effects on neonatal mortality, as well as with and without indirect effects acting through birthweight on neonatal mortality. The results of these simulations illustrate that when the birthweight-mortality relation is subject to substantial uncontrolled confounding, the bias on estimates of effect adjusted for birthweight may be sufficient to yield opposite causal conclusions, i.e. a factor that poses increased risk appears protective. Effects on stratum-specific birthweight-mortality curves were considered to illustrate the connection between collider-stratification bias and the crossing of the curves. The simulations demonstrate the conditions necessary to give rise to empirical evidence of the paradox.

14. Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

15. Fast Multivariate Search on Large Aviation Datasets

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

2010-01-01

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

16. Adaptable Multivariate Calibration Models for Spectral Applications

SciTech Connect

THOMAS,EDWARD V.

1999-12-20

Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.

17. Network structure of multivariate time series

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-01

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

18. Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

19. Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

20. Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

1. Network structure of multivariate time series

PubMed Central

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-01-01

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. PMID:26487040

2. Multivariate intralocus sexual conflict in seed beetles.

PubMed

Berger, David; Berg, Elena C; Widegren, William; Arnqvist, Göran; Maklakov, Alexei A

2014-12-01

Intralocus sexual conflict (IaSC) is pervasive because males and females experience differences in selection but share much of the same genome. Traits with integrated genetic architecture should be reservoirs of sexually antagonistic genetic variation for fitness, but explorations of multivariate IaSC are scarce. Previously, we showed that upward artificial selection on male life span decreased male fitness but increased female fitness compared with downward selection in the seed beetle Callosobruchus maculatus. Here, we use these selection lines to investigate sex-specific evolution of four functionally integrated traits (metabolic rate, locomotor activity, body mass, and life span) that collectively define a sexually dimorphic life-history syndrome in many species. Male-limited selection for short life span led to correlated evolution in females toward a more male-like multivariate phenotype. Conversely, males selected for long life span became more female-like, implying that IaSC results from genetic integration of this suite of traits. However, while life span, metabolism, and body mass showed correlated evolution in the sexes, activity did not evolve in males but, surprisingly, did so in females. This led to sexual monomorphism in locomotor activity in short-life lines associated with detrimental effects in females. Our results thus support the general tenet that widespread pleiotropy generates IaSC despite sex-specific genetic architecture.

3. Deriving ocean climatologies with multivariate coupling

Barth, Alexander; Alvera Azcarate, Aida; Beckers, Jean-Marie

2016-04-01

In situ measurements of ocean properties are generally sparsely distributed and thus undersample the ocean variability. Deriving ocean climatologies is a challenging task especially for biological and chemical parameters where the number of data is, by an order of magnitude, smaller than for physical parameters. However, physical and biogeochemical parameters are related through the ocean dynamics. In particular fronts visible in physical parameters are often related to gradients in biogeochemical parameters. Ocean climatologies are generally derived for different variables independently. For biogeochemical parameters, only the very large-scale variability can be derived for poorly sampled areas. Here we present a method to derive multivariate analysis taking the relationship between physical and biogeochemical variables into account. The benefit of this procedure is showed by using model data for salinity, nitrate and phosphate of the Mediterranean Sea. The model fields are sampled at the locations of true observations (extracted from the World Ocean Database 2013) and the analysed fields are compared to the original model fields. The multivariate analysis result in a reduction of the RMS error and to a better representation of the gradients.

4. Tau identification using multivariate techniques in ATLAS

O'Neil, D. C.; ATLAS Collaboration

2012-06-01

Tau leptons play an important role in the physics program of the LHC. They are being used in electroweak measurements, in detector related studies and in searches for new phenomena like the Higgs boson or Supersymmetry. In the detector, tau leptons are reconstructed as collimated jets with low track multiplicity. Due to the background from QCD multijet processes, efficient tau identification techniques with large fake rejection are essential. Since single variable criteria are not enough to efficiently separate them from jets and electrons, modern multivariate techniques are used. In ATLAS, several advanced algorithms are applied to identify taus, including a projective likelihood estimator and boosted decision trees. All multivariate methods applied to the ATLAS simulated data perform better than the baseline cut analysis. Their performance is shown using high energy data collected at the ATLAS experiment. The improvement ranges from a factor of 2 to 5 in rejection for the same efficiency, depending on the selected efficiency operating point and the number of prongs in the tau decay. The strengths and weaknesses of each technique are also discussed.

5. A multivariate Bayesian model for embryonic growth.

PubMed

Willemsen, Sten P; Eilers, Paul H C; Steegers-Theunissen, Régine P M; Lesaffre, Emmanuel

2015-04-15

Most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately. When applied to a 'reference population', this exercise leads to univariate reference curves against which new individuals can be evaluated. However, growth should be evaluated in totality, that is, by evaluating all body characteristics jointly. Recently, Cole et al. suggested the Superimposition by Translation and Rotation (SITAR) model, which expresses individual growth curves by three subject-specific parameters indicating their deviation from a flexible overall growth curve. This model allows the characterization of normal growth in a flexible though compact manner. In this paper, we generalize the SITAR model in a Bayesian way to multiple dimensions. The multivariate SITAR model allows us to create multivariate reference regions, which is advantageous for prediction. The usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy, collected in the ongoing Rotterdam Predict study. Further, we demonstrate how the model can be used to find determinants of embryonic growth.

6. A multivariate Baltic Sea environmental index.

PubMed

Dippner, Joachim W; Kornilovs, Georgs; Junker, Karin

2012-11-01

Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.

7. Evaluation of a physics-preserving bias correction methodology designed for climate impact simulations

Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Kirsten, T.; Mahecha, M. D.

2016-12-01

Understanding and quantifying the impacts of extreme weather and climate events across various sectors is crucial for societal adaptation in a changing climate. However, regional climate model simulations generated for this purpose typically exhibit biases in their output that impede any straightforward assessments of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and their non-preservation of the multivariate correlation structure. Here, we first present a recently introduced resampling-based bias correction scheme designed for assessing the impacts of climate extremes that fully preserves the physical consistency and multivariate correlation structure of the model output. Second, the bias correction scheme is evaluated using large ensemble simulations generated with a regional climate model over Central Europe (HadRM3P, generated through climateprediction.net/weatherathome). The procedure is demonstrated to yield a substantial improvement in the representation of variability and extremes i) in individual climate variables such as summer heat or dryness, and ii) in multivariate heat-health related indicators such as the "wet-bulb globe temperature". Finally, we conduct a climate impact assessment in the terrestrial biosphere by simulating biosphere-atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL). The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of physical consistent bias correction schemes for climate impact simulations, particularly if these assessments focus on variability and extremes in climate or its associated impacts. Whilst the resampling-methodology currently requires relatively large ensembles of climate simulations, we present some ideas how the

8. Visual search attentional bias modification reduced social phobia in adolescents.

PubMed

De Voogd, E L; Wiers, R W; Prins, P J M; Salemink, E

2014-06-01

9. a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows

Molina, J. M.; Ramirez, J. A.; Raff, D. A.

2011-12-01

A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both

10. F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

NASA Technical Reports Server (NTRS)

Soeder, J. F.

1983-01-01

As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

11. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

PubMed

Fourcade, Yoan; Engler, Jan O; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

12. Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

PubMed Central

Fourcade, Yoan; Engler, Jan O.; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases. PMID:24818607

13. Selecting, weeding, and weighting biased climate model ensembles

Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.

2012-12-01

In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.

14. Types of Research Bias Encountered in IR.

PubMed

Gabr, Ahmed; Kallini, Joseph Ralph; Desai, Kush; Hickey, Ryan; Thornburg, Bartley; Kulik, Laura; Lewandowski, Robert J; Salem, Riad

2016-04-01

15. Effects of positive interpretive bias modification in highly anxious individuals.

PubMed

Salemink, Elske; van den Hout, Marcel; Kindt, Merel

2009-06-01

Over the past 20 years evidence has accumulated that individuals suffering from anxiety tend to interpret ambiguous information as threatening. Considering the causal role of this interpretive bias in anxiety, it was recently established that modifying interpretive biases influences anxiety. This suggests that anxiety can be clinically treated by directly targeting this interpretive bias. The present study was designed to modify a negative interpretive bias in highly anxious individuals, and subsequently assess the hypothesized beneficial effects on clinical measures. High trait-anxious participants were randomly assigned to one of two conditions: a positive interpretational Cognitive Bias Modification (CBM-I) or a control condition (n=2 x 17). The program was offered online for eight consecutive days. Upon completing the program, participants who had followed positive CBM-I were less state and trait-anxious compared to the control group. Additionally, positively trained participants scored lower on a measure of general psychopathology (SCL-90). No effects were observed on social anxiety and stress vulnerability. The mixed pattern of findings renders them rather inconclusive, leaving interpretations of the potential therapeutic merits of CBM-I open for future research.

16. Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

17. Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

18. Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

19. Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2016-10-17

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Sun, Bomin; Reale, Anthony; Schroeder, Steven; Seidel, Dian J.; Ballish, Bradley

2013-05-01

biases in global operational radiosonde temperature data from May 2008 to August 2011 are examined by using spatially and temporally collocated Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) data as estimates of the truth. The data on average from most radiosonde types show a nighttime cold bias and a daytime warm bias relative to COSMIC. Most daytime biases increase with altitude and solar elevation angle (SEA). The global average biases in the 15-70 hPa layer are -0.05 ± 1.89 K standard deviation (~52,000 profiles) at night and 0.39 ± 1.80 K standard deviation (~64,500 profiles) in daytime (SEA > 7.5°). Daytime warm biases associated with clouds are smaller than those under clear conditions. Newer sondes (post-2000) have smaller biases and appear to be less sensitive to effects of clouds. Biases at night show greater seasonal and zonal variations than those for daytime. In general, warm night biases are associated with warm climate regimes and less warm or cold night biases with cold climate regimes. Bias characteristics for 13 major radiosonde types are provided, as a basis for updating radiosonde corrections used in numerical weather predictions, for validating satellite retrievals, and for adjusting archived radiosonde data to create consistent climate records.

1. A Multivariate Approach for Comparing and Classifying Streamwater Quality

Hooper, R. P.; McGlynn, B. L.; Hjerdt, K. N.; McDonnell, J. J.

2001-05-01

Few measures exist for objectively comparing the chemistry of streams. We develop a multivariate technique, based on an eigenvalue analysis of streamwater concentrations, to facilitate comparison of water quality among sites across basin scales. A correlation matrix is constructed to include only solutes that mix conservatively. An eigenvalue analysis of this matrix is performed at each site to determine the approximate rank of the data set. If the ranks of all sites are roughly equal, one site is chosen as the reference site. The reduced set of eigenvectors from this site is chosen as the basis for a new, lower dimensional coordinate system and the data from the other sites are projected into this coordinate system. To assess the relative orientation of data from the reference site to all of the other sites, the relative bias (RB) and relative root mean square error (RRMSE) are calculated between the original and the projected points. The new technique was applied to multiple sites within three experimental watersheds to assess the consistency of water quality across the basin scale. The three watersheds were: Panola Mountain, Georgia, USA (6 solutes, 8 sites, 3 to 1000 ha); Sleepers River, Vermont, USA (5 solutes, 7 sites, 3 to 840 ha); and Maimai, South Island, New Zealand (4 solutes, 4 sites, 3 to 300 ha). Data from all sites were roughly planar with the first two eigenvectors explaining more than 90% of the variation. The RRMSEs for the reference site were generally between 5 and 10% with <0.1% RB. At Maimai, the RRMSE was roughly equivalent between the test sites and the 17-ha reference site, 5-8%; the RB was less than 4% at all sites. At Sleepers River, Ca and Mg had larger RRMSE at smaller basins relative to the 41 ha reference site; there was no consistent pattern to the RB for these solutes. Mg, Na, and SiO2 exhibited larger RRMSE (10-20%) and had substantial bias (10%, -20%, and 10%, respectively) at the 840-ha site compared with the 41-ha site. At

2. Variable-bias coin tossing

SciTech Connect

2006-03-15

Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT.

3. Variable-bias coin tossing

2006-03-01

Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT.

4. Belief bias and relational reasoning.

PubMed

Roberts, Maxwell J; Sykes, Elizabeth D A

2003-01-01

When people evaluate categorical syllogisms, they tend to reject unbelievable conclusions and accept believable ones irrespective of their validity. Typically, this effect is particularly marked for invalid conclusions that are possible, but do not necessarily follow, given the premises. However, smaller believability effects can also be detected for other types of conclusion. Three experiments are reported here, in which an attempt was made to determine whether belief bias effects can manifest themselves on the relational inference task. Subjects evaluated the validity of conclusions such as William the Conqueror was king after the Pyramids were built (temporal task) or Manchester is north of Bournemouth (spatial task) with respect to their premises. All of the major findings for equivalent categorical syllogism tasks were replicated. However, the overall size of the main effect of believability appears to be related to task presentation, a phenomenon not previously identified for categorical syllogisms and which current theories of belief bias have difficulty explaining.

5. Touch Precision Modulates Visual Bias.

PubMed

Misceo, Giovanni F; Jones, Maurice D

2017-08-29

The sensory precision hypothesis holds that different seen and felt cues about the size of an object resolve themselves in favor of the more reliable modality. To examine this precision hypothesis, 60 college students were asked to look at one size while manually exploring another unseen size either with their bare fingers or, to lessen the reliability of touch, with their fingers sleeved in rigid tubes. Afterwards, the participants estimated either the seen size or the felt size by finding a match from a visual display of various sizes. Results showed that the seen size biased the estimates of the felt size when the reliability of touch decreased. This finding supports the interaction between touch reliability and visual bias predicted by statistically optimal models of sensory integration.

6. Gender Bias in SAT Items.

ERIC Educational Resources Information Center

Loewen, James W.; And Others

Sex-related bias on the Scholastic Aptitude Test (SAT) was studied in a sample of 1,112 students in SAT coaching classes who took the SAT. Of these, 1,028 answered an additional questionnaire (Appendix A of this report) about high school grade point average, perceived abilities, and background. Almost all of the subjects were 11th graders (97.8%),…

7. Self regulating body bias generator

NASA Technical Reports Server (NTRS)

Hass, Kenneth (Inventor)

2004-01-01

The back bias voltage on a functional circuit is controlled through a closed loop process. A delay element receives a clock pulse and produces a delay output. The delay element is advantageously constructed of the same materials as the functional circuit so that the aging and degradation of the delay element parallels the degradation of the functional circuit. As the delay element degrades, the transistor switching time increases, increasing the time delay of the delay output. An AND gate compares a clock pulse to an output pulse of the delay element, the AND output forming a control pulse. A duty cycle of the control pulse is determined by the delay time between the clock pulse and the delay element output. The control pulse is received at the input of a charge pump. The charge pump produces a back bias voltage which is then applied to the delay element and to the functional circuit. If the time delay produced by the delay element exceeds the optimal delay, the duty cycle of the control pulse is shortened, and the back bias voltage is lowered, thereby increasing the switching speed of the transistors in the delay element and reducing the time delay. If the throughput of the delay element is too fast, the duty cycle of the control pulse is lengthened, raising the back bias voltage produced by the charge pump. This, in turn, lowers the switching speed of the transistors in both the delay element and the functional circuit. The slower switching speed in the delay element increases time delay. In this manner, the switching speed of the delay element, and of the functional circuit, is maintained at a constant level over the life of the circuit.

8. Quantile regression reveals hidden bias and uncertainty in habitat models

Treesearch

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

2005-01-01

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

9. Categorical Biases in Spatial Memory: The Role of Certainty

ERIC Educational Resources Information Center

Holden, Mark P.; Newcombe, Nora S.; Shipley, Thomas F.

2015-01-01

Memories for spatial locations often show systematic errors toward the central value of the surrounding region. The Category Adjustment (CA) model suggests that this bias is due to a Bayesian combination of categorical and metric information, which offers an optimal solution under conditions of uncertainty (Huttenlocher, Hedges, & Duncan,…

10. The Existence of Implicit Racial Bias in Nursing Faculty

ERIC Educational Resources Information Center

Fitzsimmons, Kathleen A.

2009-01-01

This study examined the existence of implicit racial bias in nursing faculty using the Implicit Association Test (IAT). It was conducted within a critical race theory framework where race was seen as a permanent, pervasive, and systemic condition, not an individual process. The study was fueled by data showing continued disparate academic and…

11. Categorical Biases in Spatial Memory: The Role of Certainty

ERIC Educational Resources Information Center

Holden, Mark P.; Newcombe, Nora S.; Shipley, Thomas F.

2015-01-01

Memories for spatial locations often show systematic errors toward the central value of the surrounding region. The Category Adjustment (CA) model suggests that this bias is due to a Bayesian combination of categorical and metric information, which offers an optimal solution under conditions of uncertainty (Huttenlocher, Hedges, & Duncan,…

12. Good practices for quantitative bias analysis.

PubMed

Lash, Timothy L; Fox, Matthew P; MacLehose, Richard F; Maldonado, George; McCandless, Lawrence C; Greenland, Sander

2014-12-01

Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them. Finally, by suggesting aspects that dominate uncertainty in a particular research result or topic area, bias analysis can guide efficient allocation of sparse research resources. The fundamental methods of bias analyses have been known for decades, and there have been calls for more widespread use for nearly as long. There was a time when some believed that bias analyses were rarely undertaken because the methods were not widely known and because automated computing tools were not readily available to implement the methods. These shortcomings have been largely resolved. We must, therefore, contemplate other barriers to implementation. One possibility is that practitioners avoid the analyses because they lack confidence in the practice of bias analysis. The purpose of this paper is therefore to describe what we view as good practices for applying quantitative bias analysis to epidemiological data, directed towards those familiar with the methods. We focus on answering questions often posed to those of us who advocate incorporation of bias analysis methods into teaching and research. These include the following. When is bias analysis practical and productive? How does one select the biases that ought to be addressed? How does one select a method to model biases? How does one assign values to the parameters of a bias model? How does one present and interpret a bias analysis?. We hope that our guide to good practices for conducting and presenting bias analyses will encourage

13. SEASAT altimeter timing bias estimation

Marsh, J. G.; Williamson, R. G.

1982-04-01

The calibration of the altimeter observation time tags to the millisecond level of accuracy is fundamental to the processing of the data. Initial analyses of the SEASAT altimeter data indicated the presence of a time calibration bias which produced altimeter measurement errors in excess of a meter. A technique has been developed for the solution of the time tag bias based upon the analysis of sea surface height discrepancies at ground track intersections. This technique has permitted very good separation of the dominant once per revolution ephemeris error, which amounts to about 1.5 m rms, from the timing error signature. Furthermore, the technique does not depend upon the availability of precise geoid data. The application of this technique to a global set of SEASAT altimeter data covering the time period of July 28-August 9, 1978, has resulted in a value of -81.0±2 ms for the time tag bias. This value agrees to within 2.9 ms of the value derived at the University of Texas from a similar analysis of the altimeter data. Furthermore, these values corroborate the revised value of -79.4 ms derived at NASA/Wallops Flight Center and the Johns Hopkins University/Applied Physics Lab from a reexamination of the internal instrument time delays. The modeling of oceanic tides and the orbit computations are the major error sources in these analyses.

14. Opinion dynamics with confirmation bias.

PubMed

Allahverdyan, Armen E; Galstyan, Aram

2014-01-01

Confirmation bias is the tendency to acquire or evaluate new information in a way that is consistent with one's preexisting beliefs. It is omnipresent in psychology, economics, and even scientific practices. Prior theoretical research of this phenomenon has mainly focused on its economic implications possibly missing its potential connections with broader notions of cognitive science. We formulate a (non-Bayesian) model for revising subjective probabilistic opinion of a confirmationally-biased agent in the light of a persuasive opinion. The revision rule ensures that the agent does not react to persuasion that is either far from his current opinion or coincides with it. We demonstrate that the model accounts for the basic phenomenology of the social judgment theory, and allows to study various phenomena such as cognitive dissonance and boomerang effect. The model also displays the order of presentation effect-when consecutively exposed to two opinions, the preference is given to the last opinion (recency) or the first opinion (primacy) -and relates recency to confirmation bias. Finally, we study the model in the case of repeated persuasion and analyze its convergence properties. The standard Bayesian approach to probabilistic opinion revision is inadequate for describing the observed phenomenology of persuasion process. The simple non-Bayesian model proposed here does agree with this phenomenology and is capable of reproducing a spectrum of effects observed in psychology: primacy-recency phenomenon, boomerang effect and cognitive dissonance. We point out several limitations of the model that should motivate its future development.

15. Generalization of the FRAM's Bias

SciTech Connect

Duc T. Vo

2005-10-01

The Fixed-Energy Response-Function Analysis with Multiple Efficiency (FRAM) code was developed at Los Alamos National Laboratory to measure the gamma-ray spectrometry of the isotopic composition of plutonium, uranium, and other actinides. Its reported uncertainties of the results come from the propagation of the statistics in the peak areas only. No systematic error components are included in the reported uncertainties. We have done several studies and found that the FRAM's statistical precision can be reasonably represented by its reported uncertainties. The FRAM's biases or systematic uncertainties can come from a variety of sources and can be difficult to determine. We carefully examined the FRAM analytical results of the archival plutonium data and of the data specifically acquired for this isotopic uncertainty analysis project and found the relationship between the bias and other parameters. We worked out the equations representing the biases of the measured isotopes from each measurement using the internal parameters in the spectrum such as peak resolution and shape, region of analysis, and burnup (for plutonium) or enrichment (for uranium).

16. Galaxy formation and physical bias

NASA Technical Reports Server (NTRS)

Cen, Renyue; Ostriker, Jeremiah P.

1992-01-01

We have supplemented our code, which computes the evolution of the physical state of a representative piece of the universe to include, not only the dynamics of dark matter (with a standard PM code), and the hydrodynamics of the gaseous component (including detailed collisional and radiative processes), but also galaxy formation on a heuristic but plausible basis. If, within a cell the gas is Jeans' unstable, collapsing, and cooling rapidly, it is transformed to galaxy subunits, which are then followed with a collisionless code. After grouping them into galaxies, we estimate the relative distributions of galaxies and dark matter and the relative velocities of galaxies and dark matter. In a large scale CDM run of 80/h Mpc size with 8 x 10 exp 6 cells and dark matter particles, we find that physical bias b is on the 8/h Mpc scale is about 1.6 and increases towards smaller scales, and that velocity bias is about 0.8 on the same scale. The comparable HDM simulation is highly biased with b = 2.7 on the 8/h Mpc scale. Implications of these results are discussed in the light of the COBE observations which provide an accurate normalization for the initial power spectrum. CDM can be ruled out on the basis of too large a predicted small scale velocity dispersion at greater than 95 percent confidence level.

17. Response bias in plaintiffs' histories.

PubMed

Lees-Haley, P R; Williams, C W; Zasler, N D; Marguilies, S; English, L T; Stevens, K B

1997-11-01

This study investigated response bias in self-reported history of factors relevant to the assessment of traumatic brain injury, toxic brain injury and related emotional distress. Response bias refers to systematic error in self-report data. A total of 446 subjects (comprising 131 litigating and 315 non-litigating adults from five locations in the United States) completed a symptom questionnaire. Data were obtained from university faculty and students, from patients in clinics specializing in physiatry neurology, and family medicine, and from plaintiffs undergoing forensic neuropsychological evaluations. Comparisons were made for litigant and non litigant ratings of their past and current cognitive and emotional functioning, including life in general, ability to concentrate, memory, depression, anxiety, alcohol, drugs, ability to work or attend school, irritability, headaches, confusion, self-esteem, and fatigue. Although there is no basis for hypothesizing plaintiffs to be healthier than the general population, plaintiffs rated their pre-injury functioning superior to non-plaintiffs. These findings suggest that response biases need to be taken into account by forensic examiners when relying on litigants' self-reports of pre-injury status.

18. Galaxy formation and physical bias

NASA Technical Reports Server (NTRS)

Cen, Renyue; Ostriker, Jeremiah P.

1992-01-01

We have supplemented our code, which computes the evolution of the physical state of a representative piece of the universe to include, not only the dynamics of dark matter (with a standard PM code), and the hydrodynamics of the gaseous component (including detailed collisional and radiative processes), but also galaxy formation on a heuristic but plausible basis. If, within a cell the gas is Jeans' unstable, collapsing, and cooling rapidly, it is transformed to galaxy subunits, which are then followed with a collisionless code. After grouping them into galaxies, we estimate the relative distributions of galaxies and dark matter and the relative velocities of galaxies and dark matter. In a large scale CDM run of 80/h Mpc size with 8 x 10 exp 6 cells and dark matter particles, we find that physical bias b is on the 8/h Mpc scale is about 1.6 and increases towards smaller scales, and that velocity bias is about 0.8 on the same scale. The comparable HDM simulation is highly biased with b = 2.7 on the 8/h Mpc scale. Implications of these results are discussed in the light of the COBE observations which provide an accurate normalization for the initial power spectrum. CDM can be ruled out on the basis of too large a predicted small scale velocity dispersion at greater than 95 percent confidence level.

19. Bias in Estimation of Misclassification Rates.

PubMed

Haberman, Shelby J

2006-06-01

When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the independent variable. In typical cases, this increased misclassification rate due to sampling is remarkably small relative to other increases in expected measures of prediction accuracy due to samplings that are typically encountered in statistical analysis.This issue is particularly striking if a polytomous variable predicts a polytomous variable, for the excess misclassification rate due to estimation approaches 0 at an exponential rate as n increases. Even with a continuous real predictor and with simple nonparametric methods, it is typically not difficult to achieve an excess misclassification rate on the order of n (-1). Although reduced excess error is normally desirable, it may reasonably be argued that, in the case of classification, the reduction in bias is related to a more fundamental lack of sensitivity of misclassification error to the quality of the prediction. This lack of sensitivity is not an issue if criteria based on probability prediction such as logarithmic penalty or least squares are employed, but the latter measures typically involve more substantial issues of bias. With polytomous predictors, excess expected errors due to sampling are typically of order n (-1). For a continuous real predictor, the increase in expected error is typically of order n (-2/3).

20. Compensator improvement for multivariable control systems

NASA Technical Reports Server (NTRS)

Mitchell, J. R.; Mcdaniel, W. L., Jr.; Gresham, L. L.

1977-01-01

A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.