#### 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. 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.

3. 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.

4. 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…

5. 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.

6. 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.

7. 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.

8. 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

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.

9. 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

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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

20. 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.

1. 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

2. 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.

3. 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.

4. 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.

5. 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

6. 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.

7. 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.

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. 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.

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. 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.

12. 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.

13. 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.

14. 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).

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

1. 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…

2. 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.

3. 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

4. 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.

5. 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.

6. 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.

7. 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.

8. 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

9. 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.

10. 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

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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…

20. 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.

1. 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.

2. 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.

3. 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

4. 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.

5. 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.

6. 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.

7. 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).

8. 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

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

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. Topics in Multivariate Approximation Theory.

DTIC Science & Technology

1982-05-01

include tensor products, multivariate polynomial interpolation , esp. Kergin Interpolation , and the recent developments of multivariate B-splines. t1...AMS (MOS) Subject Classifications: 41-02, 41A05, 41A10, 41A15, 41A63, 41A65 Key Words: multivariate, B-splines, Kergin interpolation , linear projectors...splines and in multivariate polynomial interpolation . These developments may well provide the theoretical foundation for efficient methods of

18. 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

19. 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.

20. 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.

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. 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.

3. 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…

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. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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'…

11. 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.

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…

13. 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

14. 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

15. 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.

16. 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.

17. 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.

18. 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.

19. 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

20. "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.

1. 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.

2. 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.

3. 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.

4. Adaptable history biases in human perceptual decisions

PubMed Central

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

2016-01-01

5. 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

6. 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.

7. 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.

8. 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…

9. 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…

10. Parameter Sensitivity in Multivariate Methods

ERIC Educational Resources Information Center

Green, Bert F., Jr.

1977-01-01

Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)

11. 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…

12. 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.

13. 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.

14. 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.

15. 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.

16. 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

17. 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.

18. 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.

19. 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

20. 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.

1. 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.

2. Multivariate Bioclimatic Ecosystem Change Approaches

DTIC Science & Technology

2015-02-06

Headquarters, US Army Corps of Engineers Washington, DC 20314-1000 ERDC/CERL TR-15-2 ii Abstract Changes in climatic parameters are important in that they... climatic changes on specific installations. To support this need, the research tested and evaluated the application of six multivariate approach...techniques to predict climatic changes on a specific Army installation, Fort Benning, GA. The six approaches were tested for their ability to identify

3. 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.

4. 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…

5. 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.

6. 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.

7. 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.

8. 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).

9. 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

10. 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

11. 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.

12. 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.

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. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

1. 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.

2. 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.

3. 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.…

4. 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

5. 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.

6. 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.

7. 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…

8. 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.

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. 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).

11. 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.

12. 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.

13. 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.

14. "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…

15. 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

16. 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.

17. 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.

18. 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,…

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 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.

1. 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

2. 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…

3. 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.

4. 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.

5. 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…

6. 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.

7. 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…

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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

14. 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.

15. 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

16. 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.

17. 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

18. 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.

19. 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.

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. 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

3. 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

4. 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.

5. 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.

6. 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.

7. 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.

8. 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

9. Multivariate Analysis and Its Applications.

DTIC Science & Technology

1983-12-01

traqsformation N satisfying some conditions. Such an inverse is called the L M N- inverse . Moore - Penrose inverse corresponds to the choice N=0. Some...infinity but p/n 0 y < 1. Technical Report #83-15 (July, 1983) Generalized Inverse of Linear Transformations: A Geometric Approach : I _ V...Haruo Yanai Generalized inverse of a linear transformation A: V-+W, where V and W are arbitrary finite dimensional vector spaces, is defined using

10. 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.

11. 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.

12. 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.

13. 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

14. 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.

15. 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

16. 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

17. 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.

18. 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

19. 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

20. 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

1. 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.

2. 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

3. 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

4. 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

5. 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

6. 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

7. Experimental Investigation of DC-Bias Related Core Losses in a Boost Inductor (Postprint)

DTIC Science & Technology

2014-08-01

dc bias-flux conditions. These dc bias conditions result in distorted hysteresis loops, increased core losses, and have been shown to be independent...core are proportional to the controllable converter load currents, this topology is ideal to study dc-related losses. Inductor core B-H hysteresis ...These dc bias conditions result in dis- torted hysteresis loops, increased core losses, and have been shown to be independent of core material. The

8. 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.

9. 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.

10. 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.

11. 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.

12. 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

13. 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.

14. 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.

15. 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.

16. 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.

17. 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).

18. 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.

19. 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

20. 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.

1. 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.

2. 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.

3. 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.

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. 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...

6. 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

7. Multivariate analysis: greater insights into complex systems

Technology Transfer Automated Retrieval System (TEKTRAN)

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 ...

8. 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.

9. 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.

10. 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

11. 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

12. 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.

13. 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.

14. 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

15. 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.

16. 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

17. 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.

18. 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.

19. 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.

20. 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.

1. 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.

2. 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.

3. 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

4. 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

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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

12. 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.

13. 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

14. 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.

15. 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

16. 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

17. 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.

18. 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.

19. 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.

20. 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.

1. 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…

2. 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,…

3. 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…

4. 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…

5. 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…

6. 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)

7. 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…

8. 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

9. 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

10. 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.

11. 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

12. 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.

13. 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.

14. 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.

15. 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…

16. 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…

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. 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…

19. 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…

20. 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…

1. 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)

2. 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.

3. 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.

4. 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.

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. 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.

7. 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…

8. 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…

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. 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. 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

14. 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.

15. 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.

16. 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.

17. The nondiscriminating heart: lovingkindness meditation training decreases implicit intergroup bias.

PubMed

Kang, Yoona; Gray, Jeremy R; Dovidio, John F

2014-06-01

Although meditation is increasingly accepted as having personal benefits, less is known about the broader impact of meditation on social and intergroup relations. We tested the effect of lovingkindness meditation training on improving implicit attitudes toward members of 2 stigmatized social outgroups: Blacks and homeless people. Healthy non-Black, nonhomeless adults (N = 101) were randomly assigned to 1 of 3 conditions: 6-week lovingkindness practice, 6-week lovingkindness discussion (a closely matched active control), or waitlist control. Decreases in implicit bias against stigmatized outgroups (as measured by Implicit Association Test) were observed only in the lovingkindness practice condition. Reduced psychological stress mediated the effect of lovingkindness practice on implicit bias against homeless people, but it did not mediate the reduced bias against Black people. These results suggest that lovingkindness meditation can improve automatically activated, implicit attitudes toward stigmatized social groups and that this effect occurs through distinctive mechanisms for different stigmatized social groups.

18. 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.

19. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

20. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

1. 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

Bias is a systemic error in studies that leads to inaccurate deductions. Relevant biases in the field of IR and interventional oncology were identified after reviewing articles published in the Journal of Vascular and Interventional Radiology and CardioVascular and Interventional Radiology. Biases cited in these articles were divided into three categories: preinterventional (health care access, participation, referral, and sample biases), periinterventional (contamination, investigator, and operator biases), and postinterventional (guarantee-time, lead time, loss to follow-up, recall, and reporting biases).

2. 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

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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

11. 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.

12. 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.

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.

14. Sampling bias in an internet treatment trial for depression.

PubMed

Donkin, L; Hickie, I B; Christensen, H; Naismith, S L; Neal, B; Cockayne, N L; Glozier, N

2012-10-23

Internet psychological interventions are efficacious and may reduce traditional access barriers. No studies have evaluated whether any sampling bias exists in these trials that may limit the translation of the results of these trials into real-world application. We identified 7999 potentially eligible trial participants from a community-based health cohort study and invited them to participate in a randomized controlled trial of an online cognitive behavioural therapy programme for people with depression. We compared those who consented to being assessed for trial inclusion with nonconsenters on demographic, clinical and behavioural indicators captured in the health study. Any potentially biasing factors were then assessed for their association with depression outcome among trial participants to evaluate the existence of sampling bias. Of the 35 health survey variables explored, only 4 were independently associated with higher likelihood of consenting-female sex (odds ratio (OR) 1.11, 95% confidence interval (CI) 1.05-1.19), speaking English at home (OR 1.48, 95% CI 1.15-1.90) higher education (OR 1.67, 95% CI 1.46-1.92) and a prior diagnosis of depression (OR 1.37, 95% CI 1.22-1.55). The multivariate model accounted for limited variance (C-statistic 0.6) in explaining participation. These four factors were not significantly associated with either the primary trial outcome measure or any differential impact by intervention arm. This demonstrates that, among eligible trial participants, few factors were associated with the consent to participate. There was no indication that such self-selection biased the trial results or would limit the generalizability and translation into a public or clinical setting.

15. 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

16. A Joint Modeling Approach for Right Censored High Dimensional Multivariate Longitudinal Data

PubMed Central

Jaffa, Miran A.; Gebregziabher, Mulugeta; Jaffa, Ayad A

2015-01-01

Analysis of multivariate longitudinal data becomes complicated when the outcomes are of high dimension and informative right censoring is prevailing. Here, we propose a likelihood based approach for high dimensional outcomes wherein we jointly model the censoring process along with the slopes of the multivariate outcomes in the same likelihood function. We utilized pseudo likelihood function to generate parameter estimates for the population slopes and Empirical Bayes estimates for the individual slopes. The proposed approach was applied to jointly model longitudinal measures of blood urea nitrogen, plasma creatinine, and estimated glomerular filtration rate which are key markers of kidney function in a cohort of renal transplant patients followed from kidney transplant to kidney failure. Feasibility of the proposed joint model for high dimensional multivariate outcomes was successfully demonstrated and its performance was compared to that of a pairwise bivariate model. Our simulation study results suggested that there was a significant reduction in bias and mean squared errors associated with the joint model compared to the pairwise bivariate model. PMID:25688330

17. 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.

18. Multivariable feedback active structural acoustic control using adaptive piezoelectric sensoriactuators.

PubMed

Vipperman, J S; Clark, R L

1999-01-01

An experimental implementation of a multivariable feedback active structural acoustic control system is demonstrated on a piezostructure plate with pinned boundary conditions. Four adaptive piezoelectric sensoriactuators provide an array of truly colocated actuator/sensor pairs to be used as control transducers. Radiation filters are developed based on the self- and mutual-radiation efficiencies of the structure and are included into the performance cost of an H2 control law which minimizes total radiated sound power. In the cost function, control effort is balanced with reductions in radiated sound power. A similarity transform which produces generalized velocity states that are required as inputs to the radiation filters is presented. Up to 15 dB of attenuation in radiated sound power was observed at the resonant frequencies of the piezostructure.

19. 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,…

20. 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…

1. 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.

2. 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.

3. Unsupervised classification of multivariate geostatistical data: Two algorithms

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

4. 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).

5. Estimation of attitude sensor timetag biases

Sedlak, J.

1995-05-01

This paper presents an extended Kalman filter for estimating attitude sensor timing errors. Spacecraft attitude is determined by finding the mean rotation from a set of reference vectors in inertial space to the corresponding observed vectors in the body frame. Any timing errors in the observations can lead to attitude errors if either the spacecraft is rotating or the reference vectors themselves vary with time. The state vector here consists of the attitude quaternion, timetag biases, and, optionally, gyro drift rate biases. The filter models the timetags as random walk processes: their expectation values propagate as constants and white noise contributes to their covariance. Thus, this filter is applicable to cases where the true timing errors are constant or slowly varying. The observability of the state vector is studied first through an examination of the algebraic observability condition and then through several examples with simulated star tracker timing errors. The examples use both simulated and actual flight data from the Extreme Ultraviolet Explorer (EUVE). The flight data come from times when EUVE had a constant rotation rate, while the simulated data feature large angle attitude maneuvers. The tests include cases with timetag errors on one or two sensors, both constant and time-varying, and with and without gyro bias errors. Due to EUVE's sensor geometry, the observability of the state vector is severely limited when the spacecraft rotation rate is constant. In the absence of attitude maneuvers, the state elements are highly correlated, and the state estimate is unreliable. The estimates are particularly sensitive to filter mistuning in this case. The EUVE geometry, though, is a degenerate case having coplanar sensors and rotation vector. Observability is much improved and the filter performs well when the rate is either varying or noncoplanar with the sensors, as during a slew. Even with bad geometry and constant rates, if gyro biases are

6. The Nonverbal Transmission of Intergroup Bias: A Model of Bias Contagion with Implications for Social Policy

PubMed Central

Weisbuch, Max; Pauker, Kristin

2013-01-01

Social and policy interventions over the last half-century have achieved laudable reductions in blatant discrimination. Yet members of devalued social groups continue to face subtle discrimination. In this article, we argue that decades of anti-discrimination interventions have failed to eliminate intergroup bias because such bias is contagious. We present a model of bias contagion in which intergroup bias is subtly communicated through nonverbal behavior. Exposure to such nonverbal bias “infects” observers with intergroup bias. The model we present details two means by which nonverbal bias can be expressed—either as a veridical index of intergroup bias or as a symptom of worry about appearing biased. Exposure to this nonverbal bias can increase perceivers’ own intergroup biases through processes of implicit learning, informational influence, and normative influence. We identify critical moderators that may interfere with these processes and consequently propose several social and educational interventions based on these moderators. PMID:23997812

7. Examining Event-Related Potential (ERP) Correlates of Decision Bias in Recognition Memory Judgments

PubMed Central

Hill, Holger; Windmann, Sabine

2014-01-01

Memory judgments can be based on accurate memory information or on decision bias (the tendency to report that an event is part of episodic memory when one is in fact unsure). Event related potentials (ERP) correlates are important research tools for elucidating the dynamics underlying memory judgments but so far have been established only for investigations of accurate old/new discrimination. To identify the ERP correlates of bias, and observe how these interact with ERP correlates of memory, we conducted three experiments that manipulated decision bias within participants via instructions during recognition memory tests while their ERPs were recorded. In Experiment 1, the bias manipulation was performed between blocks of trials (automatized bias) and compared to trial-by-trial shifts of bias in accord with an external cue (flexibly controlled bias). In Experiment 2, the bias manipulation was performed at two different levels of accurate old/new discrimination as the memory strength of old (studied) items was varied. In Experiment 3, the bias manipulation was added to another, bottom-up driven manipulation of bias induced via familiarity. In the first two Experiments, and in the low familiarity condition of Experiment 3, we found evidence of an early frontocentral ERP component at 320 ms poststimulus (the FN320) that was sensitive to the manipulation of bias via instruction, with more negative amplitudes indexing more liberal bias. By contrast, later during the trial (500–700 ms poststimulus), bias effects interacted with old/new effects across all three experiments. Results suggest that the decision criterion is typically activated early during recognition memory trials, and is integrated with retrieved memory signals and task-specific processing demands later during the trial. More generally, the findings demonstrate how ERPs can help to specify the dynamics of recognition memory processes under top-down and bottom-up controlled retrieval conditions. PMID

8. CONDITIONAL DISTANCE CORRELATION

PubMed Central

Wang, Xueqin; Pan, Wenliang; Hu, Wenhao; Tian, Yuan; Zhang, Heping

2015-01-01

Statistical inference on conditional dependence is essential in many fields including genetic association studies and graphical models. The classic measures focus on linear conditional correlations, and are incapable of characterizing non-linear conditional relationship including non-monotonic relationship. To overcome this limitation, we introduces a nonparametric measure of conditional dependence for multivariate random variables with arbitrary dimensions. Our measure possesses the necessary and intuitive properties as a correlation index. Briefly, it is zero almost surely if and only if two multivariate random variables are conditionally independent given a third random variable. More importantly, the sample version of this measure can be expressed elegantly as the root of a V or U-process with random kernels and has desirable theoretical properties. Based on the sample version, we propose a test for conditional independence, which is proven to be more powerful than some recently developed tests through our numerical simulations. The advantage of our test is even greater when the relationship between the multivariate random variables given the third random variable cannot be expressed in a linear or monotonic function of one random variable versus the other. We also show that the sample measure is consistent and weakly convergent, and the test statistic is asymptotically normal. By applying our test in a real data analysis, we are able to identify two conditionally associated gene expressions, which otherwise cannot be revealed. Thus, our measure of conditional dependence is not only an ideal concept, but also has important practical utility. PMID:26877569

9. [Potential selection bias in telephone surveys: landline and mobile phones].

PubMed

Garcia-Continente, Xavier; Pérez-Giménez, Anna; López, María José; Nebot, Manel

2014-01-01

The increasing use of mobile phones in the last decade has decreased landline telephone coverage in Spanish households. This study aimed to analyze sociodemographic characteristics and health indicators by type of telephone service (mobile phone vs. landline or landline and mobile phone). Two telephone surveys were conducted in Spanish samples (February 2010 and February 2011). Multivariate logistic regression analyses were performed to analyze differences in the main sociodemographic characteristics and health indicators according to the type of telephone service available in Spanish households. We obtained 2027 valid responses (1627 landline telephones and 400 mobile phones). Persons contacted through a mobile phone were more likely to be a foreigner, to belong to the manual social class, to have a lower educational level, and to be a smoker than those contacted through a landline telephone. The profile of the population that has only a mobile phone differs from that with a landline telephone. Therefore, telephone surveys that exclude mobile phones could show a selection bias.

10. The flyby anomaly: a multivariate analysis approach

Acedo, L.

2017-02-01

The flyby anomaly is the unexpected variation of the asymptotic post-encounter velocity of a spacecraft with respect to the pre-encounter velocity as it performs a slingshot manoeuvre. This effect has been detected in, at least, six flybys of the Earth but it has not appeared in other recent flybys. In order to find a pattern in these, apparently contradictory, data several phenomenological formulas have been proposed but all have failed to predict a new result in agreement with the observations. In this paper we use a multivariate dimensional analysis approach to propose a fitting of the data in terms of the local parameters at perigee, as it would occur if this anomaly comes from an unknown fifth force with latitude dependence. Under this assumption, we estimate the range of this force around 300 km.

11. MM Algorithms for Some Discrete Multivariate Distributions.

PubMed

Zhou, Hua; Lange, Kenneth

2010-09-01

The MM (minorization-maximization) principle is a versatile tool for constructing optimization algorithms. Every EM algorithm is an MM algorithm but not vice versa. This article derives MM algorithms for maximum likelihood estimation with discrete multivariate distributions such as the Dirichlet-multinomial and Connor-Mosimann distributions, the Neerchal-Morel distribution, the negative-multinomial distribution, certain distributions on partitions, and zero-truncated and zero-inflated distributions. These MM algorithms increase the likelihood at each iteration and reliably converge to the maximum from well-chosen initial values. Because they involve no matrix inversion, the algorithms are especially pertinent to high-dimensional problems. To illustrate the performance of the MM algorithms, we compare them to Newton's method on data used to classify handwritten digits.

12. Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

13. Shape Control in Multivariate Barycentric Rational Interpolation

Nguyen, Hoa Thang; Cuyt, Annie; Celis, Oliver Salazar

2010-09-01

The most stable formula for a rational interpolant for use on a finite interval is the barycentric form [1, 2]. A simple choice of the barycentric weights ensures the absence of (unwanted) poles on the real line [3]. In [4] we indicate that a more refined choice of the weights in barycentric rational interpolation can guarantee comonotonicity and coconvexity of the rational interpolant in addition to a polefree region of interest. In this presentation we generalize the above to the multivariate case. We use a product-like form of univariate barycentric rational interpolants and indicate how the location of the poles and the shape of the function can be controlled. This functionality is of importance in the construction of mathematical models that need to express a certain trend, such as in probability distributions, economics, population dynamics, tumor growth models etc.

14. 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.

15. Multivariate Markov chain modeling for stock markets

2003-06-01

We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.

16. Multivariable Harmonic Balance for Central Pattern Generators.

PubMed

Iwasaki, Tetsuya

2008-12-01

The central pattern generator (CPG) is a nonlinear oscillator formed by a group of neurons, providing a fundamental control mechanism underlying rhythmic movements in animal locomotion. We consider a class of CPGs modeled by a set of interconnected identical neurons. Based on the idea of multivariable harmonic balance, we show how the oscillation profile is related to the connectivity matrix that specifies the architecture and strengths of the interconnections. Specifically, the frequency, amplitudes, and phases are essentially encoded in terms of a pair of eigenvalue and eigenvector. This basic principle is used to estimate the oscillation profile of a given CPG model. Moreover, a systematic method is proposed for designing a CPG-based nonlinear oscillator that achieves a prescribed oscillation profile.

17. Regionalization in geology by multivariate classification

USGS Publications Warehouse

Harff, Jan; Davis, J.C.

1990-01-01

The concept of multivariate classification of "geological objects" can be combined with the concept of regionalized variables to yield a procedure for typification of geological objects, such as rock units, well records, or samples. Numerical classification is followed by subdivision of the area of investigation, and culminates in a regionalization or mapping of the classification onto the plane. Regions are subdivisions of the map area which are spatially contiguous and relatively homogeneous in their geological properties. The probability of correct classification of each point within a region as being part of that region can be assessed in terms of Bayesian probability as a space-dependent function. The procedure is applied to subsurface data from western Kansas. The geologic properties used are quantitative variables, and relationships are expressed by Mahalanobis' distances. These functions could be replaced by other metrics if qualitative or binary data derived from geological descriptions or appraisals were included in the analysis. ?? 1990 International Association for Mathematical Geology.

18. Design of feedforward controllers for multivariable plants

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

Simple methods for the design of feedforward controllers to achieve steady-state disturbance rejection and command tracking in stable multivariable plants are developed in this paper. The controllers are represented by simple and low-order transfer functions and are not based on reconstruction of the states of the commands and disturbances. For unstable plants, it is shown that the present method can be applied directly when an additional feedback controller is employed to stabilize the plant. The feedback and feedforward controllers do not affect each other and can be designed independently based on the open-loop plant to achieve stability, disturbance rejection and command tracking, respectivley. Numerical examples are given for illustration.

19. Multivariate analysis applied to tomato hybrid production.

PubMed

Balasch, S; Nuez, F; Palomares, G; Cuartero, J

1984-11-01

Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

20. Classification of adulterated honeys by multivariate analysis.

PubMed

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%).

1. Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Radakovich, Jon D.; daSilva, Arlindo; Todling, Ricardo; Verter, Frances

2006-01-01

In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

2. Exploration of new multivariate spectral calibration algorithms.

SciTech Connect

Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.

2004-03-01

A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.

3. Tailored multivariate analysis for modulated enhanced diffraction

DOE PAGES

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

4. Tailored multivariate analysis for modulated enhanced diffraction

SciTech Connect

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; Tutuncu, Goknur; Hanson, Jonathan C.

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

5. Outcome-Reporting Bias in Education Research

ERIC Educational Resources Information Center

Pigott, Therese D.; Valentine, Jeffrey C.; Polanin, Joshua R.; Williams, Ryan T.; Canada, Dericka D.

2013-01-01

Outcome-reporting bias occurs when primary studies do not include information about all outcomes measured in a study. When studies omit findings on important measures, efforts to synthesize the research using systematic review techniques will be biased and interpretations of individual studies will be incomplete. Outcome-reporting bias has been…

6. Ion Accelerator With Negatively Biased Decelerator Grid

NASA Technical Reports Server (NTRS)

Brophy, John R.

1994-01-01

Three-grid ion accelerator in which accelerator grid is biased at negative potential and decelerator grid downstream of accelerator grid biased at smaller negative potential. This grid and bias arrangement reduces frequency of impacts, upon accelerator grid, of charge-exchange ions produced downstream in collisions between accelerated ions and atoms and molecules of background gas. Sputter erosion of accelerator grid reduced.

7. Using Newspapers to Study Media Bias.

ERIC Educational Resources Information Center

Kirman, Joseph M.

1992-01-01

Suggests that students can learn to recognize media bias by studying media reports of current events or historical topics. Describes a study unit using media coverage of the second anniversary of the Palestinian uprising against Israel. Discusses lesson objectives, planning, defining bias teaching procedures, and criteria for determining bias. (DK)

8. Attentional Bias for Exercise-Related Images

ERIC Educational Resources Information Center

Berry, Tanya R.; Spence, John C.; Stolp, Sean M.

2011-01-01

This research examined attentional bias toward exercise-related images using a visual probe task. It was hypothesized that more-active participants would display attentional bias toward the exercise-related images. The results showed that men displayed attentional bias for the exercise images. There was a significant interaction of activity level…

9. Gender Bias: Recent Research and Interventions.

ERIC Educational Resources Information Center

New Jersey Research Bulletin, 1996

1996-01-01

This annotated bibliography lists 14 publications about recent research on gender bias and interventions to reduce gender bias in schools. The bibliography is divided into two sections: current research and intervention. The first includes descriptions of studies examining the following topics: gender bias in U.S. schools and its effects;…

10. Understanding Errors, Biases that Can Affect Journalists.

ERIC Educational Resources Information Center

Stocking, S. Holly; Gross, Paget H.

1989-01-01

Outlines some of the errors and biases in thinking that psychologists have documented in recent years, including the eyewitness fallacy, underutilization of statistics, confirmation bias, misperceptions of risk, sample errors and biases, and misunderstanding of regression. Argues that journalism educators need to bring these to the attention of…

11. Integrating Implicit Bias into Counselor Education

ERIC Educational Resources Information Center

Boysen, Guy A.

2010-01-01

The author reviews the empirical and theoretical literature on implicit bias as it relates to counselor education. Counselor educators can integrate implicit bias into the concepts of multicultural knowledge, awareness, and skill. Knowledge about implicit bias includes its theoretical explanation, measurement, and impact on counseling. Awareness…

12. Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes.

13. Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques

Sarhadi, Ali; Burn, Donald H.; Johnson, Fiona; Mehrotra, Raj; Sharma, Ashish

2016-05-01

Accurate projection of global warming on the probabilistic behavior of hydro-climate variables is one of the main challenges in climate change impact assessment studies. Due to the complexity of climate-associated processes, different sources of uncertainty influence the projected behavior of hydro-climate variables in regression-based statistical downscaling procedures. The current study presents a comprehensive methodology to improve the predictive power of the procedure to provide improved projections. It does this by minimizing the uncertainty sources arising from the high-dimensionality of atmospheric predictors, the complex and nonlinear relationships between hydro-climate predictands and atmospheric predictors, as well as the biases that exist in climate model simulations. To address the impact of the high dimensional feature spaces, a supervised nonlinear dimensionality reduction algorithm is presented that is able to capture the nonlinear variability among projectors through extracting a sequence of principal components that have maximal dependency with the target hydro-climate variables. Two soft-computing nonlinear machine-learning methods, Support Vector Regression (SVR) and Relevance Vector Machine (RVM), are engaged to capture the nonlinear relationships between predictand and atmospheric predictors. To correct the spatial and temporal biases over multiple time scales in the GCM predictands, the Multivariate Recursive Nesting Bias Correction (MRNBC) approach is used. The results demonstrate that this combined approach significantly improves the downscaling procedure in terms of precipitation projection.

14. Publication bias, with a focus on psychiatry: causes and solutions.

PubMed

Turner, Erick H

2013-06-01

Publication bias undermines the integrity of the evidence base by inflating apparent drug efficacy and minimizing drug harms, thus skewing the risk-benefit ratio. This paper reviews the topic of publication bias with a focus on drugs prescribed for psychiatric conditions, especially depression, schizophrenia, bipolar disorder, and autism. Publication bias is pervasive; although psychiatry/psychology may be the most seriously afflicted field, it occurs throughout medicine and science. Responsibility lies with various parties (authors as well as journals, academia as well as industry), so the motives appear to extend beyond the financial interests of drug companies. The desire for success, in combination with cognitive biases, can also influence academic authors and journals. Amid the flood of new medical information coming out each day, the attention of the news media and academic community is more likely to be captured by studies whose results are positive or newsworthy. In the peer review system, a fundamental flaw arises from the fact that authors usually write manuscripts after they know the results. This allows hindsight and other biases to come into play, so data can be "tortured until they confess" (a detailed example is given). If a "publishable" result cannot be achieved, non-publication remains an option. To address publication bias, various measures have been undertaken, including registries of clinical trials. Drug regulatory agencies can provide valuable unpublished data. It is suggested that journals borrow from the FDA review model. Because the significance of study results biases reviewers, results should be excluded from review until after a preliminary judgment of study scientific quality has been rendered, based on the original study protocol. Protocol publication can further enhance the credibility of the published literature.

15. Error Biases in Inner and Overt Speech: Evidence from Tongue Twisters

ERIC Educational Resources Information Center

Corley, Martin; Brocklehurst, Paul H.; Moat, H. Susannah

2011-01-01

To compare the properties of inner and overt speech, Oppenheim and Dell (2008) counted participants' self-reported speech errors when reciting tongue twisters either overtly or silently and found a bias toward substituting phonemes that resulted in words in both conditions, but a bias toward substituting similar phonemes only when speech was…

16. Fluid simulation of the bias effect in inductive/capacitive discharges

SciTech Connect

Zhang, Yu-Ru; Gao, Fei; Li, Xue-Chun; Wang, You-Nian; Bogaerts, Annemie

2015-11-15

Computer simulations are performed for an argon inductively coupled plasma (ICP) with a capacitive radio-frequency bias power, to investigate the bias effect on the discharge mode transition and on the plasma characteristics at various ICP currents, bias voltages, and bias frequencies. When the bias frequency is fixed at 13.56 MHz and the ICP current is low, e.g., 6 A, the spatiotemporal averaged plasma density increases monotonically with bias voltage, and the bias effect is already prominent at a bias voltage of 90 V. The maximum of the ionization rate moves toward the bottom electrode, which indicates clearly the discharge mode transition in inductive/capacitive discharges. At higher ICP currents, i.e., 11 and 13 A, the plasma density decreases first and then increases with bias voltage, due to the competing mechanisms between the ion acceleration power dissipation and the capacitive power deposition. At 11 A, the bias effect is still important, but it is noticeable only at higher bias voltages. At 13 A, the ionization rate is characterized by a maximum at the reactor center near the dielectric window at all selected bias voltages, which indicates that the ICP power, instead of the bias power, plays a dominant role under this condition, and no mode transition is observed. Indeed, the ratio of the bias power to the total power is lower than 0.4 over a wide range of bias voltages, i.e., 0–300 V. Besides the effect of ICP current, also the effect of various bias frequencies is investigated. It is found that the modulation of the bias power to the spatiotemporal distributions of the ionization rate at 2 MHz is strikingly different from the behavior observed at higher bias frequencies. Furthermore, the minimum of the plasma density appears at different bias voltages, i.e., 120 V at 2 MHz and 90 V at 27.12 MHz.

17. An experimental verification of laser-velocimeter sampling bias and its correction

NASA Technical Reports Server (NTRS)

Johnson, D. A.; Modarress, D.; Owen, F. K.

1982-01-01

The existence of 'sampling bias' in individual-realization laser velocimeter measurements is experimentally verified and shown to be independent of sample rate. The experiments were performed in a simple two-stream mixing shear flow with the standard for comparison being laser-velocimeter results obtained under continuous-wave conditions. It is also demonstrated that the errors resulting from sampling bias can be removed by a proper interpretation of the sampling statistics. In addition, data obtained in a shock-induced separated flow and in the near-wake of airfoils are presented, both bias-corrected and uncorrected, to illustrate the effects of sampling bias in the extreme.

18. Sex Bias in Classifying Borderline and Narcissistic Personality Disorder.

PubMed

Braamhorst, Wouter; Lobbestael, Jill; Emons, Wilco H M; Arntz, Arnoud; Witteman, Cilia L M; Bekker, Marrie H J

2015-10-01

This study investigated sex bias in the classification of borderline and narcissistic personality disorders. A sample of psychologists in training for a post-master degree (N = 180) read brief case histories (male or female version) and made DSM classification. To differentiate sex bias due to sex stereotyping or to base rate variation, we used different case histories, respectively: (1) non-ambiguous case histories with enough criteria of either borderline or narcissistic personality disorder to meet the threshold for classification, and (2) an ambiguous case with subthreshold features of both borderline and narcissistic personality disorder. Results showed significant differences due to sex of the patient in the ambiguous condition. Thus, when the diagnosis is not straightforward, as in the case of mixed subthreshold features, sex bias is present and is influenced by base-rate variation. These findings emphasize the need for caution in classifying personality disorders, especially borderline or narcissistic traits.

19. Some More Sensitive Measures of Sensitivity and Response Bias

NASA Technical Reports Server (NTRS)

Balakrishnan, J. D.

1998-01-01

In this article, the author proposes a new pair of sensitivity and response bias indices and compares them to other measures currently available, including d' and Beta of signal detection theory. Unlike d' and Beta, these new performance measures do not depend on specific distributional assumptions or assumptions about the transformation from stimulus information to a discrimination judgment with simulated and empirical data, the new sensitivity index is shown to be more accurate than d' and 16 other indices when these measures are used to compare the sensitivity levels of 2 experimental conditions. Results from a perceptual discrimination experiment demonstrate the feasibility of the new distribution-free bias index and suggest that biases of the type defined within the signal detection theory framework (i.e., the placement of a decision criterion) do not exist, even under an asymmetric payoff manipulation.

20. Bias temperature instability in tunnel field-effect transistors

Mizubayashi, Wataru; Mori, Takahiro; Fukuda, Koichi; Ishikawa, Yuki; Morita, Yukinori; Migita, Shinji; Ota, Hiroyuki; Liu, Yongxun; O’uchi, Shinichi; Tsukada, Junichi; Yamauchi, Hiromi; Matsukawa, Takashi; Masahara, Meishoku; Endo, Kazuhiko

2017-04-01

We systematically investigated the bias temperature instability (BTI) of tunnel field-effect transistors (TFETs). The positive BTI and negative BTI mechanisms in TFETs are the same as those in metal–oxide–semiconductor FETs (MOSFETs). In TFETs, although traps are generated in high-k gate dielectrics by the bias stress and/or the interface state is degraded at the interfacial layer/channel interface, the threshold voltage (V th) shift due to BTI degradation is caused by the traps and/or the degradation of the interface state locating the band-to-band tunneling (BTBT) region near the source/gate edge. The BTI lifetime in n- and p-type TFETs is improved by applying a drain bias corresponding to the operation conditions.

1. Opinion Dynamics with Confirmation Bias

PubMed Central

Allahverdyan, Armen E.; Galstyan, Aram

2014-01-01

Background 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. Methodology/Principal Findings 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. Conclusions 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. PMID:25007078

2. A multivariate joint hydrological drought indicator using vine copula

Liu, Zhiyong; Menzel, Lucas

2016-04-01

We present a multivariate joint hydrological drought indicator using the high-dimensional vine copula. This hydrological indicator is based on the concept of the standardized index (SI) (the version of this algorithm for streamflow is called the standardized streamflow index, simply the SSI). Unlike the single SSI n-month scales (e.g., SSI 1-month or 6-month), this indicator is done without focusing on a certain time window. This means that all different time windows from 1- to 12-months (i.e., the SSI-1 month, SSI 2-month, ..., SSI 12-month) are considered together when developing this hydrological drought indicator. Therefore, in this study, a 12-dimensional joint function is modeled to join the multivariate margins (the distribution functions of the SSI-1 month, SSI 2-month, ..., SSI 12-month) for all time windows based on the copula algorithm. We then used the C-vine copulas to construct the joint dependence of the multivariate margins with window sizes from 1-month to 12-months. To construct the C-vine copula, five bivariate copulas (i.e., Gaussian, Clayton, Frank, Gumbel, and Joe copulas) were considered as the potential pair-copulas (building blocks). Based on well-fitted marginal distributions, a 12-d C-vine copula was used to join the margins, model the joint dependence structure and generate this 12-variate hydrological indicator (named joint streamflow drought indicator, simply JSDI). We tested the performance of this indicator using two hydrological stations in Germany. The results indicate that the JSDI generally combines the strengths of the short-term drought index in capturing the drought onset and medium-term drought index in reflecting the drought duration or persistence. Therefore, it provides a more comprehensive assessment of drought and could be more competitive than other traditional hydrological drought indices (e.g., the SSI). This attractive feature is attributed to the fact that the JSDI describes the overall drought conditions based on

3. Charge amplifier with bias compensation

DOEpatents

Johnson, Gary W.

2002-01-01

An ion beam uniformity monitor for very low beam currents using a high-sensitivity charge amplifier with bias compensation. The ion beam monitor is used to assess the uniformity of a raster-scanned ion beam, such as used in an ion implanter, and utilizes four Faraday cups placed in the geometric corners of the target area. Current from each cup is integrated with respect to time, thus measuring accumulated dose, or charge, in Coulombs. By comparing the dose at each corner, a qualitative assessment of ion beam uniformity is made possible. With knowledge of the relative area of the Faraday cups, the ion flux and areal dose can also be obtained.

4. Squeezing the halo bispectrum: a test of bias models

2016-09-01

We study the halo-matter cross bispectrum in the presence of primordial non-Gaussianity of the local type. We restrict ourselves to the squeezed limit, for which the calculation are straightforward, and perform the measurements in the initial conditions of N-body simulations, to mitigate the contamination induced by nonlinear gravitational evolution. Interestingly, the halo-matter cross bispectrum is not trivial even in this simple limit as it is strongly sensitive to the scale-dependence of the quadratic and third-order halo bias. Therefore, it can be used to test biasing prescriptions. We consider three different prescription for halo clustering: excursion set peaks (ESP), local bias and a model in which the halo bias parameters are explicitly derived from a peak-background split. In all cases, the model parameters are fully constrained with statistics other than the cross bispectrum. We measure the cross bispectrum involving one halo fluctuation field and two mass overdensity fields for various halo masses and collapse redshifts. We find that the ESP is in reasonably good agreement with the numerical data, while the other alternatives we consider fail in various cases. This suggests that the scale-dependence of halo bias also is a crucial ingredient to the squeezed limit of the halo bispectrum.

5. Estimated time of arrival and debiasing the time saving bias.

PubMed

Eriksson, Gabriella; Patten, Christopher J D; Svenson, Ola; Eriksson, Lars

2015-01-01

The time saving bias predicts that the time saved when increasing speed from a high speed is overestimated, and underestimated when increasing speed from a slow speed. In a questionnaire, time saving judgements were investigated when information of estimated time to arrival was provided. In an active driving task, an alternative meter indicating the inverted speed was used to debias judgements. The simulated task was to first drive a distance at a given speed, and then drive the same distance again at the speed the driver judged was required to gain exactly 3 min in travel time compared with the first drive. A control group performed the same task with a speedometer and saved less than the targeted 3 min when increasing speed from a high speed, and more than 3 min when increasing from a low speed. Participants in the alternative meter condition were closer to the target. The two studies corroborate a time saving bias and show that biased intuitive judgements can be debiased by displaying the inverted speed. Practitioner Summary: Previous studies have shown a cognitive bias in judgements of the time saved by increasing speed. This simulator study aims to improve driver judgements by introducing a speedometer indicating the inverted speed in active driving. The results show that the bias can be reduced by presenting the inverted speed and this finding can be used when designing in-car information systems.

6. Spatial biases in number line bisection tasks are due to a cognitive illusion of length.

PubMed

Stöttinger, Elisabeth; Anderson, Britt; Danckert, James; Frühholz, Barbara; Wood, Guilherme

2012-07-01

Placing arrow heads (Judd Illusion) or numbers of different magnitude at the end of a line biases perception of the centre of the line. For the Judd Illusion, it is known that this bias depends on the method used: a deliberate (more perceptually based) marking of the centre with a pen is more subject to the illusion than are fast (more action-based) ballistic pointing movements made towards the centre. It has been suggested that the number bias also reflects a cognitive illusion of length. To test this assumption, we used two different response methods in line bisection tasks while lines were flanked by arrow heads or numbers of different magnitudes. For both conditions, we found that the more action-based response method showed less bias. Since the pattern of biases induced by flanking numbers and arrow heads are similar, we confirm that the spatial bias produced by numerical magnitude reflects a cognitive illusion of length.

7. Adolescent threat-related interpretive bias and its modification: the moderating role of regulatory control.

PubMed

Salemink, Elske; Wiers, Reinout W

2012-01-01

Dual process models describe psychopathology as the consequence of an imbalance between a fast, impulsive system and a regulatory control system and have recently been applied to anxiety disorders. The aim of the current study was to specifically examine the role of a regulatory control system in regulating 1) threat-related interpretive bias and 2) the effectiveness of interpretive bias training in adolescents. In total, 67 adolescents participated and followed either a positive Cognitive Bias Modification of Interpretation (CBM-I) training or a placebo-control condition. Results revealed that interpretive bias and the effectiveness of its modification depended on individual differences in regulatory control. That is, low levels of regulatory control in combination with high levels of state anxiety were associated with the strongest threat-related interpretive bias and those individuals benefited the most of the positive interpretation training. The current study provided empirical support for the role of dual processes in adolescent threat-related interpretive bias.

8. Challenges in bias correcting climate change simulations

Maraun, Douglas; Shepherd, Ted; Zappa, Giuseppe; Gutierrez, Jose; Widmann, Martin; Hagemann, Stefan; Richter, Ingo; Soares, Pedro; Mearns, Linda

2016-04-01

Biases in climate model simulations - if these are directly used as input for impact models - will introduce further biases in subsequent impact simulations. In response to this issue, so-called bias correction methods have been developed to post-process climate model output. These methods are now widely used and a crucial component in the generation of high resolution climate change projections. Bias correction is conceptually similar to model output statistics, which has been successfully used for several decades in numerical weather prediction. Yet in climate science, some authors outrightly dismiss any form of bias correction. Starting from this seeming contradiction, we highlight differences between the two contexts and infer consequences and limitations for the applicability of bias correction to climate change projections. We first show that cross validation approaches successfully used to evaluate weather forecasts are fundamentally insufficient to evaluate climate change bias correction. We further demonstrate that different types of model mismatches with observations require different solutions, and some may not sensibly be mitigated. In particular we consider the influence of large-scale circulation biases, biases in the persistence of weather regimes, and regional biases caused by an insufficient representation of the flow-topography interaction. We conclude with a list of recommendations and suggestions for future research to reduce, to post-process, and to cope with climate model biases.

9. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

PubMed Central

Li, Yanming; Zhu, Ji

2015-01-01

Summary We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functioning groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. PMID:25732839

10. Numeracy and framing bias in epilepsy.

PubMed

Choi, Hyunmi; Wong, John B; Mendiratta, Anil; Heiman, Gary A; Hamberger, Marla J

2011-01-01

Patients with epilepsy are frequently confronted with complex treatment decisions. Communicating treatment risks is often difficult because patients may have difficulty with basic statistical concepts (i.e., low numeracy) or might misconceive the statistical information based on the way information is presented, a phenomenon known as "framing bias." We assessed numeracy and framing bias in 95 adults with chronic epilepsy and explored cognitive correlates of framing bias. Compared with normal controls, patients with epilepsy had significantly poorer performance on the Numeracy scale (P=0.02), despite a higher level of education than normal controls (P<0.001). Compared with patients with higher numeracy, patients with lower numeracy were significantly more likely to exhibit framing bias. Abstract problem solving performance correlated with the degree of framing bias (r=0.631, P<0.0001), suggesting a relationship between aspects of executive functioning and framing bias. Poor numeracy and susceptibility framing bias place patients with epilepsy at risk for uninformed decisions.

11. Granger causality and information flow in multivariate processes.

PubMed

Blinowska, Katarzyna J; Kuś, Rafał; Kamiński, Maciej

2004-11-01

The multivariate versus bivariate measures of Granger causality were considered. Granger causality in the application to multivariate physiological time series has the meaning of the information flow between channels. It was shown by means of simulations and by the example of experimental electroencephalogram signals that bivariate estimates of directionality in case of mutually interdependent channels give erroneous results, therefore multivariate measures such as directed transfer function should be used for determination of the information flow.

12. Terror mismanagement: evidence that mortality salience exacerbates attentional bias in social anxiety.

PubMed

Finch, Emma C; Iverach, Lisa; Menzies, Ross G; Jones, Mark

2016-11-01

Death anxiety is a basic fear underlying a range of psychological conditions, and has been found to increase avoidance in social anxiety. Given that attentional bias is a core feature of social anxiety, the aim of the present study was to examine the impact of mortality salience (MS) on attentional bias in social anxiety. Participants were 36 socially anxious and 37 non-socially anxious individuals, randomly allocated to a MS or control condition. An eye-tracking procedure assessed initial bias towards, and late-stage avoidance of, socially threatening facial expressions. As predicted, socially anxious participants in the MS condition demonstrated significantly more initial bias to social threat than non-socially anxious participants in the MS condition and socially anxious participants in the control condition. However, this effect was not found for late-stage avoidance of social threat. These findings suggest that reminders of death may heighten initial vigilance towards social threat.

13. A Multivariate Analysis of Extratropical Cyclone Environmental Sensitivity

Tierney, G.; Posselt, D. J.; Booth, J. F.

2015-12-01

The implications of a changing climate system include more than a simple temperature increase. A changing climate also modifies atmospheric conditions responsible for shaping the genesis and evolution of atmospheric circulations. In the mid-latitudes, the effects of climate change on extratropical cyclones (ETCs) can be expressed through changes in bulk temperature, horizontal and vertical temperature gradients (leading to changes in mean state winds) as well as atmospheric moisture content. Understanding how these changes impact ETC evolution and dynamics will help to inform climate mitigation and adaptation strategies, and allow for better informed weather emergency planning. However, our understanding is complicated by the complex interplay between a variety of environmental influences, and their potentially opposing effects on extratropical cyclone strength. Attempting to untangle competing influences from a theoretical or observational standpoint is complicated by nonlinear responses to environmental perturbations and a lack of data. As such, numerical models can serve as a useful tool for examining this complex issue. We present results from an analysis framework that combines the computational power of idealized modeling with the statistical robustness of multivariate sensitivity analysis. We first establish control variables, such as baroclinicity, bulk temperature, and moisture content, and specify a range of values that simulate possible changes in a future climate. The Weather Research and Forecasting (WRF) model serves as the link between changes in climate state and ETC relevant outcomes. A diverse set of output metrics (e.g., sea level pressure, average precipitation rates, eddy kinetic energy, and latent heat release) facilitates examination of storm dynamics, thermodynamic properties, and hydrologic cycles. Exploration of the multivariate sensitivity of ETCs to changes in control parameters space is performed via an ensemble of WRF runs coupled with

14. [Bias in observational research: 'confounding'].

PubMed

Groenwold, Rolf H H

2012-01-01

Confounding is an important and common issue in observational (non-randomized) research on the effects of pharmaceuticals or exposure to etiologic factors (determinants). Confounding is present when a third factor, related to both the determinant and the outcome, distorts the causal relation between these two. There are different methods to control for confounding. The most commonly used are restriction, stratification, multivariable regression models, and propensity score methods. With these methods it is only possible to control for variables for which data is known: measured confounders. Research in the area of confounding is currently directed at the incorporation of external knowledge on unmeasured confounders, the evaluation of instrumental variables, and the impact of time-dependent confounding.

15. Motion Direction Biases and Decoding in Human Visual Cortex

PubMed Central

Wang, Helena X.; Merriam, Elisha P.; Freeman, Jeremy

2014-01-01

Functional magnetic resonance imaging (fMRI) studies have relied on multivariate analysis methods to decode visual motion direction from measurements of cortical activity. Above-chance decoding has been commonly used to infer the motion-selective response properties of the underlying neural populations. Moreover, patterns of reliable response biases across voxels that underlie decoding have been interpreted to reflect maps of functional architecture. Using fMRI, we identified a direction-selective response bias in human visual cortex that: (1) predicted motion-decoding accuracy; (2) depended on the shape of the stimulus aperture rather than the absolute direction of motion, such that response amplitudes gradually decreased with distance from the stimulus aperture edge corresponding to motion origin; and 3) was present in V1, V2, V3, but not evident in MT+, explaining the higher motion-decoding accuracies reported previously in early visual cortex. These results demonstrate that fMRI-based motion decoding has little or no dependence on the underlying functional organization of motion selectivity. PMID:25209297

16. Bias correction of the CCSM4 for improved regional climate modeling of the North American monsoon

Meyer, Jonathan D. D.; Jin, Jiming

2016-05-01

This study investigates how a form of bias correction using linear regression improves the limitations of the community climate system model (CCSM) version 4 when it is dynamically downscaled with the Weather Research and Forecasting (WRF) model for the North American monsoon (NAM). Long-term biases in the CCSM dataset were removed using the climate forecast system reanalysis (CFSR) dataset as a baseline, from which a physically consistent set of bias-corrected variables were created. To quantitatively identify the effects of CCSM data on the NAM simulations, three 32-year climatologies were generated with WRF driven by (1) CFSR, (2) original CCSM, and (3) bias-corrected CCSM data. The WRF-CFSR simulations serve as a baseline for comparison. With the bias correction, onset dates simulated by WRF bias-corrected CCSM data were generally within a week of the WRF-CFSR climatology, while WRF using the original CCSM data occur up to 3-4 weeks too early over the core of the NAM. Additionally, bias-correction led to improvements in the mature phase of the NAM, reducing August root-mean-square-error values by 26 % over the core of the NAM and 36 % over the northern periphery. Comparison of the CFSR and the bias-corrected CCSM climatologies showed marked consistency in the general evolution of the NAM system. Dry biases in the NAM precipitation existed in each climatology with the original CCSM performing the poorest when compared to observations. The poor performance of the original CCSM simulations stem from biases in the thermodynamic profile supplied to the model through lateral boundary conditions. Bias-correction improved the excessive capping inversions, and mid-level mixing ratio dry biases (2-3 g kg-1) present in the CCSM simulations. Improvements in the bias-corrected CCSM data resulted in greater convective activity and a more representative seasonal distribution of precipitation.

17. A multivariate exploration of basic symptoms.

PubMed

Rubino, I Alex; Ciani, Nicola

2002-01-01

Little is known about the relationship between the different categories of basic symptoms (BS). Researchers of the Bonn School have accurately described the progression from second-level BS (relatively characteristic BS) to first-rank Schneiderian symptoms. Using a multiple regression model, the present study tried to investigate which kind of dynamic deficiencies (DDs; uncharacteristic first-level BS) mostly lead to each type of second-level BS. A group of 108 patients with a DSM-III-R diagnosis of schizophrenia completed an inventory on BS, with all items in strict accordance with those of the Bonn Scale. Five dependent variables (cognitive thought disorders, cognitive perception disorders, cognitive action disorders, increased impressionability, cenesthesias) and four independent variables (DDs with direct negative symptoms, DDs with indirect negative symptoms, affective DDs, relational DDs) were considered. Among the significant findings, a widespread contribution of DDs with indirect negative symptoms to most of the dependent variables, and the special role of DDs with direct negative symptoms as a predictor of cognitive thought disorders, must be emphasized. Suggestions for further multivariate studies in the field of BS are presented.

18. The multivariate statistical structure of DRASTIC model

Pacheco, Fernando A. L.; Sanches Fernandes, Luís F.

2013-01-01

SummaryAn assessment of aquifer intrinsic vulnerability was conducted in the Sordo river basin, a small watershed located in the Northeast of Portugal that drains to a lake used as public resource of drinking water. The method adopted to calculate intrinsic vulnerability was the DRASTIC model, which hinges on a weighted addition of seven hydrogeologic features, but was combined with a pioneering approach for feature reduction and adjustment of feature weights to local settings, based on a multivariate statistical method. Basically, with the adopted statistical technique-Correspondence Analysis-one identified and minimized redundancy between DRASTIC features, allowing for the calculation of a composite index based on just three of them: topography, recharge and aquifer material. The combined algorithm was coined vector-DRASTIC and proved to describe more realistically intrinsic vulnerability than DRASTC. The proof resulted from a validation of DRASTIC and vector-DRASTIC by the results of a groundwater pollution risk assessment standing on the spatial distribution of land uses and nitrate concentrations in groundwater, referred to as [NO3-]-DRASTIC method. Vector-DRASTIC and [NO3-]-DRASTIC portray the Sordo river basin as an environment with a self-capability to neutralize contaminants, preventing its propagation downstream. This observation was confirmed by long-standing low nitrate concentrations in the lake water and constitutes additional validation of vector-DRASTIC results. Nevertheless, some general recommendations are proposed in regard to agriculture management practices for water quality protection, as part of an overall watershed approach.

19. A Gibbs sampler for multivariate linear regression

2016-04-01

Kelly described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modelled by a flexible mixture of Gaussians rather than assumed to be uniform. Here, I extend the Kelly algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Secondly, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.

20. Multivariate volume visualization through dynamic projections

SciTech Connect

Liu, Shusen; Wang, Bei; Thiagarajan, Jayaraman J.; Bremer, Peer -Timo; Pascucci, Valerio

2014-11-01

We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.

1. Multivariate sensitivity to voice during auditory categorization.

PubMed

Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard

2015-09-01

Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex.

2. Apparatus and system for multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2003-06-24

An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of 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 by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

3. Reporting bias in medical research - a narrative review

PubMed Central

2010-01-01

Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles. We identified reporting bias in 40 indications comprising around 50 different pharmacological, surgical (e.g. vacuum-assisted closure therapy), diagnostic (e.g. ultrasound), and preventive (e.g. cancer vaccines) interventions. Regarding pharmacological interventions, cases of reporting bias were, for example, identified in the treatment of the following conditions: depression, bipolar disorder, schizophrenia, anxiety disorder, attention-deficit hyperactivity disorder, Alzheimer's disease, pain, migraine, cardiovascular disease, gastric ulcers, irritable bowel syndrome, urinary incontinence, atopic dermatitis, diabetes mellitus type 2, hypercholesterolaemia, thyroid disorders, menopausal symptoms, various types of cancer (e.g. ovarian cancer and melanoma), various types of infections (e.g. HIV, influenza and Hepatitis B), and acute trauma. Many cases involved the withholding of study data by manufacturers and regulatory agencies or the active attempt by manufacturers to suppress publication. The ascertained effects of reporting bias included the overestimation of

4. An intelligent system for multivariate statistical process monitoring and diagnosis.

PubMed

Tatara, Eric; Cinar, Ali

2002-04-01

A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.

5. Multivariate analysis of gamma spectra to characterize used nuclear fuel

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-04-01

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. This approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.

6. Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

7. Biased random walks on Kleinberg's spatial networks

Pan, Gui-Jun; Niu, Rui-Wu

2016-12-01

We investigate the problem of the particle or message that travels as a biased random walk toward a target node in Kleinberg's spatial network which is built from a d-dimensional (d = 2) regular lattice improved by adding long-range shortcuts with probability P(rij) ∼rij-α, where rij is the lattice distance between sites i and j, and α is a variable exponent. Bias is represented as a probability p of the packet to travel at every hop toward the node which has the smallest Manhattan distance to the target node. We study the mean first passage time (MFPT) for different exponent α and the scaling of the MFPT with the size of the network L. We find that there exists a threshold probability pth ≈ 0.5, for p ≥pth the optimal transportation condition is obtained with an optimal transport exponent αop = d, while for 0 < p pth, and increases with L less than a power law and get close to logarithmical law for 0 < p

8. Asymmetric divertor biasing in MAST

Helander, P.; Cohen, R.; Counsell, G. C.; Ryutov, D. D.

2002-11-01

Experiments are being carried out on the Mega-Ampere Spherical Tokamak (MAST) where the divertor tiles are electrically biased in a toroidally alternating way. The aim is to induce convective cells in the divertor plasma, broaden the SOL and reduce the divertor heat load. This paper describes the underlying theory and experimental results. Criteria are presented for achieving strong broadening and exciting shear-flow turbulence in the SOL, and properties of the expected turbulence are derived. It is also shown that magnetic shear near the X-point is likely to confine the potential perturbations to the divertor region, leaving the part of the SOL that is in direct contact with the core plasma intact. Preliminary comparison of the theory with MAST data is encouraging: the distortion of the heat deposition pattern, its broadening, and the incremental heat load are qualitatively in agreement; quantitative comparisons are underway.

9. Reexamining our bias against heuristics.

PubMed

McLaughlin, Kevin; Eva, Kevin W; Norman, Geoff R

2014-08-01

Using heuristics offers several cognitive advantages, such as increased speed and reduced effort when making decisions, in addition to allowing us to make decision in situations where missing data do not allow for formal reasoning. But the traditional view of heuristics is that they trade accuracy for efficiency. Here the authors discuss sources of bias in the literature implicating the use of heuristics in diagnostic error and highlight the fact that there are also data suggesting that under certain circumstances using heuristics may lead to better decisions that formal analysis. They suggest that diagnostic error is frequently misattributed to the use of heuristics and propose an alternative view whereby content knowledge is the root cause of diagnostic performance and heuristics lie on the causal pathway between knowledge and diagnostic error or success.

10. Social reward shapes attentional biases.

PubMed

Anderson, Brian A

2016-01-01

Paying attention to stimuli that predict a reward outcome is important for an organism to survive and thrive. When visual stimuli are associated with tangible, extrinsic rewards such as money or food, these stimuli acquire high attentional priority and come to automatically capture attention. In humans and other primates, however, many behaviors are not motivated directly by such extrinsic rewards, but rather by the social feedback that results from performing those behaviors. In the present study, I examine whether positive social feedback can similarly influence attentional bias. The results show that stimuli previously associated with a high probability of positive social feedback elicit value-driven attentional capture, much like stimuli associated with extrinsic rewards. Unlike with extrinsic rewards, however, such stimuli also influence task-specific motivation. My findings offer a potential mechanism by which social reward shapes the information that we prioritize when perceiving the world around us.

11. Relationship between Multiple Regression and Selected Multivariable Methods.

ERIC Educational Resources Information Center

Schumacker, Randall E.

The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…

12. Evaluating Univariate, Bivariate, and Multivariate Normality Using Graphical Procedures.

ERIC Educational Resources Information Center

Burdenski, Thomas K., Jr.

This paper reviews graphical and nongraphical procedures for evaluating multivariate normality by guiding the reader through univariate and bivariate procedures that are necessary, but insufficient, indications of a multivariate normal distribution. A data set using three dependent variables for two groups provided by D. George and P. Mallery…

13. Exploratory Tobit Factor Analysis for Multivariate Censored Data.

ERIC Educational Resources Information Center

Kamakura, Wagner A.; Wedel, Michel

2001-01-01

Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with…

14. Multivariate Seismic Calibration for the Novaya Zemlya Test Site

DTIC Science & Technology

1992-09-30

every multivariate magnitude combination. A classical confidence interval is presented to estimate future yields, based on estimates of the unknown...multivariate calibration parameters. A test of TTBT compliance and a definition of the F-number, based on the confidence interval , are also provided. F

15. Exploratory Multivariate Analysis of Variance: Contrasts and Variables.

ERIC Educational Resources Information Center

Barcikowski, Robert S.; Elliott, Ronald S.

The contribution of individual variables to overall multivariate significance in a multivariate analysis of variance (MANOVA) is investigated using a combination of canonical discriminant analysis and Roy-Bose simultaneous confidence intervals. Difficulties with this procedure are discussed, and its advantages are illustrated using examples based…

16. Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision.

17. Multivariate Display for Quipus to Faces. Program Statistics Research.

ERIC Educational Resources Information Center

Wainer, Howard

The past decade has seen a substantial growth in methods and schemes for the display of multivariate data. This paper encompasses a sketch of the history of multivariate displays, from the pre-Columbian Quipu to Chernoff's Face; examines a number of techniques; describes their construction; illustrates their use; and comments on their efficacy.…

18. Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

19. Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

ERIC Educational Resources Information Center

Ruscio, John; Kaczetow, Walter

2008-01-01

Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

20. Interventions That Affect Gender Bias in Hiring: A Systematic Review

PubMed Central

Isaac, Carol; Lee, Barbara; Carnes, Molly

2015-01-01

Purpose To systematically review experimental evidence for interventions mitigating gender bias in employment. Unconscious endorsement of gender stereotypes can undermine academic medicine's commitment to gender equity. Method The authors performed electronic and hand searches for randomized controlled studies since 1973 of interventions that affect gender differences in evaluation of job applicants. Twenty-seven studies met all inclusion criteria. Interventions fell into three categories: application information, applicant features, and rating conditions. Results The studies identified gender bias as the difference in ratings or perceptions of men and women with identical qualifications. Studies reaffirmed negative bias against women being evaluated for positions traditionally or predominantly held by men (male sex-typed jobs). The assessments of male and female raters rarely differed. Interventions that provided raters with clear evidence of job-relevant competencies were effective. However, clearly competent women were rated lower than equivalent men for male sex-typed jobs unless evidence of communal qualities was also provided. A commitment to the value of credentials before review of applicants and women's presence at above 25% of the applicant pool eliminated bias against women. Two studies found unconscious resistance to “antibias” training, which could be overcome with distraction or an intervening task. Explicit employment equity policies and an attractive appearance benefited men more than women, whereas repeated employment gaps were more detrimental to men. Masculine-scented perfume favored the hiring of both sexes. Negative bias occurred against women who expressed anger or who were perceived as self-promoting. Conclusions High-level evidence exists for strategies to mitigate gender bias in hiring. PMID:19881440

1. On evolutionary explanations of cognitive biases.

PubMed

Marshall, James A R; Trimmer, Pete C; Houston, Alasdair I; McNamara, John M

2013-08-01

Apparently irrational biases such as overconfidence, optimism, and pessimism are increasingly studied by biologists, psychologists, and neuroscientists. Functional explanations of such phenomena are essential; we argue that recent proposals, focused on benefits from overestimating the probability of success in conflicts or practising self-deception to better deceive others, are still lacking in crucial regards. Attention must be paid to the difference between cognitive and outcome biases; outcome biases are suboptimal, yet cognitive biases can be optimal. However, given that cognitive biases are subjectively experienced by affected individuals, developing theory and collecting evidence on them poses challenges. An evolutionary theory of cognitive bias might require closer integration of function and mechanism, analysing the evolution of constraints imposed by the mechanisms that determine behaviour.

2. Professional Culture and Climate: Addressing Unconscious Bias

Knezek, Patricia

2016-10-01

Unconscious bias reflects expectations or stereotypes that influence our judgments of others (regardless of our own group). Everyone has unconscious biases. The end result of unconscious bias can be an accumulation of advantage or disadvantage that impacts the long term career success of individuals, depending on which biases they are subject to. In order to foster a professional culture and climate, being aware of these unconscious biases and mitigating against them is a first step. This is particularly important when judgements are needed, such as in cases for recruitment, choice of speakers for conferences, and even reviewing papers submitted for publication. This presentation will cover how unconscious bias manifests itself, what evidence exists to demonstrate it exists, and ways it can be addressed.

3. Symmetry as Bias: Rediscovering Special Relativity

NASA Technical Reports Server (NTRS)

Lowry, Michael R.

1992-01-01

This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

4. Publication Bias in Methodological Computational Research

PubMed Central

Boulesteix, Anne-Laure; Stierle, Veronika; Hapfelmeier, Alexander

2015-01-01

The problem of publication bias has long been discussed in research fields such as medicine. There is a consensus that publication bias is a reality and that solutions should be found to reduce it. In methodological computational research, including cancer informatics, publication bias may also be at work. The publication of negative research findings is certainly also a relevant issue, but has attracted very little attention to date. The present paper aims at providing a new formal framework to describe the notion of publication bias in the context of methodological computational research, facilitate and stimulate discussions on this topic, and increase awareness in the scientific community. We report an exemplary pilot study that aims at gaining experiences with the collection and analysis of information on unpublished research efforts with respect to publication bias, and we outline the encountered problems. Based on these experiences, we try to formalize the notion of publication bias. PMID:26508827

5. Bioharness™ Multivariable Monitoring Device: Part. II: Reliability

PubMed Central

Johnstone, James A.; Ford, Paul A.; Hughes, Gerwyn; Watson, Tim; Garrett, Andrew T.

2012-01-01

The Bioharness™ monitoring system may provide physiological information on human performance but the reliability of this data is fundamental for confidence in the equipment being used. The objective of this study was to assess the reliability of each of the 5 Bioharness™ variables using a treadmill based protocol. 10 healthy males participated. A between and within subject design to assess the reliability of Heart rate (HR), Breathing Frequency (BF), Accelerometry (ACC) and Infra-red skin temperature (ST) was completed via a repeated, discontinuous, incremental treadmill protocol. Posture (P) was assessed by a tilt table, moved through 160°. Between subject data reported low Coefficient of Variation (CV) and strong correlations(r) for ACC and P (CV< 7.6; r = 0.99, p < 0.01). In contrast, HR and BF (CV~19.4; r~0.70, p < 0.01) and ST (CV 3.7; r = 0.61, p < 0.01), present more variable data. Intra and inter device data presented strong relationships (r > 0.89, p < 0.01) and low CV (<10.1) for HR, ACC, P and ST. BF produced weaker relationships (r < 0.72) and higher CV (<17.4). In comparison to the other variables BF variable consistently presents less reliability. Global results suggest that the Bioharness™ is a reliable multivariable monitoring device during laboratory testing within the limits presented. Key pointsHeart rate and breathing frequency data increased in variance at higher velocities (i.e. ≥ 10 km.h-1)In comparison to the between subject testing, the intra and inter reliability presented good reliability in data suggesting placement or position of device relative to performer could be important for data collectionUnderstanding a devices variability in measurement is important before it can be used within an exercise testing or monitoring setting PMID:24149347

6. Gravitational-wave detection using multivariate analysis

Adams, Thomas S.; Meacher, Duncan; Clark, James; Sutton, Patrick J.; Jones, Gareth; Minot, Ariana

2013-09-01

Searches for gravitational-wave bursts (transient signals, typically of unknown waveform) require identification of weak signals in background detector noise. The sensitivity of such searches is often critically limited by non-Gaussian noise fluctuations that are difficult to distinguish from real signals, posing a key problem for transient gravitational-wave astronomy. Current noise rejection tests are based on the analysis of a relatively small number of measured properties of the candidate signal, typically correlations between detectors. Multivariate analysis (MVA) techniques probe the full space of measured properties of events in an attempt to maximize the power to accurately classify events as signal or background. This is done by taking samples of known background events and (simulated) signal events to train the MVA classifier, which can then be applied to classify events of unknown type. We apply the boosted decision tree (BDT) MVA technique to the problem of detecting gravitational-wave bursts associated with gamma-ray bursts. We find that BDTs are able to increase the sensitive distance reach of the search by as much as 50%, corresponding to a factor of ˜3 increase in sensitive volume. This improvement is robust against trigger sky position, large sky localization error, poor data quality, and the simulated signal waveforms that are used. Critically, we find that the BDT analysis is able to detect signals that have different morphologies from those used in the classifier training and that this improvement extends to false alarm probabilities beyond the 3σ significance level. These findings indicate that MVA techniques may be used for the robust detection of gravitational-wave bursts with a priori unknown waveform.

7. Cross-Modal Multivariate Pattern Analysis

PubMed Central

Meyer, Kaspar; Kaplan, Jonas T.

2011-01-01

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

8. Multivariate statistical analysis of wildfires in Portugal

Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

2013-04-01

Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

9. Cognitive Biases and Nonverbal Cue Availability in Detecting Deception

ERIC Educational Resources Information Center

Burgoon, Judee K.; Blair, J. Pete; Strom, Renee E.

2008-01-01

In potentially deceptive situations, people rely on mental shortcuts to help process information. These heuristic judgments are often biased and result in inaccurate assessments of sender veracity. Four such biases--truth bias, visual bias, demeanor bias, and expectancy violation bias--were examined in a judgment experiment that varied nonverbal…

10. When Do Children Exhibit a "Yes" Bias?

ERIC Educational Resources Information Center

Okanda, Mako; Itakura, Shoji

2010-01-01

This study investigated whether one hundred and thirty-five 3- to 6-year-old children exhibit a yes bias to various yes-no questions and whether their knowledge status affects the production of a yes bias. Three-year-olds exhibited a yes bias to all yes-no questions such as "preference-object" and "knowledge-object" questions pertaining to…

11. Electric Control of Exchange Bias Training

Echtenkamp, W.; Binek, Ch.

2013-11-01

Voltage-controlled exchange bias training and tunability are introduced. Isothermal voltage pulses are used to reverse the antiferromagnetic order parameter of magnetoelectric Cr2O3, and thus continuously tune the exchange bias of an adjacent CoPd film. Voltage-controlled exchange bias training is initialized by tuning the antiferromagnetic interface into a nonequilibrium state incommensurate with the underlying bulk. Interpretation of these hitherto unreported effects contributes to new understanding in electrically controlled magnetism.

12. Evaluation of bias associated with high-multiplex, target-specific pre-amplification

PubMed Central

Okino, Steven T.; Kong, Michelle; Sarras, Haya; Wang, Yan

2015-01-01

We developed a novel PCR-based pre-amplification (PreAmp) technology that can increase the abundance of over 350 target genes one million-fold. To assess potential bias introduced by PreAmp we utilized ERCC RNA reference standards, a model system that quantifies measurement error in RNA analysis. We assessed three types of bias: amplification bias, dynamic range bias and fold-change bias. We show that our PreAmp workflow introduces only minimal amplification and fold-change bias under stringent conditions. We do detect dynamic range bias if a target gene is highly abundant and PreAmp occurred for 16 or more PCR cycles; however, this type of bias is easily correctable. To assess PreAmp bias in a gene expression profiling experiment, we analyzed a panel of genes that are regulated during differentiation using the NTera2 stem cell model system. We find that results generated using PreAmp are similar to results obtained using standard qPCR (without the pre-amplification step). Importantly, PreAmp maintains patterns of gene expression changes across samples; the same biological insights would be derived from a PreAmp experiment as with a standard gene expression profiling experiment. We conclude that our PreAmp technology can facilitate analysis of extremely limited samples in gene expression quantification experiments. PMID:27077043

13. Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

2013-03-01

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

14. Chronic and acute biases in perceptual stabilization

PubMed Central

Al-Dossari, Munira; Blake, Randolph; Brascamp, Jan W.; Freeman, Alan W.

2015-01-01

When perceptually ambiguous stimuli are presented intermittently, the percept on one presentation tends to be the same as that on the previous presentation. The role of short-term, acute biases in the production of this perceptual stability is relatively well understood. In addition, however, long-lasting, chronic bias may also contribute to stability. In this paper we develop indices for both biases and for stability, and show that stability can be expressed as a sum of contributions from the two types of bias. We then apply this analytical procedure to binocular rivalry, showing that adjustment of the monocular contrasts can alter the relative contributions of the two biases. Stability is mainly determined by chronic bias when the contrasts are equal, but acute bias dominates stability when right-eye contrast is set lower than left-eye contrast. Finally, we show that the right-eye bias persists in continuous binocular rivalry. Our findings reveal a previously unappreciated contribution of chronic bias to stable perception. PMID:26641947

15. Detecting Gender Bias Through Test Item Analysis

2009-03-01

Many physical science and physics instructors might not be trained in pedagogically appropriate test construction methods. This could lead to test items that do not measure what they are intended to measure. A subgroup of these items might show bias against some groups of students. This paper describes how the author became aware of potentially biased items against females in his examinations, which led to the exploration of fundamental issues related to item validity, gender bias, and differential item functioning, or DIF. A brief discussion of DIF in the context of university courses, as well as practical suggestions to detect possible gender-biased items, follows.

16. Identifying and Avoiding Bias in Research

PubMed Central

Pannucci, Christopher J.; Wilkins, Edwin G.

2010-01-01

This narrative review provides an overview on the topic of bias as part of Plastic and Reconstructive Surgery's series of articles on evidence-based medicine. Bias can occur in the planning, data collection, analysis, and publication phases of research. Understanding research bias allows readers to critically and independently review the scientific literature and avoid treatments which are suboptimal or potentially harmful. A thorough understanding of bias and how it affects study results is essential for the practice of evidence-based medicine. PMID:20679844

17. Bayesian long branch attraction bias and corrections.

PubMed

Susko, Edward

2015-03-01

Previous work on the star-tree paradox has shown that Bayesian methods suffer from a long branch attraction bias. That work is extended to settings involving more taxa and partially resolved trees. The long branch attraction bias is confirmed to arise more broadly and an additional source of bias is found. A by-product of the analysis is methods that correct for biases toward particular topologies. The corrections can be easily calculated using existing Bayesian software. Posterior support for a set of two or more trees can thus be supplemented with corrected versions to cross-check or replace results. Simulations show the corrections to be highly effective.

18. How Do Biases in General Circulation Models Affect Projections of Aridity and Drought?

Ficklin, D. L.; Abatzoglou, J. T.; Robeson, S. M.; Dufficy, A. L.

2015-12-01

Unless corrected, biases in General Circulation Models (GCMs) can affect hydroclimatological applications and projections. Compared to a raw GCM ensemble (direct GCM output), bias-corrected GCM inputs correct for systematic errors and can produce high-resolution projections that are useful for impact analyses. By examining the difference between raw and bias-corrected GCMs for the continental United States, this work highlights how GCM biases can affect projections of aridity (defined as precipitation (P)/potential evapotranspiration (PET)) and drought (using the Palmer Drought Severity Index (PDSI)). At the annual time scale for spatial averages over the continental United States, the raw GCM ensemble median has a historical positive precipitation bias (+24%) and negative PET bias (-7%) compared to the bias-corrected output. While both GCM ensembles (raw and bias-corrected) result in drier conditions in the future, the bias-corrected GCMs produce enhanced aridity (number of months with PET>P) in the late 21st century (2070-2099) compared to the historical climate (1950-1979). For the western United States, the bias-corrected GCM ensemble estimates much less humid and sub-humid conditions (based on P/PET categorical values) than the raw GCM ensemble. However, using June, July, and August PDSI, the bias-corrected GCM ensemble projects less acute decreases for the southwest United States compared to the raw GCM ensemble (1 to 2 PDSI units higher) as a result of larger decreases in projected precipitation in the raw GCM ensemble. A number of examples and ecological implications of this work for the western United States will be presented.

19. The Impact of Cognitive Stressors in the Emergency Department on Physician Implicit Racial Bias

PubMed Central

Johnson, Tiffani J.; Hickey, Robert W.; Switzer, Galen E.; Miller, Elizabeth; Winger, Daniel G.; Nguyen, Margaret; Saladino, Richard A.; Hausmann, Leslie R. M.

2016-01-01

Objectives The emergency department (ED) is characterized by stressors (e.g. fatigue, stress, time-pressure, and complex decision-making) that can pose challenges to delivering high quality, equitable care. Although it has been suggested that characteristics of the ED may exacerbate reliance on cognitive heuristics, no research has directly investigated whether stressors in the ED impact physician racial bias, a common heuristic. We seek to determine if physicians have different levels of implicit racial bias post-ED shift versus pre-shift, and to examine associations between demographics and cognitive stressors with bias. Methods This repeated measures study of resident physicians in a pediatric ED used electronic pre- and post-shift assessments of implicit racial bias, demographics, and cognitive stressors. Implicit bias was measured using the Race Implicit Association Test (IAT). Linear regression models compared differences in IAT scores pre- to post-shift, and determined associations between participant demographics and cognitive stressors with post-shift IAT and pre- to post-shift difference scores. Results Participants (n=91) displayed moderate pro-white/anti-black bias on pre-shift (M=0.50, SD=0.34, d=1.48) and post-shift (M=0.55, SD=0.39, d=1.40) IAT scores. Overall, IAT scores did not differ pre-shift to post-shift (mean increase=0.05, 95% CI −0.02,0.14, d=0.13). Sub-analyses revealed increased pre- to post-shift bias among participants working when the ED was more overcrowded (mean increase=0.09, 95% CI 0.01,0.17, d=0.24) and among those caring for >10 patients (mean increase=0.17, 95% CI 0.05,0.27, d=0.47). Residents’ demographics (including specialty), fatigue, busyness, stressfulness, and number of shifts were not associated with post-shift IAT or difference scores. In multivariable models, ED overcrowding was associated with greater post-shift bias (coefficient=0.11 per 1 unit of NEDOCS score, SE=0.05, 95% CI 0.00,0.21). Conclusions While

20. Gender bias in the evaluation of new age music.

PubMed

Colley, Ann; North, Adrian; Hargreaves, David J

2003-04-01

Eminent composers in Western European art music continue to be predominantly male and eminence in contemporary pop music is similarly male dominated. One contributing factor may be the continuing under-valuation of women's music. Possible anti-female bias in a contemporary genre was investigated using the Goldberg paradigm to elicit judgments of New Age compositions. Since stronger stereotyping effects occur when information provided about individuals is sparse, fictitious male and female composers were presented either by name only or by name with a brief biography. Evidence for anti-female bias was found in the name-only condition and was stronger when liking for the music was controlled. Other findings were the tendency for females to give higher ratings, and the association of gender differences in liking of the music with ratings of quality in the name-only condition. These results are relevant to the design of formal assessment procedures for musical composition.

1. Effects of Poverty and Lack of Insurance on Perceptions of Racial and Ethnic Bias in Health Care

PubMed Central

Stepanikova, Irena; Cook, Karen S

2008-01-01

Objective To investigate whether poverty and lack of insurance are associated with perceived racial and ethnic bias in health care. Data Source 2001 Survey on Disparities in Quality of Health Care, a nationally representative telephone survey. We use data on black, Hispanic, and white adults who have a regular physician (N=4,556). Study Design We estimate multivariate logistic regression models to examine the effects of poverty and lack of health insurance on perceived racial and ethnic bias in health care for all respondents and by racial, ethnic, and language groups. Principal Findings Controlling for sociodemographic and other factors, uninsured blacks and Hispanics interviewed in English are more likely to report racial and ethnic bias in health care compared with their privately insured counterparts. Poor whites are more likely to report racial and ethnic bias in health care compared with other whites. Good physician–patient communication is negatively associated with perceived racial and ethnic bias. Conclusions Compared with their more socioeconomically advantaged counterparts, poor whites, uninsured blacks, and some uninsured Hispanics are more likely to perceive that racial and ethnic bias operates in the health care they receive. Providing health insurance for the uninsured may help reduce this perceived bias among some minority groups. PMID:18546546

2. Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

Paxian, A.; Sein, D.; Panitz, H.-J.; Warscher, M.; Breil, M.; Engel, T.; Tödter, J.; Krause, A.; Cabos Narvaez, W. D.; Fink, A. H.; Ahrens, B.; Kunstmann, H.; Jacob, D.; Paeth, H.

2016-02-01

The West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO-CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high-resolution atmosphere-ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.

3. Multivariate-\$t\$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-03-20

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-\$t\$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-\$t\$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches.

4. Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.

PubMed

Lee, Jaeyoung; Abdel-Aty, Mohamed; Jiang, Ximiao

2015-05-01

Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode.

5. Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd.

6. A Quality by Design approach to investigate tablet dissolution shift upon accelerated stability by multivariate methods.

PubMed

Huang, Jun; Goolcharran, Chimanlall; Ghosh, Krishnendu

2011-05-01

This paper presents the use of experimental design, optimization and multivariate techniques to investigate root-cause of tablet dissolution shift (slow-down) upon stability and develop control strategies for a drug product during formulation and process development. The effectiveness and usefulness of these methodologies were demonstrated through two application examples. In both applications, dissolution slow-down was observed during a 4-week accelerated stability test under 51°C/75%RH storage condition. In Application I, an experimental design was carried out to evaluate the interactions and effects of the design factors on critical quality attribute (CQA) of dissolution upon stability. The design space was studied by design of experiment (DOE) and multivariate analysis to ensure desired dissolution profile and minimal dissolution shift upon stability. Multivariate techniques, such as multi-way principal component analysis (MPCA) of the entire dissolution profiles upon stability, were performed to reveal batch relationships and to evaluate the impact of design factors on dissolution. In Application II, an experiment was conducted to study the impact of varying tablet breaking force on dissolution upon stability utilizing MPCA. It was demonstrated that the use of multivariate methods, defined as Quality by Design (QbD) principles and tools in ICH-Q8 guidance, provides an effective means to achieve a greater understanding of tablet dissolution upon stability.

7. [Statistical tests in medical research: traditional methods vs. multivariate NPC permutation tests].

PubMed

Arboretti, Rosa; Bordignon, Paolo; Corain, Livio; Palermo, Giuseppe; Pesarin, Fortunato; Salmaso, Luigi

2015-01-01

Statistical tests in medical research: traditional methods vs. multivariate npc permutation tests.Within medical research, a useful statistical tool is based on hypotheses testing in terms of the so-called null, that is the treatment has no effect, and alternative hypotheses, that is the treatment has some effects. By controlling the risks of wrong decisions, empirical data are used in order to possibly reject the null hypotheses in favour of the alternative, so that demonstrating the efficacy of a treatment of interest. The multivariate permutation tests, based on the nonparametric combination - NPC method, provide an innovative, robust and effective hypotheses testing solution to many real problems that are commonly encountered in medical research when multiple end-points are observed. This paper discusses the various approaches to hypothesis testing and the main advantages of NPC tests, which consist in the fact that they require much less stringent assumptions than traditional statistical tests. Moreover, the related results may be extended to the reference population even in case of selection-bias, that is non-random sampling. In this work, we review and discuss some basic testing procedures along with the theoretical and practical relevance of NPC tests showing their effectiveness in medical research. Within the non-parametric methods, NPC tests represent the current "frontier" of statistical research, but already widely available in the practice of analysis of clinical data.

8. Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

PubMed

Lapsley, Daniel K; Hill, Patrick L

2010-08-01

The relationship between subjective invulnerability and optimism bias in risk appraisal, and their comparative association with indices of risk activity, substance use and college adjustment problems was assessed in a sample of 350 (M (age) = 20.17; 73% female; 93% White/European American) emerging adults. Subjective invulnerability was measured with the newly devised adolescent invulnerability scale (AIS). Optimism bias in decision-making was assessed with a standard comparative-conditional risk appraisal task. Results showed that the danger- and psychological invulnerability subscales of the AIS demonstrated strong internal consistency and evidence of predictive validity. Subjective invulnerability and optimism bias were also shown to be empirically distinct constructs with differential ability to predict risk and adjustment. Danger invulnerability and psychological invulnerability were more pervasively associated with risk behavior than was optimism bias; and psychological invulnerability counter-indicated depression, self-esteem and interpersonal problems. Results support recent claims regarding the "two faces" of adolescent invulnerability. Implications for future research are drawn.

10. Potential Interference Bias in Ozone Standard Compliance Monitoring.

PubMed

Leston, Alan R; Ollison, Will M; Spicer, Chester W; Satola, Jan

2005-10-01

The U.S. Environmental Protection Agency has established a federal reference method (FRM) for ozone (O3) and allowed for designation of federal equivalent methods (FEMs). However, the ethylene-chemiluminescence FRM for O3 has been replaced by the UV photometric FEM by most state and local monitoring agencies because of its relative ease of operation. Accumulating evidence indicates that the FEM is prone to bias under the hot, humid, and stagnant conditions conducive to high O3 formation. This bias may lead to overreporting hourly O3 concentrations by as much as 20-40 ppb. Measurement bias is caused by contamination of the O3 scrubber, a problem that is not detected by dry air calibration. An adequate wet test has not been codified, although a procedure has been proposed for agency consideration. This paper includes documentation of laboratory tests quantifying specific interferant responses, collocated ambient FRM/FEM monitoring results, and smog chamber comparisons of the FRM and FEMs with alternative scrubber designs. As the numbers of reports on monitor interferences have grown, interested parties have called for agency recognition and correction of these biases.

11. Belief bias and the extinction of induced fear.

PubMed

Vroling, Maartje S; de Jong, Peter J

2013-01-01

Some people show slower extinction of UCS expectancies than other people. Little is known about what predicts such delayed extinction. Extinction requires that people deduce the logical implication of corrective experiences challenging the previously learned CS-UCS contingency. "A strong habitual tendency to confirm beliefs" may therefore be a powerful mechanism immunising against refutation of UCS expectancies. This study investigated whether individual differences in such a belief confirming tendency (a process called "belief bias") may help in explaining individual differences in extinction. We tested whether relatively strong belief bias predicts delayed extinction of experimentally induced UCS expectancies. In a differential aversive conditioning paradigm, we used UCS-irrelevant (Experiment 1) and UCS-relevant (Experiment 2) pictorial stimuli as CS⁺ and CS⁻, and electrical stimulation as UCS. Belief bias indeed predicted delayed extinction of UCS expectancies when the CS⁺ was UCS-relevant (as is typically the case for phobic stimuli, Experiment 2). The study provides preliminary evidence that enhanced belief bias may indeed play a role in the persistence of UCS expectancies, and can thereby contribute to the development and persistence of anxiety disorders. The results also point to the relevance of reasoning tendencies in the search for predictors of delayed extinction of UCS expectancies.

12. Comparison of projection skills of deterministic ensemble methods using pseudo-simulation data generated from multivariate Gaussian distribution

Oh, Seok-Geun; Suh, Myoung-Seok

2016-03-01

The projection skills of five ensemble methods were analyzed according to simulation skills, training period, and ensemble members, using 198 sets of pseudo-simulation data (PSD) produced by random number generation assuming the simulated temperature of regional climate models. The PSD sets were classified into 18 categories according to the relative magnitude of bias, variance ratio, and correlation coefficient, where each category had 11 sets (including 1 truth set) with 50 samples. The ensemble methods used were as follows: equal weighted averaging without bias correction (EWA_NBC), EWA with bias correction (EWA_WBC), weighted ensemble averaging based on root mean square errors and correlation (WEA_RAC), WEA based on the Taylor score (WEA_Tay), and multivariate linear regression (Mul_Reg). The projection skills of the ensemble methods improved generally as compared with the best member for each category. However, their projection skills are significantly affected by the simulation skills of the ensemble member. The weighted ensemble methods showed better projection skills than non-weighted methods, in particular, for the PSD categories having systematic biases and various correlation coefficients. The EWA_NBC showed considerably lower projection skills than the other methods, in particular, for the PSD categories with systematic biases. Although Mul_Reg showed relatively good skills, it showed strong sensitivity to the PSD categories, training periods, and number of members. On the other hand, the WEA_Tay and WEA_RAC showed relatively superior skills in both the accuracy and reliability for all the sensitivity experiments. This indicates that WEA_Tay and WEA_RAC are applicable even for simulation data with systematic biases, a short training period, and a small number of ensemble members.

13. Multivariable Dynamic Ankle Mechanical Impedance With Active Muscles

PubMed Central

Lee, Hyunglae; Krebs, Hermano Igo; Hogan, Neville

2015-01-01

Multivariable dynamic ankle mechanical impedance in two coupled degrees-of-freedom (DOFs) was quantified when muscles were active. Measurements were performed at five different target activation levels of tibialis anterior and soleus, from 10% to 30% of maximum voluntary contraction (MVC) with increments of 5% MVC. Interestingly, several ankle behaviors characterized in our previous study of the relaxed ankle were observed with muscles active: ankle mechanical impedance in joint coordinates showed responses largely consistent with a second-order system consisting of inertia, viscosity, and stiffness; stiffness was greater in the sagittal plane than in the frontal plane at all activation conditions for all subjects; and the coupling between dorsiflexion–plantarflexion and inversion–eversion was small—the two DOF measurements were well explained by a strictly diagonal impedance matrix. In general, ankle stiffness increased linearly with muscle activation in all directions in the 2-D space formed by the sagittal and frontal planes, but more in the sagittal than in the frontal plane, resulting in an accentuated “peanut shape.” This characterization of young healthy subjects’ ankle mechanical impedance with active muscles will serve as a baseline to investigate pathophysiological ankle behaviors of biomechanically and/or neurologically impaired patients. PMID:25203497

14. Hidden Markov latent variable models with multivariate longitudinal data.

PubMed

Song, Xinyuan; Xia, Yemao; Zhu, Hongtu

2017-03-01

Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use.

15. Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

NASA Technical Reports Server (NTRS)

Boccippio, Dennis

2004-01-01

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

16. Multivariable Techniques for High-Speed Research Flight Control Systems

NASA Technical Reports Server (NTRS)

Newman, Brett A.

1999-01-01

This report describes the activities and findings conducted under contract with NASA Langley Research Center. Subject matter is the investigation of suitable multivariable flight control design methodologies and solutions for large, flexible high-speed vehicles. Specifically, methodologies are to address the inner control loops used for stabilization and augmentation of a highly coupled airframe system possibly involving rigid-body motion, structural vibrations, unsteady aerodynamics, and actuator dynamics. Design and analysis techniques considered in this body of work are both conventional-based and contemporary-based, and the vehicle of interest is the High-Speed Civil Transport (HSCT). Major findings include: (1) control architectures based on aft tail only are not well suited for highly flexible, high-speed vehicles, (2) theoretical underpinnings of the Wykes structural mode control logic is based on several assumptions concerning vehicle dynamic characteristics, and if not satisfied, the control logic can break down leading to mode destabilization, (3) two-loop control architectures that utilize small forward vanes with the aft tail provide highly attractive and feasible solutions to the longitudinal axis control challenges, and (4) closed-loop simulation sizing analyses indicate the baseline vane model utilized in this report is most likely oversized for normal loading conditions.

17. Haploinsufficiency predictions without study bias

PubMed Central

Steinberg, Julia; Honti, Frantisek; Meader, Stephen; Webber, Caleb

2015-01-01

Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied ‘gold standard’ haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants. PMID:26001969

18. Modeling confirmation bias and polarization

PubMed Central

Del Vicario, Michela; Scala, Antonio; Caldarelli, Guido; Stanley, H. Eugene; Quattrociocchi, Walter

2017-01-01

Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models. PMID:28074874

19. Modeling confirmation bias and polarization

Del Vicario, Michela; Scala, Antonio; Caldarelli, Guido; Stanley, H. Eugene; Quattrociocchi, Walter

2017-01-01

Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.

20. Assessing threat responses towards the symptoms and diagnosis of schizophrenia using visual perceptual biases.

PubMed

Heenan, Adam; Best, Michael W; Ouellette, Sarah J; Meiklejohn, Erin; Troje, Nikolaus F; Bowie, Christopher R

2014-10-01

Stigma towards individuals diagnosed with schizophrenia continues despite increasing public knowledge about the disorder. Questionnaires are used almost exclusively to assess stigma despite self-report biases affecting their validity. The purpose of this experiment was to implicitly assess stigma towards individuals with schizophrenia by measuring visual perceptual biases immediately after participants conversed with a confederate. We manipulated both the diagnostic label attributed to the confederate (peer vs. schizophrenia) and the presence of behavioural symptoms (present vs. absent). Immediately before and after conversing with the confederate, we measured participants' facing-the-viewer (FTV) biases (the preference to perceive depth-ambiguous stick-figure walkers as facing towards them). As studies have suggested that the FTV bias is sensitive to the perception of threat, we hypothesized that FTV biases would be greater after participants conversed with someone that they believed had schizophrenia, and also after they conversed with someone who presented symptoms of schizophrenia. We found partial support for these hypotheses. Participants had significantly greater FTV biases in the Peer Label/Symptoms Present condition. Interestingly, while FTV biases were lowest in the Schizophrenia Label/Symptoms Present condition, participants in this condition were most likely to believe that people with schizophrenia should face social restrictions. Our findings support that both implicit and explicit beliefs help develop and sustain stigma.

1. The influence of anticipatory processing on attentional biases in social anxiety.

PubMed

Mills, Adam C; Grant, DeMond M; Judah, Matt R; White, Evan J

2014-09-01

Research on cognitive theories of social anxiety disorder (SAD) has identified individual processes that influence this condition (e.g., cognitive biases, repetitive negative thinking), but few studies have attempted to examine the interaction between these processes. For example, attentional biases and anticipatory processing are theoretically related and have been found to influence symptoms of SAD, but they rarely have been studied together (i.e., Clark & Wells, 1995). Therefore, the goal of the current study was to examine the effect of anticipatory processing on attentional bias for internal (i.e., heart rate feedback) and external (i.e., emotional faces) threat information. A sample of 59 participants high (HSA) and low (LSA) in social anxiety symptoms engaged in a modified dot-probe task prior to (Time 1) and after (Time 2) an anticipatory processing or distraction task. HSAs who anticipated experienced an increase in attentional bias for internal information from Time 1 to Time 2, whereas HSAs in the distraction condition and LSAs in either condition experienced no changes. No changes in biases were found for HSAs for external biases, but LSAs who engaged in the distraction task became less avoidant of emotional faces from Time 1 to Time 2. This suggests that anticipatory processing results in an activation of attentional biases for physiological information as suggested by Clark and Wells.

2. Exploratory Studies of Bias in Achievement Tests.

ERIC Educational Resources Information Center

Green, Donald Ross; Draper, John F.

This paper considers the question of bias in group administered academic achievement tests, bias which is inherent in the instruments themselves. A body of data on the test of performance of three disadvantaged minority groups--northern, urban black; southern, rural black; and, southwestern, Mexican-Americans--as tryout samples in contrast to…

3. Attributional Biases among Clinicians and Nonclinicians.

ERIC Educational Resources Information Center

Harari, Oren; Hosey, Karen R.

1981-01-01

Clinicians and nonclinicians made causal attributions to actor behaviors. Analysis showed clear observer attribution bias for both groups. A greater bias occurred with deviant actor behavior and in situations that featured actor actions over opinions over emotions. Results are discussed in terms of applicability to clinical practice. (Author/JAC)

4. Hindsight Bias and Developing Theories of Mind

ERIC Educational Resources Information Center

Bernstein, Daniel M.; Atance, Cristina; Meltzoff, Andrew N.; Loftus, Geoffrey R.

2007-01-01

Although "hindsight bias" (the "I knew it all along" phenomenon) has been documented in adults, its development has not been investigated. This is despite the fact that hindsight bias errors closely resemble the errors children make on theory of mind (ToM) tasks. Two main goals of the present work were to (a) create a battery of hindsight tasks…

5. Understanding Unconscious Bias and Unintentional Racism

ERIC Educational Resources Information Center

Moule, Jean

2009-01-01

Unconscious biases affect one's relationships, whether they are fleeting relationships in airports or longer term relationships between teachers and students, teachers and parents, teachers and other educators. In this article, the author argues that understanding one's possible biases is essential for developing community in schools.…

6. Developmental Changes in the Whole Number Bias

ERIC Educational Resources Information Center

Braithwaite, David W.; Siegler, Robert S.

2017-01-01

Many students' knowledge of fractions is adversely affected by whole number bias, the tendency to focus on the separate whole number components (numerator and denominator) of a fraction rather than on the fraction's integrated magnitude (ratio of numerator to denominator). Although whole number bias appears early in the fraction learning process…

7. Definition of the Situation and Observer Bias.

ERIC Educational Resources Information Center

Kolman, Anita Sue

An experiment is reported in which an attempt was made to bias college students' observations of a videotape of children at play. The study is framed in terms of W.I. Thomas' ideas concerning the definition of the situation. Observer bias is an instance when a definition of a situation is based primarily on subjective situational factors. Reliance…

8. Biases in Children's and Adults' Moral Judgments

ERIC Educational Resources Information Center

Powell, Nina L.; Derbyshire, Stuart W. G.; Guttentag, Robert E.

2012-01-01

Two experiments examined biases in children's (5/6- and 7/8-year-olds) and adults' moral judgments. Participants at all ages judged that it was worse to produce harm when harm occurred (a) through action rather than inaction (omission bias), (b) when physical contact with the victim was involved (physical contact principle), and (c) when the harm…

9. Distinctive characteristics of sexual orientation bias crimes.

PubMed

Stacey, Michele

2011-10-01

Despite increased attention in the area of hate crime research in the past 20 years, sexual orientation bias crimes have rarely been singled out for study. When these types of crimes are looked at, the studies are typically descriptive in nature. This article seeks to increase our knowledge of sexual orientation bias by answering the question: What are the differences between sexual orientation motivated bias crimes and racial bias crimes? This question is examined using data from the National Incident Based Reporting System (NIBRS) and multiple regression techniques. This analysis draws on the strengths of NIBRS to look at the incident characteristics of hate crimes and distinguishing characteristics of sexual orientation crimes. Specifically this analysis looks at the types and seriousness of offenses motivated by sexual orientation bias as opposed to race bias as well as victim and offender characteristics. The findings suggest that there are differences between these two types of bias crimes, suggesting a need for further separation of the bias types in policy and research.

10. Distinctive Characteristics of Sexual Orientation Bias Crimes

ERIC Educational Resources Information Center

Stacey, Michele

2011-01-01

Despite increased attention in the area of hate crime research in the past 20 years, sexual orientation bias crimes have rarely been singled out for study. When these types of crimes are looked at, the studies are typically descriptive in nature. This article seeks to increase our knowledge of sexual orientation bias by answering the question:…

11. Racially Biased Policing: Determinants of Citizen Perceptions

ERIC Educational Resources Information Center

Weitzer, Ronald; Tuch, Steven A.

2005-01-01

The current controversy surrounding racial profiling in America has focused renewed attention on the larger issue of racial bias by the police. Yet little is known about the extent of police racial bias and even less about public perceptions of the problem. This article analyzes recent national survey data on citizens' views of and reported…

12. The Battle over Studies of Faculty Bias

ERIC Educational Resources Information Center

Gravois, John

2007-01-01

The American Federation of Teachers (AFT) recently commissioned a study to review the research that finds liberal bias run amok in academe. Believing that the AFT is not a dispassionate observer of this debate, this article provides "The Chronicle of Higher Education's" survey of the genre. The studies reviewed include: (1) "Political Bias in the…

13. How Many Hindsight Biases Are There?

ERIC Educational Resources Information Center

Blank, Hartmut; Nestler, Steffen; von Collani, Gernot; Fischer, Volkhard

2008-01-01

The answer is three: questioning a conceptual default assumption in hindsight bias research, we argue that the hindsight bias is not a unitary phenomenon but consists of three separable and partially independent subphenomena or components, namely, memory distortions, impressions of foreseeability and impressions of necessity. Following a detailed…

14. Distractors--Can They Be Biased Too?

ERIC Educational Resources Information Center

Alagumalai, Sivakumar; Keeves, John P.

1999-01-01

How distractors in a test item function differentially is discussed. Also discussed are methods to identify distractor bias, including the Pearson chi square, likelihood-ratio chi square, and the Neyman weighted-least squares chi square tests. Problems from a physics test illustrate possible causes of distractor bias. (SLD)

15. Understanding Implicit Bias: What Educators Should Know

ERIC Educational Resources Information Center

Staats, Cheryl

2016-01-01

The desire to ensure the best for children is precisely why educators should become aware of the concept of implicit bias: the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. Operating outside of our conscious awareness, implicit biases are pervasive, and they can challenge even the most…

16. Multivariate neural network operators with sigmoidal activation functions.

PubMed

Costarelli, Danilo; Spigler, Renato

2013-12-01

In this paper, we study pointwise and uniform convergence, as well as order of approximation, of a family of linear positive multivariate neural network (NN) operators with sigmoidal activation functions. The order of approximation is studied for functions belonging to suitable Lipschitz classes and using a moment-type approach. The special cases of NN operators, activated by logistic, hyperbolic tangent, and ramp sigmoidal functions are considered. Multivariate NNs approximation finds applications, typically, in neurocomputing processes. Our approach to NN operators allows us to extend previous convergence results and, in some cases, to improve the order of approximation. The case of multivariate quasi-interpolation operators constructed with sigmoidal functions is also considered.

17. Root Zone Soil Moisture Forecasting Using Multivariate Relevance Vector Machines

Zaman, B.; McKee, M.

2009-12-01

Root zone soil moisture at depths of 1 and 2 meters are forecasted four days into the future. Prediction of soil moisture can be of paramount importance owing to its applicability in soil water balance calculations, modeling of various hydrometeorological, ecological, and biogeochemical factors, and initialization of various land-atmosphere models. In this study, we propose a new multivariate output prediction approach for forecasting root zone soil moisture using learning machine models. These models are known for their robustness, efficiency, and sparseness, and provide a statistically sound approach to solving the inverse problems and thus to building statistical models. The multivariate relevance vector machine (MVRVM) is used to build a model that predicts future soil state based upon current soil moisture and soil temperature conditions. The predicting function learns the input-output response pattern from the training dataset. Soil moisture measurements acquired by the Soil Climate Analysis Network (SCAN) site at Rees Center, Texas are used for this study. The methodology combines the data at different depths from 5 cm to 50 cm, the largest of which corresponds to the depth at which the soil moisture sensors are generally operational, to produce soil moisture predictions at larger depths. The MVRVM model demonstrates superior performance. The results for soil moisture predictions at 1 m and 2 m depth for the fourth day are excellent, with RMSE = 0.0125 m3water/m3soil; IoA = 0.96; CoE = 0.88 at 1 m depth, and RMSE = 0.0021 m3/m3; IoA = 0.98; CoE = 0.93 for 2 m depth. The statistics indicate good model generalization capability and computations show good agreement with the actual soil moisture measurements with R2 = 0.89 and R2 = 0.94 for 1 m and 2 m depths on fourth day, respectively. The MVRVM produces good results for all four days with a reduced computational complexity and more suitable real-time implementation. Bootstrapping is used to check over

18. Full Electric Control of Exchange Bias

Wu, S. M.; Cybart, Shane A.; Yi, D.; Parker, James M.; Ramesh, R.; Dynes, R. C.

2013-02-01

We report the creation of a multiferroic field effect device with a BiFeO3 (BFO) (antiferromagnetic-ferroelectric) gate dielectric and a La0.7Sr0.3MnO3 (LSMO) (ferromagnetic) conducting channel that exhibits direct, bipolar electrical control of exchange bias. We show that exchange bias is reversibly switched between two stable states with opposite exchange bias polarities upon ferroelectric poling of the BFO. No field cooling, temperature cycling, or additional applied magnetic or electric field beyond the initial BFO polarization is needed for this bipolar modulation effect. Based on these results and the current understanding of exchange bias, we propose a model to explain the control of exchange bias. In this model the coupled antiferromagnetic-ferroelectric order in BFO along with the modulation of interfacial exchange interactions due to ionic displacement of Fe3+ in BFO relative to Mn3+/4+ in LSMO cause bipolar modulation.

19. A catalog of biases in questionnaires.

PubMed

Choi, Bernard C K; Pak, Anita W P

2005-01-01

Bias in questionnaires is an important issue in public health research. To collect the most accurate data from respondents, investigators must understand and be able to prevent or at least minimize bias in the design of their questionnaires. This paper identifies and categorizes 48 types of bias in questionnaires based on a review of the literature and offers an example of each type. The types are categorized according to three main sources of bias: the way a question is designed, the way the questionnaire as a whole is designed, and how the questionnaire is administered. This paper is intended to help investigators in public health understand the mechanism and dynamics of problems in questionnaire design and to provide a checklist for identifying potential bias in a questionnaire before it is administered.

20. Are all biases missing data problems?

PubMed

Howe, Chanelle J; Cain, Lauren E; Hogan, Joseph W

2015-09-01

Estimating causal effects is a frequent goal of epidemiologic studies. Traditionally, there have been three established systematic threats to consistent estimation of causal effects. These three threats are bias due to confounders, selection, and measurement error. Confounding, selection, and measurement bias have typically been characterized as distinct types of biases. However, each of these biases can also be characterized as missing data problems that can be addressed with missing data solutions. Here we describe how the aforementioned systematic threats arise from missing data as well as review methods and their related assumptions for reducing each bias type. We also link the assumptions made by the reviewed methods to the missing completely at random (MCAR) and missing at random (MAR) assumptions made in the missing data framework that allow for valid inferences to be made based on the observed, incomplete data.

1. G Protein-Coupled Receptor Biased Agonism

PubMed Central

Hodavance, Sima Y.; Gareri, Clarice; Torok, Rachel D.; Rockman, Howard A.

2016-01-01

G protein-coupled receptors (GPCR) are the largest family of targets for current therapeutics. The classic model of their activation was binary, where agonist binding induced an active conformation and subsequent downstream signaling. Subsequently, the revised concept of biased agonism emerged, where different ligands at the same GPCR selectively activate one downstream pathway versus another. Advances in understanding the mechanism of biased agonism has led to the development of novel ligands, which have the potential for improved therapeutic and safety profiles. In this review, we summarize the theory and most recent breakthroughs in understanding biased signaling, examine recent laboratory investigations concerning biased ligands across different organ systems, and discuss the promising clinical applications of biased agonism. PMID:26751266

2. Attentional and memory bias for emotional information in crime victims with acute posttraumatic stress disorder (PTSD).

PubMed

Paunovi, N; Lundh, L G; Ost, L G

2002-01-01

A combined emotional Stroop, implicit memory (tachistoscopic identification) and explicit memory (free recall) task with three types of words (trauma-related, positive, and neutral) and two exposure conditions (subliminal and supraliminal) was administered to 39 crime victims with acute posttraumatic stress disorder (PTSD) and 39 age- and sex-matched controls. PTSD subjects showed supraliminal Stroop interference for trauma-related words and a similar effect on positive words. A specific explicit memory bias was found for trauma-related words among the PTSD subjects, but no preattentive bias on the subliminally presented words, nor any implicit memory bias. Findings suggest that acute PTSD subjects have an attentional and memory bias for threat-related material. Methodological limitations of the study are reviewed, and it is proposed that further studies are needed in order to elucidate whether acute PTSD Ss display a preattentive and implicit memory bias for trauma-related material.

3. Interpretable Early Classification of Multivariate Time Series

ERIC Educational Resources Information Center

Ghalwash, Mohamed F.

2013-01-01

Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems,…

4. Climate field reconstruction uncertainty arising from multivariate and nonlinear properties of predictors

Evans, M. N.; Smerdon, J. E.; Kaplan, A.; Tolwinski-Ward, S. E.; González-Rouco, J. F.

2014-12-01

Climate field reconstructions (CFRs) of the global annual surface air temperature (SAT) field and associated global area-weighted mean annual temperature (GMAT) are derived in a collection of pseudoproxy experiments for the past millennium. Pseudoproxies are modeled from temperature (T), precipitation (P), T+P, and VS-Lite (VSL), a nonlinear and multivariate proxy system model for tree ring widths. Spatial patterns of reconstruction skill and spectral bias for the T+P and VSL-derived CFRs are similar to those previously shown using temperature-only pseudoproxies but demonstrate overall degraded skill and spectral bias for SAT reconstruction. Analysis of GMAT spectra nevertheless suggests that the true GMAT frequency spectrum is resolved by those pseudoproxies (T, T+P, and VSL) that contain some temperature information. The results suggest that mixed temperature and moisture-responding paleoclimate data may produce actual GMAT reconstructions with skill, error, and spectral characteristics like those expected from univariate and linear temperature responders, but spatially resolved CFR results should be analyzed cautiously.

5. Evidence Of Nationalistic Bias In Muaythai

PubMed Central

Myers, Tony D.; Balmer, Nigel J.; Nevill, Alan M.; Nakeeb, Yahya Al

2006-01-01

MuayThai is a combat sport with a growing international profile but limited research conducted into judging practices and processes. Problems with judging of other subjectively judged combat sports have caused controversy at major international tournaments that have resulted in changes to scoring methods. Nationalistic bias has been central to these problems and has been identified across a range of sports. The aim of this study was to examine nationalistic bias in MuayThai. Data were collected from the International Federation of MuayThai Amateur (IFMA) World Championships held in Almaty, Kazakhstan September 2003 and comprised of tournament results from 70 A-class MuayThai bouts each judged by between five and nine judges. Bouts examined featured 62 competitors from 21 countries and 25 judges from 11 countries. Results suggested that nationalistic bias was evident. The bias observed equated to approximately one round difference between opposing judges over the course of a bout (a mean of 1.09 (SE=0.50) points difference between judges with opposing affilations). The number of neutral judges used meant that this level of bias generally did not influence the outcome of bouts. Future research should explore other ingroup biases, such as nearest neighbour bias and political bias as well as investigating the feasibility adopting an electronic scoring system. Key Points Nationalistic bias is evident in international amateur MuayThai judging. The impact on the outcome of bouts is limited. The practice of using a large number of neutral judges appears to reduce the impact of nationalistic bias. PMID:24357972

6. Medical journal peer review: process and bias.

PubMed

Manchikanti, Laxmaiah; Kaye, Alan D; Boswell, Mark V; Hirsch, Joshua A

2015-01-01

Scientific peer review is pivotal in health care research in that it facilitates the evaluation of findings for competence, significance, and originality by qualified experts. While the origins of peer review can be traced to the societies of the eighteenth century, it became an institutionalized part of the scholarly process in the latter half of the twentieth century. This was a response to the growth of research and greater subject specialization. With the current increase in the number of specialty journals, the peer review process continues to evolve to meet the needs of patients, clinicians, and policy makers. The peer review process itself faces challenges. Unblinded peer review might suffer from positive or negative bias towards certain authors, specialties, and institutions. Peer review can also suffer when editors and/or reviewers might be unable to understand the contents of the submitted manuscript. This can result in an inability to detect major flaws, or revelations of major flaws after acceptance of publication by the editors. Other concerns include potentially long delays in publication and challenges uncovering plagiarism, duplication, corruption and scientific misconduct. Conversely, a multitude of these challenges have led to claims of scientific misconduct and an erosion of faith. These challenges have invited criticism of the peer review process itself. However, despite its imperfections, the peer review process enjoys widespread support in the scientific community. Peer review bias is one of the major focuses of today's scientific assessment of the literature. Various types of peer review bias include content-based bias, confirmation bias, bias due to conservatism, bias against interdisciplinary research, publication bias, and the bias of conflicts of interest. Consequently, peer review would benefit from various changes and improvements with appropriate training of reviewers to provide quality reviews to maintain the quality and integrity of

7. Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE PAGES

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-01-17

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

8. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

9. Multivariable disturbance observer-based H2 analytical decoupling control design for multivariable systems

Zhang, Wei; Wang, Yagang; Liu, Yurong; Zhang, Weidong

2016-01-01

In this paper, an H2 analytical decoupling control scheme with multivariable disturbance observer for both stable and unstable multi-input/multi-output (MIMO) systems with multiple time delays is proposed. Compared with conventional control strategies, the main merit is that the proposed control scheme can improve the system performances effectively when the MIMO processes with severe model mismatches and strong external disturbances. Besides, the design method has three additional advantages. First, the derived controller and observer are given in analytical forms, the design procedure is simple. Second, the orders of the designed controller and observer are low, they can be implemented easily in practice. Finally, the performance and robustness can be adjusted easily by tuning the parameters in the designed controller and observer. It is useful for practical application. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

10. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

11. Multivariate Generalized Beta Distributions with Applications to Utility Assessment.

ERIC Educational Resources Information Center

Libby, David L.; Novick, Melvin R.

1982-01-01

Two multivariate probability distributions, a generalized beta distribution and a generalized F distribution, are derived. Formulas for the moments of these distributions are given and an example of the bivariate generalized beta is presented. (Author/JKS)

12. Multivariate Cryptography Based on Clipped Hopfield Neural Network.

PubMed

Wang, Jia; Cheng, Lee-Ming; Su, Tong

2016-11-23

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in GF(p) space. The Diffie--Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

13. A unifying modeling framework for highly multivariate disease mapping.

PubMed

Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S

2015-04-30

Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models.

14. Attentional Bias in Children with Asthma with and without Anxiety Disorders.

PubMed

Dudeney, Joanne; Sharpe, Louise; Sicouri, Gemma; Lorimer, Sarah; Dear, Blake F; Jaffe, Adam; Selvadurai, Hiran; Hunt, Caroline

2017-01-09

Children with asthma have a high prevalence of anxiety disorders, however, very little is known about the mechanisms that confer vulnerability for anxiety in this population. This study investigated whether children with asthma and anxiety disorders display attentional biases towards threatening stimuli, similar to what has been seen in children with anxiety disorders more generally. We also examined the relationships between attentional biases and anxiety symptomatology and asthma control for children with asthma. Ninety-three children, aged 8-13, took part in the study and were recruited into one of four conditions (asthma/anxiety, asthma, anxiety, control). Asthma was medically confirmed and anxiety was assessed through clinical interview. We used self- and parent-report questionnaires to measure child asthma (ATAQ) and anxiety (SCAS, CASI) variables. Participants completed a visual dot-probe task designed to measure attentional bias towards two types of stimuli: asthma related words and general threat words, as well as tasks to assess reading ability and attentional control. Results showed that attentional biases did not differ between the groups, although children with anxiety disorders displayed poorer attentional control. A significant correlation was found between poor asthma control and an attentional bias of asthma stimuli. While we found no evidence that anxiety disorders in children with asthma were associated with threat- or asthma-related attentional biases, preliminary evidence suggested that children with poor asthma control displayed biases towards asthma-specific stimuli. Future research is needed to explore whether these attentional biases are adaptive.

15. Large biases in regression-based constituent flux estimates: causes and diagnostic tools

USGS Publications Warehouse

Hirsch, Robert M.

2014-01-01

It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

16. Reward sensitivity predicts ice cream-related attentional bias assessed by inattentional blindness.

PubMed

Li, Xiaoming; Tao, Qian; Fang, Ya; Cheng, Chen; Hao, Yangyang; Qi, Jianjun; Li, Yu; Zhang, Wei; Wang, Ying; Zhang, Xiaochu

2015-06-01

The cognitive mechanism underlying the association between individual differences in reward sensitivity and food craving is unknown. The present study explored the mechanism by examining the role of reward sensitivity in attentional bias toward ice cream cues. Forty-nine college students who displayed high level of ice cream craving (HICs) and 46 who displayed low level of ice cream craving (LICs) performed an inattentional blindness (IB) task which was used to assess attentional bias for ice cream. In addition, reward sensitivity and coping style were assessed by the Behavior Inhibition System/Behavior Activation System Scales and Simplified Coping Style Questionnaire. Results showed significant higher identification rate of the critical stimulus in the HICs than LICs, suggesting greater attentional bias for ice cream in the HICs. It was indicated that attentional bias for food cues persisted even under inattentional condition. Furthermore, a significant correlation was found between the attentional bias and reward sensitivity after controlling for coping style, and reward sensitivity predicted attentional bias for food cues. The mediation analyses showed that attentional bias mediated the relationship between reward sensitivity and food craving. Those findings suggest that the association between individual differences in reward sensitivity and food craving may be attributed to attentional bias for food-related cues.

17. Selecting on Treatment: A Pervasive Form of Bias in Instrumental Variable Analyses

PubMed Central

Swanson, Sonja A.; Robins, James M.; Miller, Matthew; Hernán, Miguel A.

2015-01-01

Instrumental variable (IV) methods are increasingly being used in comparative effectiveness research. Studies using these methods often compare 2 particular treatments, and the researchers perform their IV analyses conditional on patients' receiving this subset of treatments (while ignoring the third option of “neither treatment”). The ensuing selection bias that occurs due to this restriction has gone relatively unnoticed in interpretations and discussions of these studies' results. In this paper we describe the structure of this selection bias with examples drawn from commonly proposed instruments such as calendar time and preference, illustrate the bias with causal diagrams, and estimate the magnitude and direction of possible bias using simulations. A noncausal association between the proposed instrument and the outcome can occur in analyses restricted to patients receiving a subset of the possible treatments. This results in bias in the numerator for the standard IV estimator; the bias is amplified in the treatment effect estimate. The direction and magnitude of the bias in the treatment effect estimate are functions of the distribution of and relationships between the proposed instrument, treatment values, unmeasured confounders, and outcome. IV methods used to compare a subset of treatment options are prone to substantial biases, even when the proposed instrument appears relatively strong. PMID:25609096

18. Development of bias in analytical predictions based on behavior of platforms during hurricanes

SciTech Connect

Aggarwal, R.K.; Dolan, D.K.; Cornell, C.A.

1996-12-31

A Joint Industry Project (JIP) was initiated by 13 oil companies and the US Minerals Management Service (MMS), wherein a methodology was developed to use information from observed platform conditions resulting from Andrew and the hurricane hindcast data with capacity, reliability, and Bayesian updating analyses to determine a measure of differences (biases) in the analytical predictions and field observations. The procedures used for structural integrity analysis were also improved as a result of this study. Phase 1 of this project completed in October 1993 defined a global bias factor. A study of foundation behavior was completed following Phase 1 and determined bias factors specific to foundation failure modes. This paper presents the approach followed in the most recent phase of this project in which bias factors specific to jacket and two foundation failure modes (lateral and axial) were developed. This study utilized an updated storm hindcast, improved analysis models, and a more detailed calibration procedure. The three bias factors were developed and were found to differ significantly. The bias factors developed through this study have provided means to further improve procedures used in the assessment of existing platforms. The proper use of these new analytical methodologies and bias factors will produce more appropriate and cost-effective mitigation measures for safe platform operations. The methodology for establishing bias factors developed and proven in these projects is applicable to other offshore regions and production systems with specific environmental, geotechnical, material and structure features.

19. A Bayesian approach to multivariate measurement system assessment

SciTech Connect

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

20. Constructing multivariate distributions with generalized marginals and t-copulas

Dass, Sarat C.; Huang, Wenmei; Muthuvalu, Mohana S.

2014-10-01

Generalized distributions are probability distributions that have both discrete and continuous components. In this paper, a method is proposed for constructing flexible multivariate distributions based on arbitrarily pre-specified generalized marginals and t-copulas. We give theoretical results establishing identifiability of the parameters of the multivariate distribution. These distributions are useful for modeling real data that show non-Gaussian characteristics such as disease trajectories (i.e., malaria and dengue) over time and space.

1. Learning biases underlying individual differences in sensitivity to social rejection

PubMed Central

Olsson, Andreas; Carmona, Susanna; Downey, Geraldine; Bolger, Niall; Ochsner, Kevin N.

2014-01-01

People vary greatly in their dispositions to anxiously expect, readily perceive, and strongly react to social rejection (rejection sensitivity, RS) with implications for social functioning and health. Here, we examined how RS influences learning about social threat. Using a classical fear conditioning task, we established that high as compared to low (HRS vs. LRS) individuals displayed a resistance to extinction of the conditioned response to angry faces, but not to neutral faces or non-social stimuli. Our findings suggest that RS biases the flexible updating of acquired expectations for threat, which helps to explain how RS operates as a self-fulfilling prophecy. PMID:23914767

2. Oceanic origin of southeast tropical Atlantic biases

Xu, Zhao; Li, Mingkui; Patricola, Christina M.; Chang, Ping

2014-12-01

Most coupled general circulation models suffer from a prominent warm sea surface temperature bias in the southeast tropical Atlantic Ocean off the coast of Africa. The origin of the bias is not understood and remains highly controversial. Previous studies suggest that the origin of the bias stems from systematic errors of atmospheric models in simulating surface heat flux and coastal wind, or poorly simulated coastal upwelling. In this study, we show, using different reanalysis and observational data sets combined with a set of eddy-resolving regional ocean model simulations, that systematic errors in ocean models also make a significant contribution to the bias problem. In particular (1) the strong warm bias at the Angola-Benguela front that is maintained by the local wind and the convergence of Angola and Benguela Currents is caused by an overshooting of the Angola Current in ocean models and (2) the alongshore warm bias to the south of the front is caused by ocean model deficiencies in simulating the sharp thermocline along the Angola coast, which is linked to biases in the equatorial thermocline, and the complex circulation system within the Benguela upwelling zone.

3. Auditory Localisation Biases Increase with Sensory Uncertainty

PubMed Central

Garcia, Sara E.; Jones, Pete R.; Rubin, Gary S.; Nardini, Marko

2017-01-01

Psychophysical studies have frequently found that adults with normal hearing exhibit systematic errors (biases) in their auditory localisation judgments. Here we tested (i) whether systematic localisation errors could reflect reliance on prior knowledge, as has been proposed for other systematic perceptual biases, and (ii) whether auditory localisation biases can be reduced following training with accurate visual feedback. Twenty-four normal hearing participants were asked to localise the position of a noise burst along the azimuth before, during, and after training with visual feedback. Consistent with reliance on prior knowledge to reduce sensory uncertainty, we found that auditory localisation biases increased when auditory localisation uncertainty increased. Specifically, participants mis-localised auditory stimuli as being more eccentric than they were, and did so more when auditory uncertainty was greater. However, biases also increased with eccentricity, despite no corresponding increase in uncertainty, which is not readily explained by use of a simple prior favouring peripheral locations. Localisation biases decreased (improved) following training with visual feedback, but the reliability of the visual feedback stimulus did not change the effects of training. We suggest that further research is needed to identify alternative mechanisms, besides use of prior knowledge, that could account for increased perceptual biases under sensory uncertainty. PMID:28074913

4. [Practical considerations on detection of publication bias].

PubMed

Palma Pérez, Silvia; Delgado Rodríguez, Miguel

2006-12-01

The present review aims to answer 3 questions: does publication bias need to be assessed in meta-analyses?; what procedures, not requiring complex statistical approaches, can be applied to detect it?; and should other factors be taken into account when interpreting the procedures? The first question is easy to answer. Publication bias is a potential threat to the validity of the conclusions of meta-analyses. Therefore, both the MOOSE and QUOROM statements include publication bias in their guidelines; nevertheless, many meta-analyses do not use these statements (e.g., meta-analyses conducted by the Cochrane Collaboration), perhaps because they use a comprehensive search strategy. There are many methods to assess publication bias. The most frequently used are funnel plots or , (which allow the effects of bias to be estimated), and methods based upon regression on plots, such as Egger's method and funnel plot regression. An advantage of these methods is that they can only be applied using published data. However, agreement between these methods in detecting bias is often poor. Therefore, application of more than one method to detect publication bias is recommended. To correctly interpret the results, the number of pooled studies should be more than 10 and the existence of heterogeneity in the pooled estimate must be taken into account.

5. Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

6. Association Analysis for Visual Exploration of Multivariate Scientific Data Sets.

PubMed

Liu, Xiaotong; Shen, Han-Wei

2016-01-01

The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

7. Adjusting for nonresponse bias in a health examination survey.

PubMed Central

Rowland, M L; Forthofer, R N

1993-01-01

There is a potential for nonresponse bias in most population studies using health examinations. This is true of the Mexican American portion of the Hispanic Health and Nutrition Examination Survey (HHANES), conducted by the National Center for Health Statistics, in which unit nonresponse to the examination accounted for 24 percent of the sample. Even though the full effect of nonresponse can never be really known, ancillary information from the interview sample can be used in an attempt to adjust for bias in estimates. Two techniques for nonresponse bias adjustment are presented and illustrated using health status level and hypertension status from published studies based on the HHANES of 1982-84. The first approach uses conditional probabilities and the second approach uses direct standardization. The examples examine whether or not an adjustment for socioeconomic status, sex, and age--variables related to both response status and the conditions under study--changes the prevalence estimates of (a) Mexican Americans who report poor, fair, or good health status and (b) hypertension among Mexican Americans. PMID:8497577

8. A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

9. Leaf physiognomy and climate: A multivariate analysis

Davis, J. M.; Taylor, S. E.

1980-11-01

Research has demonstrated that leaf physiognomy is representative of the local or microclimate conditions under which plants grow. The physiognomy of leaf samples from Oregon, Michigan, Missouri, Tennessee, and the Panama Canal Zone has been related to the microclimate using Walter diagrams and Thornthwaite water-budget data. A technique to aid paleoclimatologists in identifying the nature of the microclimate from leaf physiognomy utilizes statistical procedures to classify leaf samples into one of six microclimate regimes based on leaf physiognomy information available from fossilized samples.

10. Bounding the bias of contrastive divergence learning.

PubMed

Fischer, Asja; Igel, Christian

2011-03-01

Optimization based on k-step contrastive divergence (CD) has become a common way to train restricted Boltzmann machines (RBMs). The k-step CD is a biased estimator of the log-likelihood gradient relying on Gibbs sampling. We derive a new upper bound for this bias. Its magnitude depends on k, the number of variables in the RBM, and the maximum change in energy that can be produced by changing a single variable. The last reflects the dependence on the absolute values of the RBM parameters. The magnitude of the bias is also affected by the distance in variation between the modeled distribution and the starting distribution of the Gibbs chain.

11. An attempt to target anxiety sensitivity via cognitive bias modification.

PubMed

Clerkin, Elise M; Beard, Courtney; Fisher, Christopher R; Schofield, Casey A

2015-01-01

Our goals in the present study were to test an adaptation of a Cognitive Bias Modification program to reduce anxiety sensitivity, and to evaluate the causal relationships between interpretation bias of physiological cues, anxiety sensitivity, and anxiety and avoidance associated with interoceptive exposures. Participants with elevated anxiety sensitivity who endorsed having a panic attack or limited symptom attack were randomly assigned to either an Interpretation Modification Program (IMP; n = 33) or a Control (n = 32) condition. During interpretation modification training (via the Word Sentence Association Paradigm), participants read short sentences describing ambiguous panic-relevant physiological and cognitive symptoms and were trained to endorse benign interpretations and reject threatening interpretations associated with these cues. Compared to the Control condition, IMP training successfully increased endorsements of benign interpretations and decreased endorsements of threatening interpretations at visit 2. Although self-reported anxiety sensitivity decreased from pre-selection to visit 1 and from visit 1 to visit 2, the reduction was not larger for the experimental versus control condition. Further, participants in IMP (vs. Control) training did not experience less anxiety and avoidance associated with interoceptive exposures. In fact, there was some evidence that those in the Control condition experienced less avoidance following training. Potential explanations for the null findings, including problems with the benign panic-relevant stimuli and limitations with the control condition, are discussed.

12. Maternal kin bias in affiliative behavior among wild adult female blue monkeys.

PubMed

Cords, Marina; Nikitopoulos, Eleni

2015-01-01

Kin-biased cooperative and affiliative behavior is widespread in social mammals and is expected to increase fitness. However, despite evolutionary benefits of cooperating with relatives, demographic circumstances may influence the strength of kin bias. We studied the relationship between maternal kinship and affiliative behavior among 78 wild adult female blue monkeys (Cercopithecus mitis) from 8 groups monitored for 1-5 years. We compared behavior and kinship matrices, controlling for rank differences. Using multivariate models, we examined effects of demographic variables on the extent to which females groomed disproportionately with close adult female kin. Female blue monkeys, like other cercopithecine primates, generally preferred closer maternal kin for grooming and spatial association, although there was also substantial variation. Kin bias was weakest for association (at 7 m) while feeding, intermediate for closer (1 m) association while resting, and most intense for grooming. Grooming kin bias was stronger when a female had more very close relatives (either her mother or daughters), when her group contained more adult females, when she groomed with a lower percentage of group-mates, and when she had fewer total kin. Dominance rank did not predict variation in kin bias. Females generally groomed with all kin, but in larger groups they increased the number of unrelated grooming partners and total grooming time. The increased kin bias intensity in larger groups resulted from the addition of unrelated partners with whom grooming occurred less often than with kin, rather than from time constraints that drove females to select kin more strongly. In natural-sized groups, it may be common that females groom with all their adult female kin, which are present in limited numbers. The addition of grooming partners in larger groups may benefit female blue monkeys who rely on collective action in territorial defense; group-wide cooperation may thus influence grooming

13. Using multivariate decoding to go beyond contrastive analyses in consciousness research.

PubMed

Sandberg, Kristian; Andersen, Lau M; Overgaard, Morten

2014-01-01

Contrasting conditions with and without awareness has been the preferred method for investigating the neural correlates of consciousness (NCC) for decades, yet recently it has been suggested that further insights can be made by moving beyond this method, specifically by meticulously controlling that potential precursors and consequences of the NCC are not mistaken for an NCC. Here, we briefly review the advantages and potential pitfalls of existing paradigms going beyond the contrastive method, and we propose multivariate decoding of neural activity patterns as a supplement to other methods. Specifically, we emphasize the ability of multivariate decoding to detect which patterns of neural activity are consistently predictive of conscious experiences at the single trial level. This is relevant as the "NCC proper" is expected to be consistently predictive whereas processes that are consequences of consciousness may not occur on every trial (making them less predictive) and prerequisites of consciousness may be present on some trials without conscious experience (making them less predictive).

14. SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

15. Faulty measurement substitution and control reconfiguration by using a multivariate flow control loop.

PubMed

Perillo, Sergio R P; Upadhyaya, Belle R; Hines, J Wesley

2014-03-01

A two-tank multivariate loop was designed and built to support research related to instrumentation and control, equipment and sensor monitoring. This test bed provides the framework necessary to investigate and test control strategies and fault detection methods applicable to sensors, equipment, and actuators, and was used to experimentally develop and demonstrate a fault-tolerant control strategy using six correlated variables in a single-tank configuration. This work shows the feasibility of using data-based empirical models to perform fault detection and substitute faulty measurements with predictions and to perform control reconfiguration in the presence of actuator failure in a real system. These experiments were particularly important because they offered the opportunity to prove that a system, such as the multivariate control loop, could survive degraded conditions, provided the empirical models used were accurate and representative of the process dynamics.

16. F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

1977-01-01

The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

17. Assessing Attentional Biases with Stuttering

ERIC Educational Resources Information Center

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

2016-01-01

Background: 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…

18. A game of two halves? Incentive incompatibility, starting point bias and the bidding game contingent valuation method.

PubMed

McNamee, Paul; Ternent, Laura; Gbangou, Adjima; Newlands, David

2010-01-01

The bidding game (BG) method of contingent valuation is one way to increase the precision of willingness to pay (WTP) estimates relative to the single dichotomous choice approach. However, there is evidence that the method may lead to incentive incompatible responses and be associated with starting point bias. While previous studies in health using BGs test for starting point bias, none have also investigated incentive incompatibility. Using a sample of respondents resident in Burkina Faso, West Africa, this paper examines whether the BG method is associated with both incentive incompatibility and starting point bias. We find evidence for both effects. However, average WTP values remained largely unaffected after accounting for both factors in multivariate analyses. The results suggest that the BG method is an acceptable technique in settings where prices for goods are flexible.

19. The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed