ibr: Iterative bias reduction multivariate smoothing
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.
A direct-gradient multivariate index of biotic condition
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.
Inferential Conditions in the Statistical Detection of Measurement Bias.
ERIC Educational Resources Information Center
Millsap, Roger E.; Meredith, William
1992-01-01
Inferential conditions in the statistical detection of measurement bias are discussed in the contexts of differential item functioning and predictive bias in educational and employment settings. It is concluded that bias measures that rely strictly on observed measures are not generally diagnostic of measurement bias or lack of bias. (SLD)
A multivariate multi-timescale quantile-quantile bias correction approach
NASA Astrophysics Data System (ADS)
Mehrotra, Raj; Sharma, Ashish
2015-04-01
A novel multivariate quantile-based nesting bias correction approach is developed for the removal of systematic biases in the global circulation model outputs at multiple time scales. Compared to the widely used quantile-matching method that is univariate, offers correction only at a single time scale of interest and considers only distributional biases, the proposed method simultaneously considers multiple variables, multiple time scales and in addition to the adjustment of the model CDF, also corrects for the biases in the lag-0 and lag-1 persistence attributes across all the time scales considered. The proposed methodology builds on our earlier works on nesting bias correction, which progressively corrects GCM simulations from lower to higher time scales to impart the observed distributional and persistence properties across the selected multiple time scales. The proposed approach combines the best of both quantile matching and nesting approaches and offers an improved basis for applying bias correction simultaneously on many variables across multiple time scales. The use of the approach in hydrology and water resources related downscaling applications is expected to have important consequences for the occurrence and intensity of extreme events such as heat waves, floods, and droughts. Being simple and versatile, the proposed approach can be used to produce auxiliary ensemble scenarios for various climate impact-oriented applications.
NASA Astrophysics Data System (ADS)
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.
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. PMID:21744100
Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis
NASA Astrophysics Data System (ADS)
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.
Stochastic bias from non-Gaussian initial conditions
Baumann, Daniel; Ferraro, Simone; Smith, Kendrick M.; Green, Daniel E-mail: sferraro@princeton.edu E-mail: kmsmith@astro.princeton.edu
2013-05-01
In this article, we show that a stochastic form of scale-dependent halo bias arises in multi-source inflationary models, where multiple fields determine the initial curvature perturbation. We derive this effect for general non-Gaussian initial conditions and study various examples, such as curvaton models and quasi-single field inflation. We present a general formula for both the stochastic and the non-stochastic parts of the halo bias, in terms of the N-point cumulants of the curvature perturbation at the end of inflation. At lowest order, the stochasticity arises if the collapsed limit of the four-point function is boosted relative to the square of the three-point function in the squeezed limit. We derive all our results in two ways, using the barrier crossing formalism and the peak-background split method. In a companion paper [1], we prove that these two approaches are mathematically equivalent.
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.
Novelty, conditioning and attentional bias to sexual rewards
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
Novelty, conditioning and attentional bias to sexual rewards.
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
Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means
Sabit, Hakilo; Al-Anbuky, Adnan
2014-01-01
Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495
Multivariate spatial condition mapping using subtractive fuzzy cluster means.
Sabit, Hakilo; Al-Anbuky, Adnan
2014-01-01
Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495
NASA Astrophysics Data System (ADS)
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.
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.
A multivariate conditional model for streamflow prediction and spatial precipitation refinement
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Zimroz, Radoslaw; Bartkowiak, Anna
2013-07-01
This paper deals with the diagnostics of planetary gearboxes under nonstationary operating conditions. In most diagnostics applications, energy of vibration signals (calculated directly from time series or extracted from spectral representation of signal) is used. Unfortunately energy based features are sensitive to load conditions and it makes diagnostics difficult. In this paper we used energy based 15D data vectors (namely spectral amplitudes of planetary mesh frequency and its harmonics) in order to investigate if it is possible to improve diagnostics efficiency in comparison to previous, one dimensional, approaches proposed for the same problem. Two multivariate methods, Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA), were used as techniques for data analysis. We used these techniques in order to investigate dimensionality of the data and to visualize data in 3D and 2D spaces in order to understand data behavior and assess classification ability. As a case study the data from two planetary gearboxes used in complex mining machines (one in bad condition and the other in good condition) were analyzed. For these two machines more than 2000 15D vectors were acquired. It should be noted that due to non-stationarity of loading conditions, previous diagnostics results obtained using other techniques were moderately good (ca. 80% recognition efficiency); however there is still some need to improve diagnostics classification ability. After application of the proposed approaches it was found that the entire data could be reduced to 2 dimensions whereby data instances became visible and a good discriminant function (characterized by a misclassification rate of .0023, i.e. only 5 erroneous classifications for a total of 2183 instances) could be derived. This paper suggests a novel way for condition monitoring of planetary gearboxes based on multivariate statistics. The emphasis is put on the algebraic and geometric interpretations of the PCA
NASA Astrophysics Data System (ADS)
Pajor, A.
2006-11-01
In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility (SV) models to describe the conditional correlations between stock index returns. We consider four tri-variate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example we study tri-variate volatility models for the daily log returns on the WIG, S&P 500, and FTSE 100 indexes. In order to formally compare the relative explanatory power of SV specifications we use the Bayesian principles of comparing statistic models. Our results are based on the Bayes factors and implemented through Markov Chain Monte Carlo techniques. The results indicate that the most adequate specifications are those that allow for time-varying conditional correlations and that have as many latent processes as there are conditional variances and covariances. The empirical results clearly show that the data strongly reject the assumption of constant conditional correlations.
Koga, R.; Penzin, S.H.; Crawford, K.B.; Crain, W.R.; Moss, S.C.; Pinkerton, S.D.; LaLumondiere, S.D.; Maher, M.C.
1997-12-01
The single event upset (SEU) sensitivity of certain types of linear microcircuits is strongly affected by bias conditions. For these devices, a model of upset mechanism and a method for SEU control have been suggested.
NASA Astrophysics Data System (ADS)
Scoccimarro, Román; Hui, Lam; Manera, Marc; Chan, Kwan Chuen
2012-04-01
We study the scale dependence of halo bias in generic (nonlocal) primordial non-Gaussian (PNG) initial conditions of the type motivated by inflation, parametrized by an arbitrary quadratic kernel. We first show how to generate nonlocal PNG initial conditions with minimal overhead compared to local PNG models for a general class of primordial bispectra that can be written as linear combinations of separable templates. We run cosmological simulations for the local, and nonlocal equilateral and orthogonal models and present results on the scale dependence of halo bias. We also derive a general formula for the Fourier-space bias using the peak-background split in the context of the excursion-set approach to halos and discuss the difference and similarities with the known corresponding result from local bias models. Our peak-background split bias formula generalizes previous results in the literature to include non-Markovian effects and nonuniversality of the mass function and are in better agreement with measurements in numerical simulations than previous results for a variety of halo masses, redshifts and halo definitions. We also derive for the first time quadratic bias results for arbitrary nonlocal PNG, and show that nonlinear bias loops give small corrections at large scales. The resulting well-behaved perturbation theory paves the way to constrain nonlocal PNG from measurements of the power spectrum and bispectrum in galaxy redshift surveys.
NASA Astrophysics Data System (ADS)
Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris
2013-04-01
Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be
Influence of growth conditions on exchange bias of NiMn-based spin valves
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.
Impact of bias conditions on electrical stress and ionizing radiation effects in Si-based TFETs
NASA Astrophysics Data System (ADS)
Ding, Lili; Gnani, Elena; Gerardin, Simone; Bagatin, Marta; Driussi, Francesco; Selmi, Luca; Royer, Cyrille Le; Paccagnella, Alessandro
2016-01-01
The interplay between electrical stress and ionizing radiation effects is experimentally investigated in Si-based Tunnel Field Effect Transistors (TFETs). In particular, the impact of bias conditions on the performance degradation is discussed. We found that the electrical stress effects in TFETs could not be ignored in radiation tests, since they can possibly overwhelm the radiation-induced degradation. Under this circumstance, the worst-case bias condition for studying radiation effects is not straightforward to be determined when there is an interplay between electrical stress and ionizing radiation effects.
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. PMID:10975177
Conditioning-induced attentional bias for face stimuli measured with the emotional Stroop task.
Lee, Tae-Ho; Lim, Seung-Lark; Lee, Kanghee; Kim, Hyun-Taek; Choi, June-Seek
2009-02-01
People with anxiety disorder display attentional bias toward threat-related objects. Using classical fear conditioning, the authors investigated the possible source of such bias in normal participants. Following differential fear conditioning in which an angry face of either male or female (conditioned stimulus: CS+) was paired with mild electric fingershock (unconditioned stimulus: US) but the angry face of the other gender and all other facial expressions unpaired (CS-), an emotional Stroop task was administered. In the Stroop task, participants were required to identify the color of the facial stimuli (red, green, blue, or yellow). Response latency was significantly longer for the CS+ angry face than the other unpaired facial stimuli (CS-). Furthermore, this acquired attentional bias was positively correlated with the level of trait-anxiety measured before the conditioning and the degree of self-reported aversiveness of the US. Our results demonstrated that attentional bias could be induced in normal individuals through a simple associative learning procedure, and the acquisition is modulated by the level of trait anxiety and the level of perceived fear of the aversive US. PMID:19186927
Exploration of Temporal ICD Coding Bias Related to Acute Diabetic Conditions
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
N-body simulations with generic non-Gaussian initial conditions II: halo bias
NASA Astrophysics Data System (ADS)
Wagner, Christian; Verde, Licia
2012-03-01
We present N-body simulations for generic non-Gaussian initial conditions with the aim of exploring and modelling the scale-dependent halo bias. This effect is evident on very large scales requiring large simulation boxes. In addition, the previously available prescription to implement generic non-Gaussian initial conditions has been improved to keep under control higher-order terms which were spoiling the power spectrum on large scales. We pay particular attention to the differences between physical, inflation-motivated primordial bispectra and their factorizable templates, and to the operational definition of the non-Gaussian halo bias (which has both a scale-dependent and an approximately scale-independent contributions). We find that analytic predictions for both the non-Gaussian halo mass function and halo bias work well once a fudge factor (which was introduced before but still lacks convincing physical explanation) is calibrated on simulations. The halo bias remains therefore an extremely promising tool to probe primordial non-Gaussianity and thus to give insights into the physical mechanism that generated the primordial perturbations. The simulation outputs and tables of the analytic predictions will be made publicly available via the non-Gaussian comparison project web site http://icc.ub.edu/~liciaverde/NGSCP.html.
Bias-reduced and separation-proof conditional logistic regression with small or sparse data sets.
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. PMID:20213709
Del Giudice, G; Padulano, R; Siciliano, D
2016-01-01
The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements. PMID:26901717
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. PMID:24036167
Condition bias of hunter-shot ring-necked ducks exposed to lead
McCracken, K.G.; Afton, A.D.; Peters, M.S.
2000-01-01
We evaluated the condition bias hypothesis for ring-necked ducks (Aythya collaris) exposed to lead by testing the null hypothesis that ducks shot by hunters do not differ in physiological condition from those collected randomly from the same location. After adjusting for structural body size and log(e) concentration of blood lead, we found that overall body condition differed significantly between collection types and age classes, and marginally between sexes. Ingesta-free body mass of ring-necked ducks sampled randomly averaged 8.8% greater than those shot over decoys, and 99% of this difference was accounted for by lipid reserves. Ingesta, ash, and protein did not differ between collection types; however, after-hatching-year (AHY) birds had 5.1% more ash and 4.8% more protein than did hatching-year (HY) birds. The only sex difference was that males had 4.1% more protein than did females. Ingesta-free body mass, lipids, and protein were negatively related to concentration of blood lead. Collection type-by-concentration of blood lead and age-by-sex-by-concentration of blood lead interactions were not significant. To the extent that lead pellets persist as a cause of disease or mortality, waterfowl biologists should account for lead exposure as a possible source of condition bias when estimating population parameters and modeling survival of ring-necked ducks and other waterfowl species prone to ingest lead. These findings further underscore the problem that ingested lead shotgun pellets pose for waterfowl.
NASA Astrophysics Data System (ADS)
Parat, Sylvie; Perdrix, Alain; Fricker-Hidalgo, Hélène; Saude, Isabelle; Grillot, Renee; Baconnier, Pierre
Heating, ventilation and air-conditioning (HVAC) may be responsible for the production and spread of airborne microorganisms in office buildings. In order to compare airborne microbiological flora in an air-conditioned building with that in a naturally ventilated building, eight sets of measurements were made over a 1-year period. Concurrently with other environmental measurements, air samples were collected in each building, from three offices and from the outdoor air, using the Andersen single-stage sampler. Three different media were used to culture fungi, staphylococci and mesophilic bacteria. Multivariate analysis revealed a group of offices more contaminated than others, and a marked seasonal variation in fungal concentrations. A comparison of mean levels of microorganisms measured in the two buildings showed that the air microbial content was significantly higher and more variable in the naturally ventilated building than in the air-conditioned building. Moreover, in the naturally ventilated building, the interior fungal content was strongly dependent on the outdoor content, while in the air-conditioned building fungal concentrations remained constant despite significant variations measured outside. This was confirmed by a statistical comparison of the correlation coefficients between indoor and outdoor concentrations. No difference was observed regarding gaseous pollutants and temperature, but relative humidity was significantly higher in the air-conditioned building. The effect of HVAC was to prevent the intake of outdoor particles and to dilute the indoor concentrations. These results are consistent with the presence of high-efficiency filters and a steam humidifier in the HVAC system under study.
NASA Astrophysics Data System (ADS)
Kim, D. W.; Kim, J. G.; Kim, A. R.; Park, M.; Yu, I. K.; Sim, K. D.; Kim, S. H.; Lee, S. J.; Cho, J. W.; Won, Y. J.
2010-11-01
The authors calculated the loss of the High Temperature Superconducting (HTS) model cable using Norris ellipse formula, and measured the loss of the model cable experimentally. Two kinds of measuring method are used. One is the electrical method, and the other is the calorimetric method. The electrical method can be used only in AC condition. But the calorimetric method can be used in both AC and DC bias conditions. In order to propose an effective measuring approach for Ripple Dependent Loss (RDL) under DC bias condition using the calorimetric method, Bismuth Strontium Calcium Copper Oxide (BSCCO) wires were used for the HTS model cable, and the SUS tapes were used as a heating tape to make the same pattern of the temperature profiles as in the electrical method without the transport current. The temperature-loss relations were obtained by the electrical method, and then applied to the calorimetric method by which the RDL under DC bias condition was well estimated.
NASA Astrophysics Data System (ADS)
Teutschbein, Claudia; Seibert, Jan
2014-05-01
Regional Climate Models (RCMs) are commonly used in climate-change impact studies to transfer large-scale Global Climate Model (GCM) values to smaller scales and to provide more detailed regional information. There is, however, the problem that RCM simulations often show considerable deviations from local observations due to systematic and random model errors. This issue has caused the development of several correction approaches, that can be classified according to their degree of complexity and include simple-to-apply methods such as linear transformations but also more advanced methods such as distribution mapping. Most of these common correction approaches are based on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this contribution, however, we show that it is possible to evaluate how well correction methods perform for conditions different from those that they were calibrated to. This can be done with the relatively simple differential split-sample test, originally proposed by Klemeš ["Operational testing of hydrological simulation models", Hydrological Sciences Journal 31, no. 1 (1986): 13-24]. For five Swedish catchments, precipitation and temperature time series from 15 different ERA40-driven RCM simulations were corrected with different commonly-used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for
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.
NASA Astrophysics Data System (ADS)
Kling, Harald; Klein, Bastian; Stanzel, Philipp
2013-04-01
A common approach for assessing future runoff conditions is to drive hydrological models with downscaled, bias corrected data of climate models. In a previous study for the Upper Danube basin, Kling et al. (2012) used a large ensemble of regional climate models to analyse the uncertainty in future runoff projections due to choice of climate model. In this follow-up study we extend this work by also examining the impact of the bias correction method. We apply five different bias correction methods to monthly precipitation and temperature data of 32 recently published regional climate models of the ENSEMBLES and CERA climate data bases. The bias correction methods include delta change, linear scaling, moment scaling, quantile mapping and a de-trended version of quantile mapping. This yields 160 different climate scenario sets for runoff modelling until the end of the 21st century, showing distinctive changes in mean annual runoff, seasonality in runoff, distribution of runoff and low flows of the Danube River. In general the uncertainty due to choice of climate model is considerably larger than due to choice of bias correction method. Only for climate models that perform exceptionally poor for simulation of historic climate, the choice of bias correction method becomes similarly important for future runoff simulations. Systematic differences - albeit smaller than between climate models - are found between two groups of bias correction methods. The climate change signals differ between these two groups after application of the bias correction. A detailed analysis of climate model error properties reveals for most climate models a problematic cross-correlation between projected trends in future climate and errors in historic climate variability. Thereby, the climate change signals in future mean annual temperature and precipitation are modified in frequency-based bias correction methods, such as the popular quantile mapping method. As there is no way of quantifying the
Park, Yeong-Shin; Lee, Yuna; Dang, Jeong-Jeung; Chung, Kyoung-Jae; Hwang, Y S
2014-02-01
Stability of an anode spot plasma, which is an additional high density plasma generated in front of a positively biased electrode immersed in ambient plasma, is a critical issue for its utilization to various types of ion sources. In this study, operating conditions for the generation of stable anode spot plasmas are experimentally investigated. Diagnostics of the bias current flowing into the positively biased electrode and the properties of ambient plasma reveal that unstable nature of the anode spot is deeply associated with the reduction of double layer potential between the anode spot plasma and the ambient plasma. It is found that stability of the anode spot plasma can be improved with increasing the ionization rate in ambient plasma so as to compensate the loss of electrons across the double layer or with enlarging the area of the biased electrode to prevent electron accumulation inside the anode spot. The results obtained from the present study give the guideline for operating conditions of anode spot plasmas as an ion source with high brightness. PMID:24593431
NASA Astrophysics Data System (ADS)
Durmaz, Murat; Karslioglu, Mahmut Onur
2015-04-01
There are various global and regional methods that have been proposed for the modeling of ionospheric vertical total electron content (VTEC). Global distribution of VTEC is usually modeled by spherical harmonic expansions, while tensor products of compactly supported univariate B-splines can be used for regional modeling. In these empirical parametric models, the coefficients of the basis functions as well as differential code biases (DCBs) of satellites and receivers can be treated as unknown parameters which can be estimated from geometry-free linear combinations of global positioning system observables. In this work we propose a new semi-parametric multivariate adaptive regression B-splines (SP-BMARS) method for the regional modeling of VTEC together with satellite and receiver DCBs, where the parametric part of the model is related to the DCBs as fixed parameters and the non-parametric part adaptively models the spatio-temporal distribution of VTEC. The latter is based on multivariate adaptive regression B-splines which is a non-parametric modeling technique making use of compactly supported B-spline basis functions that are generated from the observations automatically. This algorithm takes advantage of an adaptive scale-by-scale model building strategy that searches for best-fitting B-splines to the data at each scale. The VTEC maps generated from the proposed method are compared numerically and visually with the global ionosphere maps (GIMs) which are provided by the Center for Orbit Determination in Europe (CODE). The VTEC values from SP-BMARS and CODE GIMs are also compared with VTEC values obtained through calibration using local ionospheric model. The estimated satellite and receiver DCBs from the SP-BMARS model are compared with the CODE distributed DCBs. The results show that the SP-BMARS algorithm can be used to estimate satellite and receiver DCBs while adaptively and flexibly modeling the daily regional VTEC.
Wallace, Jack; Champagne, Pascale; Hall, Geof
2016-06-01
The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets. PMID:27038585
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.
Multivariate Analysis in Metabolomics
Worley, Bradley; Powers, Robert
2015-01-01
Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916
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…
NASA Astrophysics Data System (ADS)
Kaczmarek, Łukasz; Dobak, Paweł
2015-12-01
Localised landslide activity has been observed in the area of the plateau slope analysed, in the vicinity of the planned Warsaw Southern Ring Road. Using calculation models quantitative and qualitative evaluations of the impact of natural and anthropogenic load factors on slope stability (and hence, safety) are made. The present paper defines six stages of slope stability analysis, leading to an indication of optimum slope design in relation to the development planned. The proposed procedure produces a ranking of factors that affect slope stability. In the engineering geological conditions under consideration, the greatest factors impacting degradation and failure of slope stability are changes in soil strength due to local, periodic yielding and the presence of dynamic loads generated by intensification of road traffic. Calculation models were used to assess the impact of destabilisation factors and to obtain mutual equivalence with 3D-visualisation relations. Based on this methodology, various scenarios dedicated to specific engineering geological conditions can be developed and rapid stability evaluations of changing slope loads can be performed.
NASA Astrophysics Data System (ADS)
Pond, G. J.
2005-05-01
There is still debate on the strength of various data analysis tools for assessing biological condition in streams. This study compared two popular assessment approaches (multimetric index and RIVPACS-type O/E model) using macroinvertebrates from Kentucky streams. Data from 557 targeted and randomly selected sites (212 reference, 345 non-reference) sampled between 2000 and 2004 were used in this analysis. The Kentucky Macroinvertebrate Bioassessment Index (MBI) combines seven metrics (total generic richness, EPT generic richness, modified HBI, %Ephemeroptera, %EPT minus Cheumatopsyche, %midges+worms, and %clingers) that are scored by standardizing to the 95th or 5th percentile of the reference distribution and averaged. For comparison, three separate genus-level RIVPACS-type models were constructed (high-, low-, and mixed gradient streams) using four predictive variables (area, latitude, longitude, and week number) and taxa from reference sites. All 3 models preformed well but the low gradient model had the lowest precision. Assessments of non-reference sites based on MBI and O/E scores yielded similar results in terms of discrimination efficiency but the model based on mixed-gradient streams was the least sensitive. Using a subset of data from 84 headwater streams in the Appalachian region, MBI and O/E scores responded almost identically to stressors such as conductivity and habitat degradation.
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.
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.
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
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.
Chambless, Lloyd E; Davis, Vicki
2003-04-15
A simple general algorithm is described for correcting for bias caused by measurement error in independent variables in multivariate linear regression. This algorithm, using standard software, is then applied to several approaches to the analysis of change from baseline as a function of baseline value of the outcome measure plus other covariates, any of which might have measurement error. The algorithm may also be used when the independent variables differ by component of the multivariate independent variable. Simulations indicate that under various conditions bias is much reduced, as is mean squared error, and coverage of 95 per cent confidence intervals is good. PMID:12652553
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. PMID:25604449
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.
Hughes, D; Dodge, M A
1997-10-01
Although studies have described work processes among employed African American women, few have examined the influence of these processes on job outcomes. This study examined relationships between African American women's exposure to a range of occupational stressors, including two types of racial bias--institutional discrimination and interpersonal prejudice--and their evaluations of job quality. Findings indicated that institutional discrimination and interpersonal prejudice were more important predictors of job quality among these women than were other occupational stressors such as low task variety and decision authority, heavy workloads, and poor supervision. Racial bias in the workplace was most likely to be reported by workers in predominantly white work settings. In addition, Black women who worked in service, semiskilled, and unskilled occupations reported significantly more institutional discrimination, but not more interpersonal prejudice, than did women in professional, managerial, and technical occupations or those in sales and clerical occupations. PMID:9485575
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
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. PMID:25106739
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. PMID:25871169
Estimating the decomposition of predictive information in multivariate systems
NASA Astrophysics Data System (ADS)
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.
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. PMID:26005126
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. PMID:11752497
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…
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. PMID:26283086
Joint bias correction of temperature and precipitation in climate model simulations
NASA Astrophysics Data System (ADS)
Li, Chao; Sinha, Eva; Horton, Daniel E.; Diffenbaugh, Noah S.; Michalak, Anna M.
2014-12-01
Bias correction of meteorological variables from climate model simulations is a routine strategy for circumventing known limitations of state-of-the-art general circulation models. Although the assessment of climate change impacts often depends on the joint variability of multiple variables, commonly used bias correction methodologies treat each variable independently and do not consider the relationship among variables. Independent bias correction can therefore produce non-physical corrections and may fail to capture important multivariate relationships. Here, we introduce a joint bias correction methodology (JBC) and apply it to precipitation (P) and temperature (T) fields from the fifth phase of the Climate Model Intercomparison Project (CMIP5) model ensemble. This approach is based on a general bivariate distribution of P-T and can be seen as a multivariate extension of the commonly used univariate quantile mapping method. It proceeds by correcting either P or T first and then correcting the other variable conditional upon the first one, both following the concept of the univariate quantile mapping. JBC is shown to not only reduce biases in the mean and variance of P and T similarly to univariate quantile mapping, but also to correct model-simulated biases in P-T correlation fields. JBC, using methods such as the one presented here, thus represents an important step in impacts-based research as it explicitly accounts for inter-variable relationships as part of the bias correction procedure, thereby improving not only the individual distributions of P and T, but critically, their joint distribution.
Levy, David M; Peart, Sandra J
2008-06-01
We wish to deal with investigator bias in a statistical context. We sketch how a textbook solution to the problem of "outliers" which avoids one sort of investigator bias, creates the temptation for another sort. We write down a model of the approbation seeking statistician who is tempted by sympathy for client to violate the disciplinary standards. We give a simple account of one context in which we might expect investigator bias to flourish. Finally, we offer tentative suggestions to deal with the problem of investigator bias which follow from our account. As we have given a very sparse and stylized account of investigator bias, we ask what might be done to overcome this limitation. PMID:17925315
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.
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.
Roese, Neal J; Vohs, Kathleen D
2012-09-01
Hindsight bias occurs when people feel that they "knew it all along," that is, when they believe that an event is more predictable after it becomes known than it was before it became known. Hindsight bias embodies any combination of three aspects: memory distortion, beliefs about events' objective likelihoods, or subjective beliefs about one's own prediction abilities. Hindsight bias stems from (a) cognitive inputs (people selectively recall information consistent with what they now know to be true and engage in sensemaking to impose meaning on their own knowledge), (b) metacognitive inputs (the ease with which a past outcome is understood may be misattributed to its assumed prior likelihood), and (c) motivational inputs (people have a need to see the world as orderly and predictable and to avoid being blamed for problems). Consequences of hindsight bias include myopic attention to a single causal understanding of the past (to the neglect of other reasonable explanations) as well as general overconfidence in the certainty of one's judgments. New technologies for visualizing and understanding data sets may have the unintended consequence of heightening hindsight bias, but an intervention that encourages people to consider alternative causal explanations for a given outcome can reduce hindsight bias. PMID:26168501
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. PMID:25413792
Multivariable PID control by decoupling
NASA Astrophysics Data System (ADS)
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.
Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?
ERIC Educational Resources Information Center
Thompson, Bruce
Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…
NASA Astrophysics Data System (ADS)
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
A general, multivariate definition of causal effects in epidemiology.
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. PMID:25946227
A multivariate CAR model for mismatched lattices.
Porter, Aaron T; Oleson, Jacob J
2014-10-01
In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598
Lagrangian bias in the local bias model
Frusciante, Noemi; Sheth, Ravi K. E-mail: sheth@ictp.it
2012-11-01
It is often assumed that the halo-patch fluctuation field can be written as a Taylor series in the initial Lagrangian dark matter density fluctuation field. We show that if this Lagrangian bias is local, and the initial conditions are Gaussian, then the two-point cross-correlation between halos and mass should be linearly proportional to the mass-mass auto-correlation function. This statement is exact and valid on all scales; there are no higher order contributions, e.g., from terms proportional to products or convolutions of two-point functions, which one might have thought would appear upon truncating the Taylor series of the halo bias function. In addition, the auto-correlation function of locally biased tracers can be written as a Taylor series in the auto-correlation function of the mass; there are no terms involving, e.g., derivatives or convolutions. Moreover, although the leading order coefficient, the linear bias factor of the auto-correlation function is just the square of that for the cross-correlation, it is the same as that obtained from expanding the mean number of halos as a function of the local density only in the large-scale limit. In principle, these relations allow simple tests of whether or not halo bias is indeed local in Lagrangian space. We discuss why things are more complicated in practice. We also discuss our results in light of recent work on the renormalizability of halo bias, demonstrating that it is better to renormalize than not. We use the Lognormal model to illustrate many of our findings.
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
NASA Astrophysics Data System (ADS)
Sung, Yung-Ta; Devinney, Michael; Scharer, John
2013-10-01
The MadHeX experiment consists of a Pyrex tube connected to a stainless steel magnetic field expansion chamber (expansion ratio RE = 4.5) has been upgraded with an axial magnetic mirror field and an additional magnet in the transition region. This configuration enhances electron temperature and ionization fraction and minimizes neutral reflux. A half-turn double-helix antenna is used to excite electrostatic or inductive regime waves in the source. An ion beam of energy, E = 160 eV at 500 W RF power, has been observed in a low pressure (0.3 mtorr) argon plasma formed in the expansion region with a 340 G magnetic field with a R = 1.4 nozzle. The effects of upstream end plate boundary conditions on the plasma self-bias and ion beam acceleration are discussed. The effect of lower flow rates and pressures, higher RF powers (500 W-8 kW) and magnetic field strength dependence on the ion beam acceleration, plasma potential, electron density and temperature are explored. The axial ion velocity distribution function and temperatures at higher powers are observed by argon 668 nm laser induced fluorescence with density measurements obtained by mm wave interferometry. The EEDF and non-Maxwellian tail are examined using optical emission spectroscopy. Research supported by the University of Wisconsin-Madison.
Multivariate Regression with Calibration*
Liu, Han; Wang, Lie; Zhao, Tuo
2014-01-01
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861
Multivariate Data EXplorer (MDX)
Steed, Chad Allen
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.
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.
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.
Multivariate Intraclass Correlation.
ERIC Educational Resources Information Center
Wiley, David E.; Hawkes, Thomas H.
This paper is an explication of a statistical model which will permit an interpretable intraclass correlation coefficient that is negative, and a generalized extension of that model to cover a multivariate problem. The methodological problem has its practical roots in an attempt to find a statistic which could indicate the degree of similarity or…
Experimenter bias and subliminal perception
ERIC Educational Resources Information Center
Barber, Paul J.; Rushton, J. Philippe
1975-01-01
It has been suggested that subliminal perception phenomena may be in part due to experimenter bias effects. Two studies that obtained positive evidence of subliminal perception were therefore replicated with experimenters tested under blind and not blind conditions. (Editor)
Multivariate Data EXplorer (MDX)
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 wherebymore » 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.« less
Edelstein, Stuart J; Changeux, Jean-Pierre
2016-09-01
G-protein-coupled receptors (GPCRs) constitute a large group of integral membrane proteins that transduce extracellular signals from a wide range of agonists into targeted intracellular responses. Although the responses can vary depending on the category of G-proteins activated by a particular receptor, responses were also found to be triggered by interactions of the receptor with β-arrestins. It was subsequently discovered that for the same receptor molecule (e.g., the β-adrenergic receptor), some agonists have a propensity to specifically favor responses by G-proteins, others by β-arrestins, as has now been extensively studied. This feature of the GPCR system is known as biased agonism and is subject to various interpretations, including agonist-induced conformational change versus selective stabilization of preexisting active conformations. Here, we explore a complete allosteric framework for biased agonism based on alternative preexisting conformations that bind more strongly, but nonexclusively, either G-proteins or β-arrestins. The framework incorporates reciprocal effects among all interacting molecules. As a result, G-proteins and β-arrestins are in steric competition for binding to the cytoplasmic surface of either the G-protein-favoring or β-arrestin-favoring GPCR conformation. Moreover, through linkage relations, the strength of the interactions of G-proteins or β-arrestins with the corresponding active conformation potentiates the apparent affinity for the agonist, effectively equating these two proteins to allosteric modulators. The balance between response alternatives can also be influenced by the physiological concentrations of either G-proteins or β-arrestins, as well as by phosphorylation or interactions with positive or negative allosteric modulators. The nature of the interactions in the simulations presented suggests novel experimental tests to distinguish more fully among alternative mechanisms. PMID:27602718
Joint bias correction of temperature and precipitation in climate model simulations
NASA Astrophysics Data System (ADS)
Li, C.; Michalak, A. M.; Sinha, E.; Horton, D. E.; Diffenbaugh, N. S.
2014-12-01
Bias correction of meteorological variables from climate model simulations is a routine strategy for circumventing known limitations of state-of-the-art general circulation models. Although the assessment of climate change impacts often depends on the joint variability of multiple variables, commonly used bias correction methodologies treat each variable independently, and do not consider the relationship among variables. Independent bias correction can therefore produce non-physical corrections and may fail to capture important multivariate relationships. Here, we introduce a joint bias correction methodology (JBC) and apply it to precipitation (P) and temperature (T) fields from the CMIP5 model ensemble. This approach is based on a general bivariate distribution of P-T, and can be seen as a multivariate extension of the commonly used univariate quantile mapping method. It proceeds by correcting either P or T first and then correcting the other variable conditional upon the first one, both following the concept of the univariate quantile mapping. JBC is shown to reduce model-simulated biases in P-T correlation fields, as well as biases in the mean and variance of P and T. In addition, it overcomes a noted problem with an existing joint P-T correction method, namely that this earlier approach did not yield appreciable improvements in P-T correlation coefficients. JBC, using methods such as the one presented here, thus represents an important step in impacts-based research as it explicitly accounts for inter-variable relationships as part of the bias correction procedure, thereby improving not only the individual distributions of P and T, but critically, their joint distribution.
Multivariate respiratory motion prediction
NASA Astrophysics Data System (ADS)
Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.
2014-10-01
In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.
Introduction to multivariate discrimination
NASA Astrophysics Data System (ADS)
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
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.
Primer on multivariate calibration
Thomas, E.V. )
1994-08-01
In analytical chemistry, calibration is the procedure that relates instrumental measurements to an analyte of interest. Typically, instrumental measurements are obtained from specimens in which the amount (or level) of the analyte has been determined by some independent and inherently accurate assay (e.g., wet chemistry). Together, the instrumental measurements and results from the independent assays are used to construct a model that relates the analyte level to the instrumental measurements. The advent of high-speed digital computers has greatly increased data acquisition and analysis capabilities and has provided the analytical chemist with opportunities to use many measurements - perhaps hundreds - for calibrating an instrument (e.g., absorbances at multiple wave-lengths). To take advantage of this technology, however, new methods (i.e., multivariate calibration methods) were needed for analyzing and modeling the experimental data. The purpose of this report is to introduce several evolving multivariate calibration methods and to present some important issues regarding their use. 30 refs., 7 figs.
System identification for multivariable control
NASA Astrophysics Data System (ADS)
Vanzee, G. A.
1981-05-01
System identification methods and modern control theory are applied to industrial processes. These processes must often be controlled in order to meet certain requirements with respect to the product quality, safety, energy consumption, and environmental load. Modern control system design methods which take the occurring interaction phenomena and stochastic disturbances into account are used. An accurate dynamic mathematical model of the process, by theoretical modelling and/or by system identification is obtained. The computational aspects of two important types of identifications methods, i.e., stochastic realization and prediction error based parameter estimation are studied. The studied computational aspects are the robustness, the accuracy, and the computational costs of the methods. Theoretical analyses and applications to a multivariable pilot scale process, operating under closed loop conditions are investigated.
Multivariate Hypergeometric Similarity Measure
Kaddi, Chanchala D.; Parry, R. Mitchell; Wang, May D.
2016-01-01
We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets. PMID:24407308
Greenwald, Anthony G.
2012-01-01
Objectives. We examined the association between pediatricians’ attitudes about race and treatment recommendations by patients’ race. Methods. We conducted an online survey of academic pediatricians (n = 86). We used 3 Implicit Association Tests to measure implicit attitudes and stereotypes about race. Dependent variables were recommendations for pain management, urinary tract infections, attention deficit hyperactivity disorder, and asthma, measured by case vignettes. We used correlational analysis to assess associations among measures and hierarchical multiple regression to measure the interactive effect of the attitude measures and patients’ race on treatment recommendations. Results. Pediatricians’ implicit (unconscious) attitudes and stereotypes were associated with treatment recommendations. The association between unconscious bias and patient’s race was statistically significant for prescribing a narcotic medication for pain following surgery. As pediatricians’ implicit pro-White bias increased, prescribing narcotic medication decreased for African American patients but not for the White patients. Self-reported attitudes about race were associated with some treatment recommendations. Conclusions. Pediatricians’ implicit attitudes about race affect pain management. There is a need to better understand the influence of physicians’ unconscious beliefs about race on pain and other areas of care. PMID:22420817
MacNab, Ying C
2016-09-20
We present a general coregionalization framework for developing coregionalized multivariate Gaussian conditional autoregressive (cMCAR) models for Bayesian analysis of multivariate lattice data in general and multivariate disease mapping data in particular. This framework is inclusive of cMCARs that facilitate flexible modelling of spatially structured symmetric or asymmetric cross-variable local interactions, allowing a wide range of separable or non-separable covariance structures, and symmetric or asymmetric cross-covariances, to be modelled. We present a brief overview of established univariate Gaussian conditional autoregressive (CAR) models for univariate lattice data and develop coregionalized multivariate extensions. Classes of cMCARs are presented by formulating precision structures. The resulting conditional properties of the multivariate spatial models are established, which cast new light on cMCARs with richly structured covariances and cross-covariances of different spatial ranges. The related methods are illustrated via an in-depth Bayesian analysis of a Minnesota county-level cancer data set. We also bring a new dimension to the traditional enterprize of Bayesian disease mapping: estimating and mapping covariances and cross-covariances of the underlying disease risks. Maps of covariances and cross-covariances bring to light spatial characterizations of the cMCARs and inform on spatial risk associations between areas and diseases. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27091685
Assessment of bias for MRI diffusion tensor imaging using SIMEX.
Lauzon, Carolyn B; Asman, Andrew J; Crainiceanu, Ciprian; Caffo, Brian C; Landman, Bennett A
2011-01-01
Diffusion Tensor Imaging (DTI) is a Magnetic Resonance Imaging method for measuring water diffusion in vivo. One powerful DTI contrast is fractional anisotropy (FA). FA reflects the strength of water's diffusion directional preference and is a primary metric for neuronal fiber tracking. As with other DTI contrasts, FA measurements are obscured by the well established presence of bias. DTI bias has been challenging to assess because it is a multivariable problem including SNR, six tensor parameters, and the DTI collection and processing method used. SIMEX is a modem statistical technique that estimates bias by tracking measurement error as a function of added noise. Here, we use SIMEX to assess bias in FA measurements and show the method provides; i) accurate FA bias estimates, ii) representation of FA bias that is data set specific and accessible to non-statisticians, and iii) a first time possibility for incorporation of bias into DTI data analysis. PMID:21995019
Distraction from emotional information reduces biased judgements.
Lench, Heather C; Bench, Shane W; Davis, Elizabeth L
2016-06-01
Biases arising from emotional processes are some of the most robust behavioural effects in the social sciences. The goal of this investigation was to examine the extent to which the emotion regulation strategy of distraction could reduce biases in judgement known to result from emotional information. Study 1 explored lay views regarding whether distraction is an effective strategy to improve decision-making and revealed that participants did not endorse this strategy. Studies 2-5 focused on several established, robust biases that result from emotional information: loss aversion, desirability bias, risk aversion and optimistic bias. Participants were prompted to divert attention away from their feelings while making judgements, and in each study this distraction strategy resulted in reduced bias in judgement relative to control conditions. The findings provide evidence that distraction can improve choice across several situations that typically elicit robustly biased responses, even though participants are not aware of the effectiveness of this strategy. PMID:25787937
Recursive bias estimation for high dimensional smoothers
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.
Solar array/spacecraft biasing
NASA Technical Reports Server (NTRS)
Fitzgerald, D. J.
1981-01-01
Biasing techniques and their application to the control of spacecraft potential is discussed. Normally when a spacecraft is operated with ion thrusters, the spacecraft will be 10-20 volts negative of the surrounding plasma. This will affect scientific measurements and will allow ions from the charge-exchange plasma to bombard the spacecraft surfaces with a few tens of volts of energy. This condition may not be tolerable. A proper bias system is described that can bring the spacecraft to or near the potential of the surrounding plasma.
ERIC Educational Resources Information Center
Bond, Lloyd
1981-01-01
While some forms of test bias (for example, bias in selection and prediction) appear amenable to definitional consensus, a definition of cultural bias will remain problematic so long as it is confused with the nature/nurture issue. (Author/BW)
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'…
ERIC Educational Resources Information Center
Malouff, John
2008-01-01
Bias in grading can be conscious or unconscious. The author describes different types of bias, such as those based on student attractiveness or performance in prior courses, and a variety of methods of reducing bias, including keeping students anonymous during grading and using detailed criteria for subjective grading.
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…
Scülfort, Stefanie A; Bartsch, Dusan; Enkel, Thomas
2016-11-01
Dopamine's (DA) role in reward-processing is currently discussed as either providing a teaching signal to guide learning or mediating the transfer of incentive salience (i.e. motivational aspects) from unconditioned stimuli (US) to conditioned stimuli (CS). We used a Pavlovian conditioned approach (PCA) procedure to further investigate DAs contribution to these processes. Experiment 1 assessed the acquisition of PCA to a manipulable lever cue for 7days under DA-blockade with Flupenthixol (FLU; 225μg/kg) or Saline (SAL) treatment, followed by 6-days off-drug testing. FLU decreased the number of conditioned responses (CR) during the treatment phase, but cessation of treatment resulted in an immediate increase in CR to levels comparable to SAL controls; notably, CR in FLU-treated rats were restricted to goal tracking behaviour. During continued off-drug testing, rats from the FLU group developed sign tracking with a similar temporal pattern as controls. In experiment 2, acquisition of PCA to a non-manipulable auditory cue was investigated. FLU reduced the number of CR during treatment, and removing DA antagonism resulted in a similar rapid increase of CR as seen in experiment 1. These data complement other reports by demonstrating that, independently from the physical properties of the CS, DA is not required for learning predictive aspects of a CS-US relationship but for the development of behaviour (namely sign tracking) which is based on the motivational aspects of a CS-US relationship. PMID:27478141
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.
Selection bias in rheumatic disease research
Choi, Hyon K.; Nguyen, Uyen-Sa; Niu, Jingbo; Danaei, Goodarz; Zhang, Yuqing
2014-01-01
The identification of modifiable risk factors for the development of rheumatic conditions and their sequelae is crucial for reducing the substantial worldwide burden of these diseases. However, the validity of such research can be threatened by sources of bias, including confounding, measurement and selection biases. In this Review, we discuss potentially major issues of selection bias—a type of bias frequently overshadowed by other bias and feasibility issues, despite being equally or more problematic—in key areas of rheumatic disease research. We present index event bias (a type of selection bias) as one of the potentially unifying reasons behind some unexpected findings, such as the ‘risk factor paradox’—a phenomenon exemplified by the discrepant effects of certain risk factors on the development versus the progression of osteoarthritis (OA) or rheumatoid arthritis (RA). We also discuss potential selection biases owing to differential loss to follow-up in RA and OA research, as well as those due to the depletion of susceptibles (prevalent user bias) and immortal time bias. The lesson remains that selection bias can be ubiquitous and, therefore, has the potential to lead the field astray. Thus, we conclude with suggestions to help investigators avoid such issues and limit the impact on future rheumatology research. PMID:24686510
(Anti)symmetric multivariate trigonometric functions and corresponding Fourier transforms
NASA Astrophysics Data System (ADS)
Klimyk, A.; Patera, J.
2007-09-01
Four families of special functions, depending on n variables, are studied. We call them symmetric and antisymmetric multivariate sine and cosine functions. They are given as determinants or antideterminants of matrices, whose matrix elements are sine or cosine functions of one variable each. These functions are eigenfunctions of the Laplace operator, satisfying specific conditions at the boundary of a certain domain F of the n-dimensional Euclidean space. Discrete and continuous orthogonality on F of the functions within each family allows one to introduce symmetrized and antisymmetrized multivariate Fourier-like transforms involving the symmetric and antisymmetric multivariate sine and cosine functions.
2013-01-01
Background Weight bias is widespread and has numerous harmful consequences. The internalization of weight bias has been associated with significant psychological impairment. Other forms of discrimination, such as racial and anti-gay bias, have been shown to be associated with physical health impairment. However, research has not yet examined whether internalized weight bias is associated with physical as well as psychological impairment in health-related quality of life. Methods Participants included 120 treatment-seeking overweight and obese adults (mean body mass index = 35.09; mean age = 48.31; 68% female; 59% mixed or Asian ethnicity). Participants were administered measures of internalized weight bias and physical and mental health-related quality of life, and they were assessed for the presence of chronic medical conditions, use of prescription and non-prescription medications, and current exercise. Results Internalized weight bias was significantly correlated with health impairment in both physical (r = −.25) and mental (r = −.48) domains. In multivariate analyses controlling for body mass index, age, and other physical health indicators, internalized weight bias significantly and independently predicted impairment in both physical (β = −.31) and mental (β = −.47) health. Conclusions Internalized weight bias was associated with greater impairment in both the physical and mental domains of health-related quality of life. Internalized weight bias also contributed significantly to the variance in physical and mental health impairment over and above the contributions of BMI, age, and medical comorbidity. Consistent with the association between prejudice and physical health in other minority groups, these findings suggest a link between the effects of internalized weight-based discrimination and physical health. Research is needed on strategies to prevent weight bias and its internalization on both a societal and individual level. PMID:24764526
Adaptable history biases in human perceptual decisions.
Abrahamyan, Arman; Silva, Laura Luz; Dakin, Steven C; Carandini, Matteo; Gardner, Justin L
2016-06-21
When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject's default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics. PMID:27330086
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)
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…
A "Model" Multivariable Calculus Course.
ERIC Educational Resources Information Center
Beckmann, Charlene E.; Schlicker, Steven J.
1999-01-01
Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…
NASA Astrophysics Data System (ADS)
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.
Pettersson, E; Larsson, H; Lichtenstein, P
2016-05-01
Recent studies have shown that different mental-health problems appear to be partly influenced by the same set of genes, which can be summarized by a general genetic factor. To date, such studies have relied on surveys of community-based samples, which could introduce potential biases. The goal of this study was to examine whether a general genetic factor would still emerge when based on a different ascertainment method with different biases from previous studies. We targeted all adults in Sweden (n=3 475 112) using national registers and identified those who had received one or more psychiatric diagnoses after seeking or being forced into mental health care. In order to examine the genetic versus environmental etiology of the general factor, we examined whether participants' full- or half-siblings had also received diagnoses. We focused on eight major psychiatric disorders based on the International Classification of Diseases, including schizophrenia, schizoaffective disorder, bipolar disorder, depression, anxiety, attention-deficit/hyperactivity disorder, alcohol use disorder and drug abuse. In addition, we included convictions of violent crimes. Multivariate analyses demonstrated that a general genetic factor influenced all disorders and convictions of violent crimes, accounting for between 10% (attention-deficit/hyperactivity disorder) and 36% (drug abuse) of the variance of the conditions. Thus, a general genetic factor of psychopathology emerges when based on both surveys as well as national registers, indicating that a set of pleiotropic genes influence a variety of psychiatric disorders. PMID:26303662
Context, engagement, and the (multiple) functions of negativity bias.
Federico, Christopher M; Johnston, Christopher D; Lavine, Howard G
2014-06-01
Hibbing and colleagues argue that political attitudes may be rooted in individual differences in negativity bias. Here, we highlight the complex, conditional nature of the relationship between negativity bias and ideology by arguing that the political impact of negativity bias should vary as a function of (1) issue domain and (2) political engagement. PMID:24970433
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.
Multichannel hierarchical image classification using multivariate copulas
NASA Astrophysics Data System (ADS)
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.
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.
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…
Remote Impact of Extratropical Thermal Bias on Tropical Biases in the Norwegian Earth System Model
NASA Astrophysics Data System (ADS)
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
Biased predecision processing.
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. PMID:12848220
Harassment, Bias, and Discrimination.
ERIC Educational Resources Information Center
Welliver, Paul W.
1995-01-01
Discusses a new principle which has been added to the AECT (Association for Educational Communications and Technology) Code of Professional Ethics regarding discrimination, harassment, and bias. An example is presented which illustrates a violation of a professional colleague's rights. (LRW)
Introduction to Unconscious Bias
NASA Astrophysics Data System (ADS)
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.
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.
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.
Ditto, Peter H; Wojcik, Sean P; Chen, Eric Evan; Grady, Rebecca Hofstein; Ringel, Megan M
2015-01-01
Duarte et al. are right to worry about political bias in social psychology but they underestimate the ease of correcting it. Both liberals and conservatives show partisan bias that often worsens with cognitive sophistication. More non-liberals in social psychology is unlikely to speed our convergence upon the truth, although it may broaden the questions we ask and the data we collect. PMID:26786070
A semiparametric multivariate and multisite weather generator
NASA Astrophysics Data System (ADS)
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.
Leinenger, Mallorie; Rayner, Keith
2013-01-01
Readers experience processing difficulties when reading biased homographs preceded by subordinate-biasing contexts. Attempts to overcome this processing deficit have often failed to reduce the subordinate bias effect (SBE). In the present studies, we examined the processing of biased homographs preceded by single-sentence, subordinate-biasing contexts, and varied whether this preceding context contained a prior instance of the homograph or a control word/phrase. Having previously encountered the homograph earlier in the sentence reduced the SBE for the subsequent encounter, while simply instantiating the subordinate meaning produced processing difficulty. We compared these reductions in reading times to differences in processing time between dominant-biased repeated and non-repeated conditions in order to verify that the reductions observed in the subordinate cases did not simply reflect a general repetition benefit. Our results indicate that a strong, subordinate-biasing context can interact during lexical access to overcome the activation from meaning frequency and reduce the SBE during reading. PMID:24073328
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.
MacNab, Ying C
2016-08-01
This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. PMID:27566769
Biases in Visual, Auditory, and Audiovisual Perception of Space
Odegaard, Brian; Wozny, David R.; Shams, Ladan
2015-01-01
Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1) if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors), and (2) whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli). Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers) was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s) driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only improves the
Prediction Bias and Selection Bias: An Empirical Analysis.
ERIC Educational Resources Information Center
Cahan, Sorel; Gamliel, Eyal
2001-01-01
Proposed a definition of selection bias and studied the empirical relation between prediction bias and selection bias with respect to prominent social groups. Results show that, although the two biases are related, the relation is not isomorphic. It is mediated by the selection ratio, and for most selection ratios, it is only moderate. (SLD)
Multivariate meta-analysis using individual participant data.
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. PMID:26099484
Residual bias in a multiphase flow model calibration and prediction
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.
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.
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. PMID:26164254
Galaxy bias and primordial non-Gaussianity
NASA Astrophysics Data System (ADS)
Assassi, Valentin; Baumann, Daniel; Schmidt, Fabian
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.
Bias in Psychological Assessment: Heterosexism.
ERIC Educational Resources Information Center
Chernin, Jeffrey; Holden, Janice Miner; Chandler, Cynthia
1997-01-01
Explores heterosexist bias in seven widely used assessment instruments. Focuses on bias that is observable in the instruments themselves and in the ancillary materials. Describes three types of bias, how these biases manifest in various instruments, and makes recommendations for mental health practitioners and for professionals who develop…
Method of multivariate spectral analysis
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).
Multivariable Burchnall-Chaundy theory.
Previato, Emma
2008-03-28
Burchnall & Chaundy (Burchnall & Chaundy 1928 Proc. R. Soc. A 118, 557-583) classified the (rank 1) commutative subalgebras of the algebra of ordinary differential operators. To date, there is no such result for several variables. This paper presents the problem and the current state of the knowledge, together with an interpretation in differential Galois theory. It is known that the spectral variety of a multivariable commutative ring will not be associated to a KP-type hierarchy of deformations, but examples of related integrable equations were produced and are reviewed. Moreover, such an algebro-geometric interpretation is made to fit into A.N. Parshin's newer theory of commuting rings of partial pseudodifferential operators and KP-type hierarchies which uses higher local fields. PMID:17588865
Comparing G: multivariate analysis of genetic variation in multiple populations.
Aguirre, J D; Hine, E; McGuigan, K; Blows, M W
2014-01-01
The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design. PMID:23486079
ERIC Educational Resources Information Center
Sebastian-Galles, Nuria
2007-01-01
Some recent publications that explore the foundations of early language development are reviewed in this article. The review adopts the pivotal idea that infants' advancements are helped by the existence of different types of biases. The infant's discovery of the phonological properties of the language of the environment, as well as their learning…
Anderson, D.Z.; Chow, W.W.; Scully, M.O.; Sanders, V.E.
1980-10-01
We describe a four-mode ring laser that exhibits none of the mode-locking characteristics that plague laser gyros. This laser is characterized by a bias that changes sign with a change in the direction of rotation and prevents the counterpropagating modes from locking. A theoretical analysis explaining the experimental results is outlined.
Multivariate Time Series Similarity Searching
Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng
2014-01-01
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665
Multivariate time series similarity searching.
Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng
2014-01-01
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665
NASA Astrophysics Data System (ADS)
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
The Psychological Price of Media Bias
ERIC Educational Resources Information Center
Babad, Elisha
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…
Multivariate pluvial flood damage models
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.
Negativity bias and basic values.
Schwartz, Shalom H
2014-06-01
Basic values explain more variance in political attitudes and preferences than other personality and sociodemographic variables. The values most relevant to the political domain are those likely to reflect the degree of negativity bias. Value conflicts that represent negativity bias clarify differences between what worries conservatives and liberals and suggest that relations between ideology and negativity bias are linear. PMID:24970450
Assessing Bias in Search Engines.
ERIC Educational Resources Information Center
Mowshowitz, Abbe; Kawaguchi, Akira
2002-01-01
Addresses the measurement of bias in search engines on the Web, defining bias as the balance and representation of items in a collection retrieved from a database for a set of queries. Assesses bias by measuring the deviation from the ideal of the distribution produced by a particular search engine. (Author/LRW)
Inclusion of Dominance Effects in the Multivariate GBLUP Model
Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Von Pinho, Renzo Garcia
2016-01-01
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056
Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data
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
Inclusion of Dominance Effects in the Multivariate GBLUP Model.
Dos Santos, Jhonathan Pedroso Rigal; Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Balestre, Marcio; Von Pinho, Renzo Garcia
2016-01-01
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056
A novel bias correction methodology for climate impact simulations
NASA Astrophysics Data System (ADS)
Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Thonicke, K.; Mahecha, M. D.
2016-02-01
Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere-atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes.
Test Bias and the Elimination of Racism
ERIC Educational Resources Information Center
Sedlacek, William E.
1977-01-01
Three types of test bias are discussed: content bias, atmosphere bias, and use bias. Use bias is considered the most important. Tests reflect the bias in society, and eliminating test bias means eliminating racism and sexism in society. A six-stage model to eliminate racism and sexism is presented. (Author)
The distinct effects of internalizing weight bias: An experimental study.
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. PMID:26927688
Audibility and visual biasing in speech perception
NASA Astrophysics Data System (ADS)
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
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…
Recognition bias and the physical attractiveness stereotype.
Rohner, Jean-Christophe; Rasmussen, Anders
2012-06-01
Previous studies have found a recognition bias for information consistent with the physical attractiveness stereotype (PAS), in which participants believe that they remember that attractive individuals have positive qualities and that unattractive individuals have negative qualities, regardless of what information actually occurred. The purpose of this research was to examine whether recognition bias for PAS congruent information is replicable and invariant across a variety of conditions (i.e. generalizable). The effects of nine different moderator variables were examined in two experiments. With a few exceptions, the effect of PAS congruence on recognition bias was independent of the moderator variables. The results suggest that the tendency to believe that one remembers information consistent with the physical attractiveness stereotype is a robust phenomenon. PMID:22416805
Multivariate models of adult Pacific salmon returns.
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
Multivariate Models of Adult Pacific Salmon Returns
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
Evaluating solutions to sponsorship bias.
Doucet, M; Sismondo, S
2008-08-01
More than 40 primary studies, and three recent systematic reviews and meta-analyses, have shown a clear association between pharmaceutical industry funding of clinical trials and pro-industry results. Industry sponsorship biases published scientific research in favour of the sponsors, a result of the strong interest commercial sponsors have in obtaining favourable results. Three proposed remedies to this problem are widely agreed upon among those concerned with the level of sponsorship bias: financial disclosure, reporting standards and trial registries. This paper argues that all of these remedies either fail to address the mechanisms by which pharmaceutical companies' sponsorship leads to biased results-design bias, multiple trials with predictable outcomes, fraud, rhetorical effects and publication bias-or else only inadequately address those mechanisms. As a result, the policies normally proposed for dealing with sponsorship bias are unable to eliminate it. Only completely separating public clinical research from pharmaceutical industry funding can eliminate sponsorship bias. PMID:18667655
NASA Astrophysics Data System (ADS)
Malakar, N. K.; Lary, D. J.; Gencaga, D.; Albayrak, A.; Wei, J.
2013-08-01
Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program.
CLUSTERING CRITERIA AND MULTIVARIATE NORMAL MIXTURES
New clustering criteria for use when a mixture of multivariate normal distributions is an appropriate model are presented. They are derived from maximum likelihood and Bayesian approaches corresponding to different assumptions about the covariance matrices of the mixture componen...
A Course in... Multivariable Control Methods.
ERIC Educational Resources Information Center
Deshpande, Pradeep B.
1988-01-01
Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)
Multivariate data analysis of proteome data.
Engkilde, Kåre; Jacobsen, Susanne; Søndergaard, Ib
2007-01-01
We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image processing software, transfer of data, multivariate data analysis, interpretation of the results, and finally we return to biology. PMID:17093312
Auditory perception bias in speech imitation
Postma-Nilsenová, Marie; Postma, Eric
2013-01-01
In an experimental study, we explored the role of auditory perception bias in vocal pitch imitation. Psychoacoustic tasks involving a missing fundamental indicate that some listeners are attuned to the relationship between all the higher harmonics present in the signal, which supports their perception of the fundamental frequency (the primary acoustic correlate of pitch). Other listeners focus on the lowest harmonic constituents of the complex sound signal which may hamper the perception of the fundamental. These two listener types are referred to as fundamental and spectral listeners, respectively. We hypothesized that the individual differences in speakers' capacity to imitate F0 found in earlier studies, may at least partly be due to the capacity to extract information about F0 from the speech signal. Participants' auditory perception bias was determined with a standard missing fundamental perceptual test. Subsequently, speech data were collected in a shadowing task with two conditions, one with a full speech signal and one with high-pass filtered speech above 300 Hz. The results showed that perception bias toward fundamental frequency was related to the degree of F0 imitation. The effect was stronger in the condition with high-pass filtered speech. The experimental outcomes suggest advantages for fundamental listeners in communicative situations where F0 imitation is used as a behavioral cue. Future research needs to determine to what extent auditory perception bias may be related to other individual properties known to improve imitation, such as phonetic talent. PMID:24204361
Multivariate Longitudinal Analysis with Bivariate Correlation Test.
Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory
2016-01-01
In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692
Multivariate Longitudinal Analysis with Bivariate Correlation Test
Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory
2016-01-01
In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692
Leyrat, C; Caille, A; Donner, A; Giraudeau, B
2013-08-30
Cluster randomized trials (CRTs) are often prone to selection bias despite randomization. Using a simulation study, we investigated the use of propensity score (PS) based methods in estimating treatment effects in CRTs with selection bias when the outcome is quantitative. Of four PS-based methods (adjustment on PS, inverse weighting, stratification, and optimal full matching method), three successfully corrected the bias, as did an approach using classical multivariable regression. However, they showed poorer statistical efficiency than classical methods, with higher standard error for the treatment effect, and type I error much smaller than the 5% nominal level. PMID:23553813
Fundamental elements of vector enhanced multivariance product representation
NASA Astrophysics Data System (ADS)
Kalay, Berfin; Demiralp, Metin
2012-09-01
A new version of High Dimensional Model Representation (HDMR) is presented in this work. Vector HDMR has been quite recently developed to deal with the decomposition of vector valued multivariate functions. It was an extension from scalars to vectors by possibly using matrix weights. However, that expansion is based on an ascending multivariance starting from a constant term via a set of appropriately imposed conditions which can be related to orthogonality in a conveniently chosen Hilbert space. This work adds more flexibility by introducing certain matrix valued univariate support functions. We assume weight matrices proportional to unit matrices. This work covers only the basic issues related to the fundamental elements of the new approach.
Multivariable control altitude demonstration on the F100 turbofan engine
NASA Technical Reports Server (NTRS)
Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.
1979-01-01
The control system designed under the Multivariable Control Synthesis (MVCS) program for the F100 turbofan engine is described. The MVCS program, applied the linear quadratic regulator (LQR) synthesis methods in the design of a multivariable engine control system to obtain enhanced performance from cross-coupled controls, maximum use of engine variable geometry, and a systematic design procedure that can be applied efficiently to new engine systems. Basic components of the control system, a reference value generator for deriving a desired equilibrium state and an approximate control vector, a transition model to produce compatible reference point trajectories during gross transients, gain schedules for producing feedback terms appropriate to the flight condition, and integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance are described and the details of the F100 implementation presented. The engine altitude test phase of the MVCS program, and engine responses in a variety of test operating points and power transitions are presented.
Multivariable control altitude demonstration on the F100 turbofan engine
NASA Technical Reports Server (NTRS)
Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.
1979-01-01
The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.
Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review
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
A Study of Item Bias for Attitudinal Measurement Using Maximum Likelihood Factor Analysis.
ERIC Educational Resources Information Center
Mayberry, Paul W.
A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…
NASA Astrophysics Data System (ADS)
Alih, Ekele; Ong, Hong Choon
2014-07-01
The application of Ordinary Least Squares (OLS) to a single equation assumes among others, that the predictor variables are truly exogenous; that there is only one-way causation between the dependent variable yi and the predictor variables xij. If this is not true and the xij 'S are at the same time determined by yi, the OLS assumption will be violated and a single equation method will give biased and inconsistent parameter estimates. The OLS also suffers a huge set back in the presence of contaminated data. In order to rectify these problems, simultaneous equation models have been introduced as well as robust regression. In this paper, we construct a simultaneous equation model with variables that exhibit simultaneous dependence and we proposed a robust multivariate regression procedure for estimating the parameters of such models. The performance of the robust multivariate regression procedure was examined and compared with the OLS multivariate regression technique and the Three-Stage Least squares procedure (3SLS) using numerical simulation experiment. The performance of the robust multivariate regression and (3SLS) were approximately equally better than OLS when there is no contamination in the data. Nevertheless, when contaminations occur in the data, the robust multivariate regression outperformed the 3SLS and OLS.
Multivariate orthogonal regression in astronomy
NASA Astrophysics Data System (ADS)
Branham, Richard L., Jr.
1995-03-01
Total least squares considers the problem of data reduction when error resides in both the data itself and also in the equations of condition. Error may be found in all of the columns of the matrix of the equations of condition, or merely in some; the latter situation is referred to as a mixed total least squares problem. A covariance matrix may be derived for total least squares. Both memory and operation count requirements are more severe than for ordinary least squares: about four times more memory and, if the problem involves n unknowns, 15n + 4 more arithmetic operations. The method, applicable in any situation where ordinary least squares is relevant, including the estimation of scaled variables, is applied to three examples, one artificial and two taken from astronomy: the estimation of various parameters of Galactic kinematics, and the differential correction of a planetary orbit. In these two examples the results from total least squares are superior to those from ordinary least squares.
Enhancing scientific reasoning by refining students' models of multivariable causality
NASA Astrophysics Data System (ADS)
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
Recursive bias estimation for high dimensional regression smoothers
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.
Multivariate Statistical Modelling of Drought and Heat Wave Events
NASA Astrophysics Data System (ADS)
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
NASA Astrophysics Data System (ADS)
Khajehei, Sepideh; Moradkhani, Hamid
2015-04-01
Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre
Assessing Projection Bias in Consumers' Food Preferences.
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
Assessing Projection Bias in Consumers’ Food Preferences
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
Training interpretation biases among individuals with body dysmorphic disorder symptoms.
Premo, Julie E; Sarfan, Laurel D; Clerkin, Elise M
2016-03-01
The current study provided an initial test of a Cognitive Bias Modification for Interpretations (CBM-I) training paradigm among a sample with elevated BDD symptoms (N=86). As expected, BDD-relevant interpretations were reduced among participants who completed a positive (vs. comparison) training program. Results also pointed to the intriguing possibility that modifying biased appearance-relevant interpretations is causally related to changes in biased, socially relevant interpretations. Further, providing support for cognitive behavioral models, residual change in interpretations was associated with some aspects of in vivo stressor responding. However, contrary to expectations there were no significant effects of condition on emotional vulnerability to a BDD stressor, potentially because participants in both training conditions experienced reductions in biased socially-threatening interpretations following training (suggesting that the "comparison" condition was not inert). These findings have meaningful theoretical and clinical implications, and fit with transdiagnostic conceptualizations of psychopathology. PMID:26705744
Cognitive biases in dermatology training.
Shokeen, Divya
2016-07-01
Cognitive biases are patterns that physicians develop based on predetermined judgments that can influence their decisions regarding patient care. Unfortunately, they are usually encountered on a daily basis in clinics. A few examples include affective, anchoring, availability, confirmation, zebra, and Sutton's biases. PMID:27529715
Snow multivariable data assimilation for hydrological predictions in mountain areas
NASA Astrophysics Data System (ADS)
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
Multivariate analysis: A statistical approach for computations
NASA Astrophysics Data System (ADS)
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.
Classical least squares multivariate spectral analysis
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.
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.
Biological sequence classification with multivariate string kernels.
Kuksa, Pavel P
2013-01-01
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on the analysis of discrete 1D string data (e.g., DNA or amino acid sequences). In this paper, we address the multiclass biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physicochemical descriptors) and a class of multivariate string kernels that exploit these representations. On three protein sequence classification tasks, the proposed multivariate representations and kernels show significant 15-20 percent improvements compared to existing state-of-the-art sequence classification methods. PMID:24384708
Biological Sequence Analysis with Multivariate String Kernels.
Kuksa, Pavel P
2013-03-01
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete one-dimensional (1D) string data (e.g., DNA or amino acid sequences). In this work we address the multi-class biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors) and a class of multivariate string kernels that exploit these representations. On a number of protein sequence classification tasks proposed multivariate representations and kernels show significant 15-20\\% improvements compared to existing state-of-the-art sequence classification methods. PMID:23509193
Classifying sex biased congenital anomalies
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.
Optimal and multivariable control of a turbogenerator
NASA Astrophysics Data System (ADS)
Lahoud, M. A.; Harley, R. G.; Secker, A.
The use of modern control methods to design multivariable controllers which improve the performance of a turbogenerator was investigated. The turbogenerator nonlinear mathematical model from which a linearized model is deduced is presented. The inverse Nyquist Array method and the theory of optimal control are both applied to the linearized model to generate two alternative control schemes. The schemes are implemented on the nonlinear simulation model to assess their dynamic performance. Results from modern multivariable control schemes are compared with the classical automatic voltage regulator and speed governor system.
Multivariate multiscale entropy for brain consciousness analysis.
Ahmed, Mosabber Uddin; Li, Ling; Cao, Jianting; Mandic, Danilo P
2011-01-01
The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case. This allows us to gain a greater insight into the complexity of the underlying signal generating system, producing multifaceted and more robust estimates than standard single channel MSE. Simulations on both synthetic data and brain consciousness analysis support the approach. PMID:22254434
Sparse Multivariate Regression With Covariance Estimation
Rothman, Adam J.; Levina, Elizaveta; Zhu, Ji
2014-01-01
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online. PMID:24963268
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
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). PMID:25882406
Leyrat, Clémence; Caille, Agnès; Donner, Allan; Giraudeau, Bruno
2014-09-10
Despite randomization, selection bias may occur in cluster randomized trials. Classical multivariable regression usually allows for adjusting treatment effect estimates with unbalanced covariates. However, for binary outcomes with low incidence, such a method may fail because of separation problems. This simulation study focused on the performance of propensity score (PS)-based methods to estimate relative risks from cluster randomized trials with binary outcomes with low incidence. The results suggested that among the different approaches used (multivariable regression, direct adjustment on PS, inverse weighting on PS, and stratification on PS), only direct adjustment on the PS fully corrected the bias and moreover had the best statistical properties. PMID:24771662
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.
Quantifying Multivariate Classification Performance - the Problem of Overfitting
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.
Eliminating bias in CEM systems
Jahnke, J.A.
1995-12-31
The emission of sulfur dioxide, nitrogen oxides, and particulate matter from fossil-fuel fired power plants and industrial sources, is a matter of public concern that has led to both State and Federal requirements for pollution control. The accuracy of continuous emission monitoring (CEM) system data has been an issue of considerable importance in the development and implementation of the Acid Rain Program. The more stringent relative accuracy requirements of this program, coupled with the importance of emission trading allowances, has led to the need to minimize systematic errors in emissions measurement. With the inclusion of the t-test for bias in the Acid Rain Regulations of 40 CFR Part 75, a method was introduced that could be used to uncover systematic error, or bias, in CEM system measurements. Once bias is detected, it is highly desirable to either eliminate the cause of the bias or to apply correction factors to minimize its effect. However, a problem occurs in determining the cause of the bias; a task that is often both difficult and time consuming. This paper explains the rationale behind the bias test as applied to CEM systems and summarizes potential sources of systematic error in both extractive and in-situ CEM systems. The paper examines a number of factors that contribute to CEM system measurement error. Methods are suggested for both the detection and correction of the resulting biases.
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.
Multivariate Outliers. Review of the Literature.
ERIC Educational Resources Information Center
Jarrell, Michele G.
Research in the area of multivariate outliers is reviewed, emphasizing the problems associated with definition and identification. Treatment of the problem can be traced to 1777 and the work of D. Bernoulli. Most of the many procedures developed for identifying outliers proceed sequentially starting with the most aberrant observation, or proceed…
DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)
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...
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,…
Multivariate statistical mapping of spectroscopic imaging data.
Young, Karl; Govind, Varan; Sharma, Khema; Studholme, Colin; Maudsley, Andrew A; Schuff, Norbert
2010-01-01
For magnetic resonance spectroscopic imaging studies of the brain, it is important to measure the distribution of metabolites in a regionally unbiased way; that is, without restrictions to a priori defined regions of interest. Since magnetic resonance spectroscopic imaging provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of magnetic resonance spectroscopic imaging 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 spectroscopic imaging (SI) studies of brain. The aims of this study were to (1) develop and validate multivariate voxel-based statistical mapping for magnetic resonance spectroscopic imaging 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
Intermittent control of unstable multivariate systems.
Loram, I; Gawthrop, P; Gollee, H
2015-08-01
A sensorimotor architecture inspired from biological, vertebrate control should (i) explain the interface between high dimensional sensory analysis, low dimensional goals and high dimensional motor mechanisms and (ii) provide both stability and flexibility. Our interest concerns whether single-input-single-output intermittent control (SISO_IC) generalized to multivariable intermittent control (MIC) can meet these requirements.We base MIC on the continuous-time observer-predictorstate-feedback architecture. MIC uses event detection. A system matched hold (SMH), using the underlying continuoustime optimal control design, generates multivariate open-loop control signals between samples of the predicted state. Combined, this serial process provides a single-channel of control with optimised sensor fusion and motor synergies. Quadratic programming provides constrained, optimised equilibrium control design to handle unphysical configurations, redundancy and provides minimum, necessary reduction of open loop instability through optimised joint impedance. In this multivariate form, dimensionality is linked to goals rather than neuromuscular or sensory degrees of freedom. The biological and engineering rationale for intermittent rather than continuous multivariate control, is that the generalised hold sustains open loop predictive control while the open loop interval provides time within the feedback loop for online centralised, state dependent optimisation and selection. PMID:26736539
Threat bias, not negativity bias, underpins differences in political ideology.
Lilienfeld, Scott O; Latzman, Robert D
2014-06-01
Although disparities in political ideology are rooted partly in dispositional differences, Hibbing et al.'s analysis paints with an overly broad brush. Research on the personality correlates of liberal-conservative differences points not to global differences in negativity bias, but to differences in threat bias, probably emanating from differences in fearfulness. This distinction bears implications for etiological research and persuasion efforts. PMID:24970441
Psychological biases in environmental judgments
Miller, A.
1985-04-01
Faced with a complex environment, all of us resort to cognitive over-simplifications and wishful thinking, in an attempt to achieve an often illusory control over our lives. The resulting biases in judgment may lead to unfortunate decisions, as well as exacerbating disputes over such matters as the interpretation of environmental data. The detrimental effects of such biases are sufficient to warrant greater attention to the phenomenon. As a step in this direction, a variety of cognitive and motivated biases are discussed, together with examples of their effect on environmental judgment.
Bias and spread in EVT performance tests.
NASA Technical Reports Server (NTRS)
Smith, J. G.
1971-01-01
Performance tests (error probability measurements) of communications systems characterized by low bit rates and high reliability requirements frequently utilize classical extreme value theory (EVT) to avoid the excessive test times encountered with bit error rate (BER) tests. If the underlying noise is Gaussian or perturbed Gaussian, the EVT error estimates have either excessive bias or excessive variance if an insufficient number of test samples is used. EVT is examined to explain the cause of this bias and spread. Experimental verification is made by testing a known Gaussian source, and procedures that minimize these effects are described. It seems apparent that even under the best of conditions the EVT test results are not particularly better than those of BER tests.
Griswold, Cortland K
2015-12-21
Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. PMID:26431770
The intentionality bias in schizophrenia.
Peyroux, Elodie; Strickland, Brent; Tapiero, Isabelle; Franck, Nicolas
2014-11-30
The tendency to over-interpret events of daily life as resulting from voluntary or intentional actions is one of the key aspects of schizophrenia with persecutory delusions. Here, we ask whether this characteristic may emerge from the abnormal activity of a basic cognitive process found in healthy adults and children: the intentionality bias, which refers to the implicit and automatic inclination to interpret human actions as intentional (Rosset, 2008, Cognition 108, 771-780). In our experiment, patients with schizophrenia and healthy controls were shown sentences describing human actions in various linguistic contexts, and were asked to indicate whether the action was intentional or not. The results indicated that people with schizophrenia exhibited a striking bias to over attribute intentionality regardless of linguistic context, contrary to healthy controls who did not exhibit such a general intentionality bias. Moreover, this study provides some insight into the cognitive mechanisms underlying this bias: an inability to inhibit the automatic attribution of intentionality. PMID:25042425
Response Bias Modulates the Speech Motor System during Syllable Discrimination
Venezia, Jonathan Henry; Saberi, Kourosh; Chubb, Charles; Hickok, Gregory
2012-01-01
Recent evidence suggests that the speech motor system may play a significant role in speech perception. Repetitive transcranial magnetic stimulation (TMS) applied to a speech region of premotor cortex impaired syllable identification, while stimulation of motor areas for different articulators selectively facilitated identification of phonemes relying on those articulators. However, in these experiments performance was not corrected for response bias. It is not currently known how response bias modulates activity in these networks. The present functional magnetic resonance imaging experiment was designed to produce specific, measureable changes in response bias in a speech perception task. Minimal consonant-vowel stimulus pairs were presented between volume acquisitions for same-different discrimination. Speech stimuli were embedded in Gaussian noise at the psychophysically determined threshold level. We manipulated bias by changing the ratio of same-to-different trials: 1:3, 1:2, 1:1, 2:1, 3:1. Ratios were blocked by run and subjects were cued to the upcoming ratio at the beginning of each run. The stimuli were physically identical across runs. Response bias (criterion, C) was measured in individual subjects for each ratio condition. Group mean bias varied in the expected direction. We predicted that activation in frontal but not temporal brain regions would co-vary with bias. Group-level regression of bias scores on percent signal change revealed a fronto-parietal network of motor and sensory-motor brain regions that were sensitive to changes in response bias. We identified several pre- and post-central clusters in the left hemisphere that overlap well with TMS targets from the aforementioned studies. Importantly, activity in these regions covaried with response bias even while the perceptual targets remained constant. Thus, previous results suggesting that speech motor cortex participates directly in the perceptual analysis of speech should be called into
Influence of SST biases on future climate change projections
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.
Reducing bias in survival under non-random temporary emigration
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
Reducing bias in survival under nonrandom temporary emigration.
Peñaloza, Claudia L; Kendall, William L; Langtimm, Catherine A
2014-07-01
Despite intensive monitoring, temporary emigration from the sampling area can induce bias severe enough for managers to discard survival 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 nonrandom 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 were 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 naive constraint model (last and penultimate temporary emigration parameters made equal), was the least efficient, although still able to reduce terminal bias when compared to an unconstrained model. Joint analysis of several
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.
NASA Astrophysics Data System (ADS)
Greatbatch, Richard; Drews, Annika; Ding, Hui; Latif, Mojib; Park, Wonsun
2016-04-01
The North Atlantic cold bias, associated with a too zonal path of the North Atlantic Current and a missing "northwest corner", is a common problem in coupled climate and forecast models. The bias affects the North Atlantic and European climate mean state, variability and predictability. We investigate the use of a flow field correction to adjust the path of the North Atlantic Current as well as additional corrections to the surface heat and freshwater fluxes. Results using the Kiel Climate Model show that the flow field correction allows a northward flow into the northwest corner, largely eliminating the bias below the surface layer. A surface cold bias remains but can be eliminated by additionally correcting the surface freshwater flux, without adjusting the surface heat flux seen by the ocean model. A model version in which only the surface fluxes of heat and freshwater are corrected continues to exhibit the incorrect path of the North Atlantic Current and a strong subsurface bias. Removing the bias impacts the multi-decadal time scale variability in the model and leads to a better representation of the SST pattern associated with the Atlantic Multidecadal Variability than the uncorrected model.
Approach bias modification in inpatient psychiatric smokers.
Machulska, Alla; Zlomuzica, Armin; Rinck, Mike; Assion, Hans-Jörg; Margraf, Jürgen
2016-05-01
Drug-related automatic approach tendencies contribute to the development and maintenance of addictive behavior. The present study investigated whether a nicotine-related approach bias can be modified in smokers undergoing inpatient psychiatric treatment by using a novel training variant of the nicotine Approach-Avoidance-Task (AAT). Additionally, we assessed whether the AAT-training would affect smoking behavior. Inpatient smokers were randomly assigned to either an AAT-training or a sham-training condition. In the AAT-training condition, smokers were indirectly instructed to make avoidance movements in response to nicotine-related pictures and to make approach movements in response to tooth-cleaning pictures. In the sham-training condition, no contingency between picture content und arm movements existed. Trainings were administered in four sessions, accompanied by a brief smoking-cessation intervention. Smoking-related self-report measures and automatic approach biases toward smoking cues were measured before and after training. Three months after training, daily nicotine consumption was obtained. A total of 205 participants were recruited, and data from 139 participants were considered in the final analysis. Prior to the trainings, smokers in both conditions exhibited a stronger approach bias for nicotine-related pictures than for tooth-cleaning pictures. After both trainings, this difference was no longer evident. Although reduced smoking behavior at posttest was observed after both trainings, only the AAT-training led to a larger reduction of nicotine consumption at a three-month follow-up. Our preliminary data partially support the conclusion that the AAT might be a feasible tool to reduce smoking in the long-term in psychiatric patients, albeit its effect on other smoking-related measures remains to be explored. PMID:26874269
Adaptive multiscale entropy analysis of multivariate neural data.
Hu, Meng; Liang, Hualou
2012-01-01
Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data. PMID:21788182
Learning multivariate distributions by competitive assembly of marginals.
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. PMID:22529323
Bias and design in software specifications
NASA Technical Reports Server (NTRS)
Straub, Pablo A.; Zelkowitz, Marvin V.
1990-01-01
Implementation bias in a specification is an arbitrary constraint in the solution space. Presented here is a model of bias in software specifications. Bias is defined in terms of the specification process and a classification of the attributes of the software product. Our definition of bias provides insight into both the origin and the consequences of bias. It also shows that bias is relative and essentially unavoidable. Finally, we describe current work on defining a measure of bias, formalizing our model, and relating bias to software defects.
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.
Multivariate Mapping of Environmental Data Using Extreme Learning Machines
NASA Astrophysics Data System (ADS)
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
Advancing emotion theory with multivariate pattern classification
Kragel, Philip A.; LaBar, Kevin S.
2016-01-01
Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system.
Hybrid least squares multivariate spectral analysis methods
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.
Hybrid least squares multivariate spectral analysis methods
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.
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.
Multivariate linear recurrences and power series division
Hauser, Herwig; Koutschan, Christoph
2012-01-01
Bousquet-Mélou and Petkovšek investigated the generating functions of multivariate linear recurrences with constant coefficients. We will give a reinterpretation of their results by means of division theorems for formal power series, which clarifies the structural background and provides short, conceptual proofs. In addition, extending the division to the context of differential operators, the case of recurrences with polynomial coefficients can be treated in an analogous way. PMID:23482936
The Evolution of Multivariate Maternal Effects
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
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.
Simplified Linear Multivariable Control Of Robots
NASA Technical Reports Server (NTRS)
Seraji, Homayoun
1989-01-01
Simplified method developed to design control system that makes joints of robot follow reference trajectories. Generic design includes independent multivariable feedforward and feedback controllers. Feedforward controller based on inverse of linearized model of dynamics of robot and implements control law that contains only proportional and first and second derivatives of reference trajectories with respect to time. Feedback controller, which implements control law of proportional, first-derivative, and integral terms, makes tracking errors converge toward zero as time passes.
Ma, Jianming; Kockelman, Kara M; Damien, Paul
2008-05-01
Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves. PMID:18460364
Multivariate streamflow forecasting using independent component analysis
NASA Astrophysics Data System (ADS)
Westra, Seth; Sharma, Ashish; Brown, Casey; Lall, Upmanu
2008-02-01
Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence, while not resulting in any loss of ability in capturing temporal dependence. As such, the ICA-based technique would be expected to yield considerable advantages when used in a probabilistic setting to manage large reservoir systems with multiple inflows or data collection points.
Damage detection using multivariate recurrence quantification analysis
NASA Astrophysics Data System (ADS)
Nichols, J. M.; Trickey, S. T.; Seaver, M.
2006-02-01
Recurrence-quantification analysis (RQA) has emerged as a useful tool for detecting subtle non-stationarities and/or changes in time-series data. Here, we extend the RQA analysis methods to multivariate observations and present a method by which the "length scale" parameter ɛ (the only parameter required for RQA) may be selected. We then apply the technique to the difficult engineering problem of damage detection. The structure considered is a finite element model of a rectangular steel plate where damage is represented as a cut in the plate, starting at one edge and extending from 0% to 25% of the plate width in 5% increments. Time series, recorded at nine separate locations on the structure, are used to reconstruct the phase space of the system's dynamics and subsequently generate the multivariate recurrence (and cross-recurrence) plots. Multivariate RQA is then used to detect damage-induced changes to the structural dynamics. These results are then compared with shifts in the plate's natural frequencies. Two of the RQA-based features are found to be more sensitive to damage than are the plate's frequencies.
Regional dissociated heterochrony in multivariate analysis.
Mitteroecker, P; Gunz, P; Weber, G W; Bookstein, F L
2004-12-01
Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony. PMID:15646279
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
Hemri, S.; Lisniak, D.; Klein, B.
2015-09-01
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both location 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) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers 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. In this study, the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modeling the temporal dependencies of probabilistic hydrologic forecasts.
The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.
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. PMID:19501102
Negativity Bias in Dangerous Drivers
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
Zubizarreta, José R.; Cerdá, Magdalena; Rosenbaum, Paul R.
2013-01-01
In 2010, a magnitude 8.8 earthquake hit Chile, devastating parts of the country. Having just completed its national socioeconomic survey, the Chilean government reinterviewed a subsample of respondents, creating unusual longitudinal data about the same persons before and after a major disaster. The follow-up evaluated posttraumatic stress symptoms (PTSS) using Davidson’s Trauma Scale. We use these data with two goals in mind. Most studies of PTSS after disasters rely on recall to characterize the state of affairs before the disaster. We are able to use prospective data on preexposure conditions, free of recall bias, to study the effects of the earthquake. Second, we illustrate recent developments in statistical methodology for the design and analysis of observational studies. In particular, we use new and recent methods for multivariate matching to control 46 covariates that describe demographic variables, housing quality, wealth, health, and health insurance before the earthquake. We use the statistical theory of design sensitivity to select a study design with findings expected to be insensitive to small or moderate biases from failure to control some unmeasured covariate. PTSS were dramatically but unevenly elevated among residents of strongly shaken areas of Chile when compared with similar persons in largely untouched parts of the country. In 96% of exposed-control pairs exhibiting substantial PTSS, it was the exposed person who experienced stronger symptoms (95% confidence interval = 0.91–1.00). PMID:23222557
Zubizarreta, José R; Cerdá, Magdalena; Rosenbaum, Paul R
2013-01-01
In 2010, a magnitude 8.8 earthquake hit Chile, devastating parts of the country. Having just completed its national socioeconomic survey, the Chilean government reinterviewed a subsample of respondents, creating unusual longitudinal data about the same persons before and after a major disaster. The follow-up evaluated posttraumatic stress symptoms (PTSS) using Davidson's Trauma Scale. We use these data with two goals in mind. Most studies of PTSS after disasters rely on recall to characterize the state of affairs before the disaster. We are able to use prospective data on preexposure conditions, free of recall bias, to study the effects of the earthquake. Second, we illustrate recent developments in statistical methodology for the design and analysis of observational studies. In particular, we use new and recent methods for multivariate matching to control 46 covariates that describe demographic variables, housing quality, wealth, health, and health insurance before the earthquake. We use the statistical theory of design sensitivity to select a study design with findings expected to be insensitive to small or moderate biases from failure to control some unmeasured covariate. PTSS were dramatically but unevenly elevated among residents of strongly shaken areas of Chile when compared with similar persons in largely untouched parts of the country. In 96% of exposed-control pairs exhibiting substantial PTSS, it was the exposed person who experienced stronger symptoms (95% confidence interval = 0.91-1.00). PMID:23222557
Asymmetric Magnetization Reversal in Exchange Bias Systems*
NASA Astrophysics Data System (ADS)
Fitzsimmons, Michael
2001-03-01
Polarized neutron reflectometry measured the in-plane projection of the net-magnetization vector of polycrystalline Fe films exchange-coupled to (110) FeF2 antiferromagnetic (AF) films of controlled crystalline quality. For the sample with the single crystal AF film, we observed perpendicular exchange coupling across the ferromagnetic (F)-AF interface on either side of the hysteresis loop at coercivity. Perpendicular exchange coupling was observed regardless of cooling field orientation parallel or perpendicular to the AF anisotropy axis. Yet, for one orientation the exchange bias was zero; thus, perpendicular exchange coupling is not a sufficient condition for exchange bias. For samples with twinned AF films, an asymmetry in the spin flip scattering on either side of the hysteresis loop, and consequently in the magnetization reversal process, was observed. The origin of the asymmetry is explained by frustration of perpendicular exchange coupling, which enhances exchange bias and leads to 45° exchange coupling across the F-AF interface. The easy axis in the ferromagnet, which gives rise to asymmetric magnetization reversal in the twinned samples, is not present in samples with (110) textured polycrystalline AF films; and consequently exchange bias is reduced. *Work supported by the U.S. Department of Energy, BES-DMS under Contract No. W-7405-Eng-36, Grant No. DE-FG03-87ER-45332 and funds from the University of California Collaborative University and Laboratory Assisted Research. ÝWork in collaboration with A. Hoffmann, P. Yashar, J. Groves, R. Springer, P. Arendt (LANL), C. Leighton, K. Liu, Ivan K. Schuller (UCSD), J. Nogués (UAB), C.F. Majkrzak, J.A. Dura (NIST), H. Fritzsche (HMI), V. Leiner, H. Lauter (ILL).
Maintenance of motility bias during cyanobacterial phototaxis.
Chau, Rosanna Man Wah; Ursell, Tristan; Wang, Shuo; Huang, Kerwyn Casey; Bhaya, Devaki
2015-04-01
Signal transduction in bacteria is complex, ranging across scales from molecular signal detectors and effectors to cellular and community responses to stimuli. The unicellular, photosynthetic cyanobacterium Synechocystis sp. PCC6803 transduces a light stimulus into directional movement known as phototaxis. This response occurs via a biased random walk toward or away from a directional light source, which is sensed by intracellular photoreceptors and mediated by Type IV pili. It is unknown how quickly cells can respond to changes in the presence or directionality of light, or how photoreceptors affect single-cell motility behavior. In this study, we use time-lapse microscopy coupled with quantitative single-cell tracking to investigate the timescale of the cellular response to various light conditions and to characterize the contribution of the photoreceptor TaxD1 (PixJ1) to phototaxis. We first demonstrate that a community of cells exhibits both spatial and population heterogeneity in its phototactic response. We then show that individual cells respond within minutes to changes in light conditions, and that movement directionality is conferred only by the current light directionality, rather than by a long-term memory of previous conditions. Our measurements indicate that motility bias likely results from the polarization of pilus activity, yielding variable levels of movement in different directions. Experiments with a photoreceptor (taxD1) mutant suggest a supplementary role of TaxD1 in enhancing movement directionality, in addition to its previously identified role in promoting positive phototaxis. Motivated by the behavior of the taxD1 mutant, we demonstrate using a reaction-diffusion model that diffusion anisotropy is sufficient to produce the observed changes in the pattern of collective motility. Taken together, our results establish that single-cell tracking can be used to determine the factors that affect motility bias, which can then be coupled with
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
Radial bias is not necessary for orientation decoding.
Pratte, Michael S; Sy, Jocelyn L; Swisher, Jascha D; Tong, Frank
2016-02-15
Multivariate pattern analysis can be used to decode the orientation of a viewed grating from fMRI signals in early visual areas. Although some studies have reported identifying multiple sources of the orientation information that make decoding possible, a recent study argued that orientation decoding is only possible because of a single source: a coarse-scale retinotopically organized preference for radial orientations. Here we aim to resolve these discrepant findings. We show that there were subtle, but critical, experimental design choices that led to the erroneous conclusion that a radial bias is the only source of orientation information in fMRI signals. In particular, we show that the reliance on a fast temporal-encoding paradigm for spatial mapping can be problematic, as effects of space and time become conflated and lead to distorted estimates of a voxel's orientation or retinotopic preference. When we implement minor changes to the temporal paradigm or to the visual stimulus itself, by slowing the periodic rotation of the stimulus or by smoothing its contrast-energy profile, we find significant evidence of orientation information that does not originate from radial bias. In an additional block-paradigm experiment where space and time were not conflated, we apply a formal model comparison approach and find that many voxels exhibit more complex tuning properties than predicted by radial bias alone or in combination with other known coarse-scale biases. Our findings support the conclusion that radial bias is not necessary for orientation decoding. In addition, our study highlights potential limitations of using temporal phase-encoded fMRI designs for characterizing voxel tuning properties. PMID:26666900
Cravo, Andre M; Haddad, Hamilton; Claessens, Peter M E; Baldo, Marcus V C
2013-12-01
It has consistently been shown that agents judge the intervals between their actions and outcomes as compressed in time, an effect named intentional binding. In the present work, we investigated whether this effect is result of prior bias volunteers have about the timing of the consequences of their actions, or if it is due to learning that occurs during the experimental session. Volunteers made temporal estimates of the interval between their action and target onset (Action conditions), or between two events (No-Action conditions). Our results show that temporal estimates become shorter throughout each experimental block in both conditions. Moreover, we found that observers judged intervals between action and outcomes as shorter even in very early trials of each block. To quantify the decrease of temporal judgments in experimental blocks, exponential functions were fitted to participants' temporal judgments. The fitted parameters suggest that observers had different prior biases as to intervals between events in which action was involved. These findings suggest that prior bias might play a more important role in this effect than calibration-type learning processes. PMID:24016785
High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation
NASA Astrophysics Data System (ADS)
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.
Perceptual bias, more than age, impacts on eye movements during face processing.
Williams, Louise R; Grealy, Madeleine A; Kelly, Steve W; Henderson, Iona; Butler, Stephen H
2016-02-01
Consistent with the right hemispheric dominance for face processing, a left perceptual bias (LPB) is typically demonstrated by younger adults viewing faces and a left eye movement bias has also been revealed. Hemispheric asymmetry is predicted to reduce with age and older adults have demonstrated a weaker LPB, particularly when viewing time is restricted. What is currently unclear is whether age also weakens the left eye movement bias. Additionally, a right perceptual bias (RPB) for facial judgments has less frequently been demonstrated, but whether this is accompanied by a right eye movement bias has not been investigated. To address these issues older and younger adults' eye movements and gender judgments of chimeric faces were recorded in two time conditions. Age did not significantly weaken the LPB or eye movement bias; both groups looked initially to the left side of the face and made more fixations when the gender judgment was based on the left side. A positive association was found between LPB and initial saccades in the freeview condition and with all eye movements (initial saccades, number and duration of fixations) when time was restricted. The accompanying eye movement bias revealed by LPB participants contrasted with RPB participants who demonstrated no eye movement bias in either time condition. Consequently, increased age is not clearly associated with weakened perceptual and eye movement biases. Instead an eye movement bias accompanies an LPB (particularly under restricted viewing time conditions) but not an RPB. PMID:26799983
Essentialism promotes children's inter-ethnic bias
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
Multivariate Padé Approximations For Solving Nonlinear Diffusion Equations
NASA Astrophysics Data System (ADS)
Turut, V.
2015-11-01
In this paper, multivariate Padé approximation is applied to power series solutions of nonlinear diffusion equations. As it is seen from tables, multivariate Padé approximation (MPA) gives reliable solutions and numerical results.
Adaptation to high throughput batch chromatography enhances multivariate screening.
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. PMID:25914370
Heuristic-biased stochastic sampling
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.
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…
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,…
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…
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…
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…
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 position and…
From acclaim to blame: evidence of a person sensitivity decision bias.
Moon, Henry; Conlon, Donald E
2002-02-01
In a series of studies, the authors established empirical support for a general decision-making bias that they termed a person sensitvity bias. Specifically, a person sensitivity bias consists of a person positivity bias (D. O. Sears, 1983) under positive performance conditions and a person negativity bias under negative performance conditions. The authors conducted the first empirical studies of a direct comparison between individuals and objects performing the same task under both positive and negative performance conditions. Two additional studies tested the boundaries of the sensitivity bias within negatively framed decision dilemmas. The results are discussed in terms of their relevance toward a more comprehensive theory of person-object evaluation differences. PMID:11916214
Bias correction with Data Assimilation
NASA Astrophysics Data System (ADS)
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
The Electrically Controlled Exchange Bias
NASA Astrophysics Data System (ADS)
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
Modeling rainfall-runoff relationship using multivariate GARCH model
NASA Astrophysics Data System (ADS)
Modarres, R.; Ouarda, T. B. M. J.
2013-08-01
The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.
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…
What`s new in multivariable predictive control
Colwell, L.W.; Poe, W.A.; Papadopoulos, M.N.; Gamez, J.P.
1995-11-01
Multivariable control techniques have been successfully applied to a variety of gas processing operations. The technology has been applied to CO{sub 2} recovery towers, cryogenic demethanizers, lean oil absorbers, rich oil demethanizers, rich oil stills, deethanizers, depropanizers, deisobutanizers, amine treaters, sulfur recovery units, nitrogen rejection units and compressors. The system has been developed with a modular structure and employs process model based predictions of key plant variables. Modules for each type of operation are available and, with minimal modification, can be applied to a specific unit since the key plant variables are usually common between plants and are affected by similar disturbances. Adaptive nonlinear multivariable control models allow continuous operation at optimum conditions within plant constraints. In most applications a personal computer (PC) containing the control software dan supervisory control and data acquisition (SCADA) system operates under a UNIX operating system and interfaces with the plant`s existing control system. The PC-based system dispatches setpoints that have been calculated to optimize on-line the profitability of the plant. A typical project can be implemented in 4-6 months with a payout of less than a year by increasing natural gas liquids (NGL) revenues and decreasing plant operating costs. This paper describes the technology and the initial installation results.
Multivariate Analysis of Genotype-Phenotype Association.
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
Time varying, multivariate volume data reduction
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
Bias in Dynamic Monte Carlo Alpha Calculations
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.
Multivariate tests for trend in water quality
NASA Astrophysics Data System (ADS)
Loftis, Jim C.; Taylor, Charles H.; Chapman, Phillip L.
1991-07-01
Several methods of testing for multivariate trend have been discussed in the statistical and water quality literature. We review both parametric and nonparametric approaches and compare their performance using, synthetic data. A new method, based on a robust estimation and testing approach suggested by Sen and Puri, performed very well for serially independent observations. A modified version of the covariance inversion approach presented by Dietz and Killeen also performed well for serially independent observations. For serially correlated observations, the covariance eigenvalue method suggested by Lettenmaier was the best performer.
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
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
Multivariate curve-fitting in GAUSS
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.
COSIMA data analysis using multivariate techniques
NASA Astrophysics Data System (ADS)
Silén, J.; Cottin, H.; Hilchenbach, M.; Kissel, J.; Lehto, H.; Siljeström, S.; Varmuza, K.
2015-02-01
We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra by introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a cross-validation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.
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.
Algorithms for computing the multivariable stability margin
NASA Technical Reports Server (NTRS)
Tekawy, Jonathan A.; Safonov, Michael G.; Chiang, Richard Y.
1989-01-01
Stability margin for multiloop flight control systems has become a critical issue, especially in highly maneuverable aircraft designs where there are inherent strong cross-couplings between the various feedback control loops. To cope with this issue, we have developed computer algorithms based on non-differentiable optimization theory. These algorithms have been developed for computing the Multivariable Stability Margin (MSM). The MSM of a dynamical system is the size of the smallest structured perturbation in component dynamics that will destabilize the system. These algorithms have been coded and appear to be reliable. As illustrated by examples, they provide the basis for evaluating the robustness and performance of flight control systems.
Bayesian Transformation Models for Multivariate Survival Data
DE CASTRO, MÁRIO; CHEN, MING-HUI; IBRAHIM, JOSEPH G.; KLEIN, JOHN P.
2014-01-01
In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported. PMID:24904194
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.
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…
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…
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…
Observer Biases in the Classroom.
ERIC Educational Resources Information Center
Kite, Mary E.
1991-01-01
Presents three student exercises that demonstrate common perceptual errors described in social psychological literature: actor-observer effect, false consensus bias, and priming effects. Describes methods to be followed and gives terms, sentences, and a story to be used in the exercises. Suggests discussion of the bases and impact of such…
Attentional bias in math anxiety.
Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly
2015-01-01
Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms. PMID:26528208
Attentional bias in math anxiety
Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly
2015-01-01
Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms. PMID:26528208
ERIC Educational Resources Information Center
Cargill-Power, C.
Although cultural content is unavoidable as a backdrop for good language testing, cultural bias in testing English as a second language presents many dangers. A picture cue calling for a correct grammatical response may evoke an incorrect answer if the pictorial content is culturally coded. The cultural background behind a test must be accurately…
Correcting for Visuo-Haptic Biases in 3D Haptic Guidance
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
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. PMID:24334320
Network structure of multivariate time series.
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
Network structure of multivariate time series
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
Augmented classical least squares multivariate spectral analysis
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
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
Network structure of multivariate time series
NASA Astrophysics Data System (ADS)
Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito
2015-10-01
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.
Adaptable Multivariate Calibration Models for Spectral Applications
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.
Biased diffusion in tubes of alternating diameter: Analytical treatment in the case of strong bias
NASA Astrophysics Data System (ADS)
Zitserman, Vladimir Yu.; Berezhkovskii, Alexander M.; Antipov, Anatoly E.; Makhnovskii, Yurii A.
2014-12-01
This paper is devoted to the effective transport coefficients of a particle in a tube of alternating diameter. Analytical expressions are derived for the effective mobility and diffusivity under strong bias conditions, i.e., in the limiting case where the external biasing force tends to infinity. The expressions give the transport coefficients as functions of the geometric parameters of the tube and the external force. They show that the effective diffusivity is a linear function of the square of the external force, whereas the effective mobility is independent of the force. The problem of finding effective transport coefficients in a tube of alternating diameter is too complex to be analyzed by conventional methods. Therefore, the expressions are derived in the framework of an intuition-based approach and validated by Brownian dynamics simulations. The obtained results extend a short list of available analytical expressions for the effective transport coefficients.
A Multivariate Approach for Comparing and Classifying Streamwater Quality
NASA Astrophysics Data System (ADS)
Hooper, R. P.; McGlynn, B. L.; Hjerdt, K. N.; McDonnell, J. J.
2001-05-01
Few measures exist for objectively comparing the chemistry of streams. We develop a multivariate technique, based on an eigenvalue analysis of streamwater concentrations, to facilitate comparison of water quality among sites across basin scales. A correlation matrix is constructed to include only solutes that mix conservatively. An eigenvalue analysis of this matrix is performed at each site to determine the approximate rank of the data set. If the ranks of all sites are roughly equal, one site is chosen as the reference site. The reduced set of eigenvectors from this site is chosen as the basis for a new, lower dimensional coordinate system and the data from the other sites are projected into this coordinate system. To assess the relative orientation of data from the reference site to all of the other sites, the relative bias (RB) and relative root mean square error (RRMSE) are calculated between the original and the projected points. The new technique was applied to multiple sites within three experimental watersheds to assess the consistency of water quality across the basin scale. The three watersheds were: Panola Mountain, Georgia, USA (6 solutes, 8 sites, 3 to 1000 ha); Sleepers River, Vermont, USA (5 solutes, 7 sites, 3 to 840 ha); and Maimai, South Island, New Zealand (4 solutes, 4 sites, 3 to 300 ha). Data from all sites were roughly planar with the first two eigenvectors explaining more than 90% of the variation. The RRMSEs for the reference site were generally between 5 and 10% with <0.1% RB. At Maimai, the RRMSE was roughly equivalent between the test sites and the 17-ha reference site, 5-8%; the RB was less than 4% at all sites. At Sleepers River, Ca and Mg had larger RRMSE at smaller basins relative to the 41 ha reference site; there was no consistent pattern to the RB for these solutes. Mg, Na, and SiO2 exhibited larger RRMSE (10-20%) and had substantial bias (10%, -20%, and 10%, respectively) at the 840-ha site compared with the 41-ha site. At
Biased Brownian dynamics for rate constant calculation.
Zou, G; Skeel, R D; Subramaniam, S
2000-08-01
An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampling of trajectories is then biased, but the sampling is unbiased when the trajectory outcomes are multiplied by their weights. With a suitable choice of the biasing force, more reacted trajectories are sampled. As a consequence, the variance of the estimate is reduced. In our test case, biased Brownian dynamics gives a sevenfold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasing force. PMID:10919998
The nondiscriminating heart: lovingkindness meditation training decreases implicit intergroup bias.
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. PMID:23957283
Multivariable control system installed at ARCO west Texas gas plant
Chou, K. ); Clay, R.M. ); Gamez, J.P. ); Berkowitz, P.N.; Papadopoulos, M.N. )
1992-11-16
This paper reports that a PC-based, multivariable process control (MVC) system was installed last year at an ARCO Oil and Gas Co. gas plant in West Texas. This gas-processing application was developed under sponsorship of the Gas Research Institute. The system was installed, tuned, and on-line within 2 weeks and fully verified in closed loop service by operations in 8 weeks. Four more gas processing installations are currently under way. The general and main objective of the MVC control system is to achieve continuous optimum operation of a process unit through on-line prediction and control of setpoints for the key process variables in the unit. specifically, the objective is to achieve this operation, especially under constantly changing conditions, with reliable solutions requiring minimal operator intervention, customization, or update effort upon each plant change. MVC was developed by continental Controls Inc. (CCI), Houston.
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. PMID:9921654
Unsupervised classification of multivariate geostatistical data: Two algorithms
NASA Astrophysics Data System (ADS)
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.
Fourcade, Yoan; Engler, Jan O.; Rödder, Dennis; Secondi, Jean
2014-01-01
MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases. PMID:24818607
Garcia, Janelle T; Amankwah, Ernest K; Hernandez, Raquel G
2016-01-01
The increasing burden of obesity is prevalent in the pediatric populations. Pediatric nurses are spending increasing amounts of time and effort caring for obese patients however no prior studies have explored how nurses perceive obese patients. The purpose of this study is to identify weight bias in pediatric nurses (RNs) and clinical support staff (CSS) working in a pediatric hospital setting. A convenience sample of RNs and CSS from an urban, pediatric hospital were surveyed using the Nurses' Attitudes toward Obesity and Obese Patients Scale (NATOOPS), which consists of 6 patient-care factors with an additional factor added to assess bias towards the patient's caregiver. Mean factor scores ≥50 indicated bias. Data were summarized using descriptive statistics and means were compared using independent t tests. Multivariate logistic regression models were used to determine the association between putative risk factors and weight bias. RNs and CSS (N=308) demonstrated weight bias toward obese patient characteristics (mean=61.9) and perceived controllability of obesity (mean=65.8). CSS felt negatively about their supportive roles in caring for obese patients (mean=52.5). Respondent weight status and professional title resulted in variability of biased attitudes. Race, employment status, number of obese patients cared for daily, and department were predictive of biased attitudes. Weight biased attitudes toward obese pediatric patients and their caregivers were found among RNs and CSS. Future qualitative research will assist in the understanding the factors that cause nurse weight bias. PMID:26948091
Age differences in the correction processes of context-induced biases: when correction succeeds.
Wang, Mo; Chen, Yiwei
2004-09-01
Previous research has demonstrated that older adults are more susceptible than young adults to context-induced biases in social judgments. The primary goal of this study was to examine the conditions under which older adults could or could not correct their biases. Young and older adults completed a social judgment task that normally would produce contrast biases in 3 correction cue conditions: no cue, subtle cue, and blatant cue. It was found that both young and older adults corrected their biases in the blatant cue condition, but only young adults corrected in the subtle cue condition. The results suggest that older adults may need more environmental support in correcting their biases. PMID:15383003
Sampling bias in an internet treatment trial for depression.
Donkin, L; Hickie, I B; Christensen, H; Naismith, S L; Neal, B; Cockayne, N L; Glozier, N
2012-01-01
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. PMID:23092978
Analysis of bias effects on the total ionizing dose response in a 180 nm technology
NASA Astrophysics Data System (ADS)
Liu, Zhangli; Hu, Zhiyuan; Zhang, Zhengxuan; Shao, Hua; Chen, Ming; Bi, Dawei; Ning, Bingxu; Zou, Shichang
2011-07-01
The effects of gamma ray irradiation on the shallow trench isolation (STI) leakage current in a 180 nm technology are investigated. The radiation response is strongly influenced by the bias modes, gate bias during irradiation, substrate bias during irradiation and operating substrate bias after irradiation. We found that the worst case occurs under the ON bias condition for the ON, OFF and PASS bias mode. A positive gate bias during irradiation significantly enhances the STI leakage current, indicating the electric field influence on the charge buildup process during radiation. Also, a negative substrate bias during irradiation enhances the STI leakage current. However a negative operating substrate bias effectively suppresses the STI leakage current, and can be used to eliminate the leakage current produced by the charge trapped in the deep STI oxide. Appropriate substrate bias should be introduced to alleviate the total ionizing dose (TID) response, and lead to acceptable threshold voltage shift and subthreshold hump effect. Depending on the simulation results, we believe that the electric field distribution in the STI oxide is the key parameter influencing bias effects on the radiation response of transistor.
Meta-regression approximations to reduce publication selection bias.
Stanley, T D; Doucouliagos, Hristos
2014-03-01
Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy. PMID:26054026
Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.
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. PMID:23872657
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.
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.
Nested Taylor decomposition in multivariate function decomposition
NASA Astrophysics Data System (ADS)
Baykara, N. A.; Gürvit, Ercan
2014-12-01
Fluctuationlessness approximation applied to the remainder term of a Taylor decomposition expressed in integral form is already used in many articles. Some forms of multi-point Taylor expansion also are considered in some articles. This work is somehow a combination these where the Taylor decomposition of a function is taken where the remainder is expressed in integral form. Then the integrand is decomposed to Taylor again, not necessarily around the same point as the first decomposition and a second remainder is obtained. After taking into consideration the necessary change of variables and converting the integration limits to the universal [0;1] interval a multiple integration system formed by a multivariate function is formed. Then it is intended to apply the Fluctuationlessness approximation to each of these integrals one by one and get better results as compared with the single node Taylor decomposition on which the Fluctuationlessness is applied.
Multivariate Markov chain modeling for stock markets
NASA Astrophysics Data System (ADS)
Maskawa, Jun-ichi
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.
Multivariable Harmonic Balance for Central Pattern Generators★
Iwasaki, Tetsuya
2009-01-01
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. PMID:19956774
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.
Bayesian Local Contamination Models for Multivariate Outliers
Page, Garritt L.; Dunson, David B.
2013-01-01
In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465
Recent developments in biased agonism
Wisler, James W.; Xiao, Kunhong; Thomsen, Alex R.B.; Lefkowitz, Robert J.
2014-01-01
The classic paradigm of G protein-coupled receptor (GPCR) activation was based on the understanding that agonist binding to a receptor induces or stabilizes a conformational change to an “active” conformation. In the past decade, however, it has been appreciated that ligands can induce distinct “active” receptor conformations with unique downstream functional signaling profiles. Building on the initial recognition of the existence of such “biased ligands”, recent years have witnessed significant developments in several areas of GPCR biology. These include increased understanding of structural and biophysical mechanisms underlying biased agonism, improvements in characterization and quantification of ligand efficacy, as well as clinical development of these novel ligands. Here we review recent major developments in these areas over the past several years. PMID:24680426
A complete procedure for multivariate index-flood model application
NASA Astrophysics Data System (ADS)
Requena, Ana Isabel; Chebana, Fateh; Mediero, Luis
2016-04-01
Multivariate frequency analyses are needed to study floods due to dependence existing among representative variables of the flood hydrograph. Particularly, multivariate analyses are essential when flood-routing processes significantly attenuate flood peaks, such as in dams and flood management in flood-prone areas. Besides, regional analyses improve at-site quantile estimates obtained at gauged sites, especially when short flow series exist, and provide estimates at ungauged sites where flow records are unavailable. However, very few studies deal simultaneously with both multivariate and regional aspects. This study seeks to introduce a complete procedure to conduct a multivariate regional hydrological frequency analysis (HFA), providing guidelines. The methodology joins recent developments achieved in multivariate and regional HFA, such as copulas, multivariate quantiles and the multivariate index-flood model. The proposed multivariate methodology, focused on the bivariate case, is applied to a case study located in Spain by using hydrograph volume and flood peak observed series. As a result, a set of volume-peak events under a bivariate quantile curve can be obtained for a given return period at a target site, providing flexibility to practitioners to check and decide what the design event for a given purpose should be. In addition, the multivariate regional approach can also be used for obtaining the multivariate distribution of the hydrological variables when the aim is to assess the structure failure for a given return period.
Exploration of new multivariate spectral calibration algorithms.
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.
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.
Chowdhry, D P
1995-01-01
This article identifies gender bias against female children and youth in India. Gender bias is based on centuries-old religious beliefs and sayings from ancient times. Discrimination is reflected in denial or ignorance of female children's educational, health, nutrition, and recreational needs. Female infanticide and selective abortion of female fetuses are other forms of discrimination. The task of eliminating or reducing gender bias will involve legal, developmental, political, and administrative measures. Public awareness needs to be created. There is a need to reorient the education and health systems and to advocate for gender equality. The government of India set the following goals for the 1990s: to protect the survival of the girl child and practice safe motherhood; to develop the girl child in general; and to protect vulnerable girl children in different circumstances and in special groups. The Health Authorities should monitor the laws carefully to assure marriage after the minimum age, ban sex determination of the fetus, and monitor the health and nutrition of pre-school girls and nursing and pregnant mothers. Mothers need to be encouraged to breast feed, and to breast feed equally between genders. Every village and slum area needs a mini health center. Maternal mortality must decline. Primary health centers and hospitals need more women's wards. Education must be universally accessible. Enrollments should be increased by educating rural tribal and slum parents, reducing distances between home and school, making curriculum more relevant to girls, creating more female teachers, and providing facilities and incentives for meeting the needs of girl students. Supplementary income could be provided to families for sending girls to school. Recreational activities must be free of gender bias. Dowry, sati, and devdasi systems should be banned. PMID:12158019
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,…
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…
Significant biases affecting abundance determinations
NASA Astrophysics Data System (ADS)
Wesson, Roger
2015-08-01
I have developed two highly efficient codes to automate analyses of emission line nebulae. The tools place particular emphasis on the propagation of uncertainties. The first tool, ALFA, uses a genetic algorithm to rapidly optimise the parameters of gaussian fits to line profiles. It can fit emission line spectra of arbitrary resolution, wavelength range and depth, with no user input at all. It is well suited to highly multiplexed spectroscopy such as that now being carried out with instruments such as MUSE at the VLT. The second tool, NEAT, carries out a full analysis of emission line fluxes, robustly propagating uncertainties using a Monte Carlo technique.Using these tools, I have found that considerable biases can be introduced into abundance determinations if the uncertainty distribution of emission lines is not well characterised. For weak lines, normally distributed uncertainties are generally assumed, though it is incorrect to do so, and significant biases can result. I discuss observational evidence of these biases. The two new codes contain routines to correctly characterise the probability distributions, giving more reliable results in analyses of emission line nebulae.
Estimation of attitude sensor timetag biases
NASA Technical Reports Server (NTRS)
Sedlak, J.
1995-01-01
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
The Probability Distribution for a Biased Spinner
ERIC Educational Resources Information Center
Foster, Colin
2012-01-01
This article advocates biased spinners as an engaging context for statistics students. Calculating the probability of a biased spinner landing on a particular side makes valuable connections between probability and other areas of mathematics. (Contains 2 figures and 1 table.)
NASA Astrophysics Data System (ADS)
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.
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
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.
Examining Event-Related Potential (ERP) Correlates of Decision Bias in Recognition Memory Judgments
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
A multivariate joint hydrological drought indicator using vine copula
NASA Astrophysics Data System (ADS)
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
The origin of female-biased sex ratios in intertidal seagrasses (Phyllospadix spp.).
Shelton, Andrew Olaf
2010-05-01
Flowering sex ratios of dioecious plants are commonly male-biased but rarely female-biased. While greater costs of reproduction from females have been repeatedly demonstrated and explain male biases, male reproductive costs almost never exceed female costs, making the origins of female biases enigmatic. I investigated the seagrasses Phyllospadix scouleri and P. serrulatus (surfgrasses), which have some of the most extreme female-biased sex ratios documented (>90% female), to identify the mechanisms driving sex ratio bias. I developed sex-linked amplified fragment length polymorphism (AFLP) markers and applied them to three P. scouleri life stages at four sites to determine when during the life cycle sex ratio bias arises. Sex ratios were even among seedlings but became more female-biased at later life stages, indicating that sex ratios were driven by male-biased mortality. To identify when during the life cycle sex ratio bias developed, I examined sex differences in survival among seedlings and three aspects of reproductive costs that could potentially generate biased sex ratios under field conditions. No differences in seedling survival between the sexes were detected, and there was no evidence of substantial sex differences in costs of reproduction. I found no support for a trade-off between current and future reproduction or between reproductive investment and growth. Thus, costs of reproduction appear unlikely to drive sex ratio bias in surfgrass. Instead, small sex differences in growth and survival spread across the life cycle appear to be responsible for female-biased sex ratios and suggest that life history trade-offs other than reproductive costs drive sex ratio bias. PMID:20503870
Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi
2015-01-01
Background. Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients. PMID:26413142
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…
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)
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…
Begging the Question: Is Critical Thinking Biased?
ERIC Educational Resources Information Center
Alston, Kal
1995-01-01
Discusses whether critical thinking is biased, examining what is meant by critical thinking and bias and what the consequences are for addressing bias in different ways. The paper responds to the three previous papers in the critical thinking symposium in this issue of the journal. (SM)
Assessing Gender Bias in Computer Software.
ERIC Educational Resources Information Center
Rosenthal, Nina Ribak; Demetrulias, Diana Mayer
1988-01-01
Discusses gender bias in educational software programs and describes two studies that explored the ability of preservice and inservice teachers to detect bias in software programs. Evaluation instruments used to measure gender bias are described, and the need for instruction in equity issues is discussed. (23 references) (LRW)
Culturally Biased Assumptions in Counseling Psychology
ERIC Educational Resources Information Center
Pedersen, Paul B.
2003-01-01
Eight clusters of culturally biased assumptions are identified for further discussion from Leong and Ponterotto's (2003) article. The presence of cultural bias demonstrates that cultural bias is so robust and pervasive that is permeates the profession of counseling psychology, even including those articles that effectively attack cultural bias…
Computers, Gender Bias, and Young Children.
ERIC Educational Resources Information Center
Bhargava, Ambika; Kirova-Petrova, Anna; McNair, Shannan
1999-01-01
Discusses gender discrepancy in classroom computer access and use; suggests strategies to minimize gender biases. Argues that gender differences in computer usage are due to biased classroom practices, lack of female role models, home computer gender gaps, and scarcity of bias-free software. Maintains that increased teacher/parent awareness,…
a Multivariate Statistical Analysis of Visibility at California Regions.
NASA Astrophysics Data System (ADS)
Motallebi, Nehzat
This study summarizes the results of a comprehensive study of visibility in California. California is one of the few states that has promulgated air quality standards for visibility. The study was concerned not only with major metropolitan areas such as Los Angeles, but also with deterioration of visibility in the less urbanized areas of California. The relationships among visibility reduction, atmospheric pollutants, and meteorological conditions were examined by using the multivariate statistical techniques of principal component analysis and multiple linear regression analysis. The primary concern of this work was to find which of the many atmospheric constituents most effectively reduce visibility, and to determine the role of the different meteorological variables on these relationships. Another objective was to identify the major pollutant sources and transport routes which contribute to visibility degradation. In order to establish the relationship between the light scattering coefficient and particulate data, both the size distribution and the elemental composition of particulate aerosols were considered. Meanwhile, including meteorological parameters in the principal component analysis made it possible to investigate meteorological effects on the observed pollution patterns. The associations among wind direction, elemental concentration, and additional meteorological parameters were considered by using a special modification of principal component analysis. This technique can identify all of the main features, and provides reasonable source direction for particular elements. It is appropriate to note that there appeared to be no published accounts of a principal component analysis for a data set similar to that analyzed in this work. Finally, the results of the multivariate statistical analyses, multiple linear regression analysis and principal component analysis, indicate that intermediate size sulfur containing aerosols, sulfur size mode 0.6 (mu)m < D
Multivariable control of Texaco`s Eunice South Gas Plant
Alexander, M.C.; Martin, O.; Basu, U.; Poe, W.A.
1998-12-31
A model predictive multivariable controller has been commissioned at Texaco`s Eunice South Gas Plant to increase profits and to provide better overall control of the Cryogenic Demethanizer Unit. The project payback was based on increased recovery of ethane and NGL. The controller has also been successful in maintaining a composition specification at the bottom of the demethanizer column while optimizing operations by pushing the plant to run at its pressure constraints. The South Plant Cryogenic Unit at Texaco`s Eunice complex processes about 100 MMscfd of natural gas from various sources. The demethanizer recovers ethane plus while rejecting methane from the bottom product. The column consists of a top section providing entries for the reflux and expander outlet and a lower section consisting of two packed beds. Cold separator liquids enter near the top of the lower section. Bottom and side reboilers are attached to the lower portion of the column. Residue gas leaves the top and demethanized NGL leaves the bottom of the column. A multivariable control (MVC{reg_sign}) module was installed with the primary objective of increasing ethane recovery by decreasing the column pressure and increasing the pressure differential across the expander unit while maintaining operating constraints with varying inlet conditions. The MVC controller runs in a Pentium{reg_sign} computer operating under SCO{reg_sign} UNIX{reg_sign} and is also attached to the plant ethernet network. The plant has programmable logic controllers (PLC) which are networked to a supervisory control and data acquisition (SCADA) system through pyramid integrators. MVC computes the optimal setpoint to six basic control loops in supervisory mode.
Multivariate mixtures of Erlangs for density estimation under censoring.
Verbelen, Roel; Antonio, Katrien; Claeskens, Gerda
2016-07-01
Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets. PMID:26340888
Optimal decision making in heterogeneous and biased environments.
Moran, Rani
2015-02-01
The issue of optimal performance in speeded two-choice tasks has played a substantial role in the development and evaluation of decision making theories. For difficulty-homogeneous environments, the means to achieve optimality are prescribed by the sequential probability ratio test (SPRT), or equivalently, by the drift diffusion model (DDM). Biases in the external environments are easily accommodated into these models by adopting a prior integration bias. However, for difficulty-heterogeneous environments, the issue is more elusive. I show that in such cases, the SPRT and the DDM are no longer equivalent and both are suboptimal. Optimality is achieved by a diffusion-like accumulation of evidence while adjusting the choice thresholds during the time course of a trial. In the second part of the paper, assuming that decisions are made according to the popular DDM, I show that optimal performance in biased environments mandates incorporating a dynamic-bias component (a shift in the drift threshold) in addition to the prior bias (a shift in the starting point) into the model. These conclusions support a conjecture by Hanks, Mazurek, Kiani, Hopp, and Shadlen, (The Journal of Neuroscience, 31(17), 6339-6352, 2011) and contradict a recent attempt to refute this conjecture by arguing that optimality is achieved with the aid of prior bias alone (van Ravenzwaaij et al., 2012). The psychological plausibility of such "mathematically optimal" strategies is discussed. The current paper contributes to the ongoing effort to understand optimal behavior in biased and heterogeneous environments and corrects prior conclusions with respect to optimality in such conditions. PMID:24928091
Publication bias, with a focus on psychiatry: causes and solutions.
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. PMID:23696308
Bioharness™ Multivariable Monitoring Device: Part. I: Validity
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 there is limited information on its validity. The objective of this study was to assess the validity of all 5 Bioharness™ variables using a laboratory based treadmill protocol. 22 healthy males participated. Heart rate (HR), Breathing Frequency (BF) and Accelerometry (ACC) precision were assessed during a discontinuous incremental (0-12 km·h-1) treadmill protocol. Infra-red skin temperature (ST) was assessed during a 45 min-1 sub-maximal cycle ergometer test, completed twice, with environmental temperature controlled at 20 ± 0.1 °C and 30 ± 0.1 °C. Posture (P) was assessed using a tilt table moved through 160°. Adopted precision of measurement devices were; HR: Polar T31 (Polar Electro), BF: Spirometer (Cortex Metalyser), ACC: Oxygen expenditure (Cortex Metalyser), ST: Skin thermistors (Grant Instruments), P:Goniometer (Leighton Flexometer). Strong relationships (r = .89 to .99, p < 0.01) were reported for HR, BF, ACC and P. Limits of agreement identified differences in HR (-3.05 ± 32.20 b·min-1), BF (-3.46 ± 43.70 br·min-1) and P (0.20 ± 2.62°). ST established a moderate relationships (-0.61 ± 1.98 °C; r = 0.76, p < 0.01). Higher velocities on the treadmill decreased the precision of measurement, especially HR and BF. Global results suggest that the BioharressTM is a valid multivariable monitoring device within the laboratory environment. Key pointsDifferent levels of precision exist for each variable in the Bioharness™ (Version 1) multi-variable monitoring deviceAccelerometry and posture variables presented the most precise dataData from the heart rate and breathing frequency variable decrease in precision at velocities ≥ 10 km·h-1Clear understanding of the limitations of new applied monitoring technology is required before it is used by the exercise scientist PMID:24149346
On the Misuse of Manifest Variables in the Detection of Measurement Bias.
ERIC Educational Resources Information Center
Meredith, William; Millsap, Roger E.
1992-01-01
A unified treatment is presented for conditions that should allow detection of measurement bias using statistical procedures involving only observed or manifest variables. Computational results demonstrate that methods for studying bias that rely exclusively on manifest variables are not generally diagnostic of the presence or absence of…
Fluid simulation of the bias effect in inductive/capacitive discharges
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.
Biased selection within the social health insurance market in Colombia.
Castano, Ramon; Zambrano, Andres
2006-12-01
Reducing the impact of insurance market failures with regulations such as community-rated premiums, standardized benefit packages and open enrolment, yield limited effect because they create room for selection bias. The Colombian social health insurance system started a market approach in 1993 expecting to improve performance of preexisting monopolistic insurance funds by exposing them to competition by new entrants. This paper tests the hypothesis that market failures would lead to biased selection favoring new entrants. Two household surveys are analyzed using Self-Reported Health Status and the presence of chronic conditions as prospective indicators of individual risk. Biased selection is found to take place, leading to adverse selection among incumbents, and favorable selection among new entrants. This pattern is absent in 1997 but is evident in 2003. Given that the two incumbents analyzed are public organizations, the fiscal implications of the findings in terms of government bailouts, are analyzed. PMID:16516333
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.
Bias effects on the electronic spectrum of a molecular bridge
Phillips, Heidi; Prociuk, Alexander; Dunietz, Barry D
2011-01-01
In this paper the effect of bias and geometric symmetry breaking on the electronic spectrum of a model molecular system is studied. Geometric symmetry breaking can either enhance the dissipative effect of the bias, where spectral peaks are disabled, or enable new excitations that are absent under zero bias conditions. The spectralanalysis is performed on a simple model system by solving for the electronic response to an instantaneously impulsive perturbation in the dipole approximation. The dynamical response is extracted from the electronic equations of motion as expressed by the Keldysh formalism. This expression provides for the accurate treatment of the electronic structure of a bulk-coupled system at the chosen model Hamiltonian electronic structure level.
Sex Bias in Classifying Borderline and Narcissistic Personality Disorder.
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. PMID:26421970
Opinion Dynamics with Confirmation Bias
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
Charge amplifier with bias compensation
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.
Electromagnetic bias of 10-GHz radar altimeter measurements of MSL
NASA Technical Reports Server (NTRS)
Choy, L. W.; Hammond, D. L.; Uliana, E. A.
1984-01-01
Electromagnetic bias, the small difference that exists between the radar measured mean sea level and the geometric mean sea level is an important issue in high precision satellite altimetry. Present day satellite altimetry has achieved, with SEASAT-1, a precision of 5 cm rms in the range measurement. Future altimeter designs are expected to improve the range measurement precision to cm rms. In order to exploit the capability of these precise radar altimeters are marine geodesy and oceanography, it is necessary to understand and account for all of the known biases in the range measurement. The electromagnetic bias or the EM bias, which has been attributed to the observed fact that ocean wave troughs tend to be better reflectors of nadir viewing microwave radar energy than ocean wave crests, can be observed with high resolution airborne radar. This report presents the results of the EM bias measurements made by NRL using an airborne radar altimeter operating at 10 GHz with a 1 ns range resolution. Data were taken for various sea states and wind conditions. The experimental results are compared with current theories.
Spatial Bias in Field-Estimated Unsaturated Hydraulic Properties
HOLT,ROBERT M.; WILSON,JOHN L.; GLASS JR.,ROBERT J.
2000-12-21
Hydraulic property measurements often rely on non-linear inversion models whose errors vary between samples. In non-linear physical measurement systems, bias can be directly quantified and removed using calibration standards. In hydrologic systems, field calibration is often infeasible and bias must be quantified indirectly. We use a Monte Carlo error analysis to indirectly quantify spatial bias in the saturated hydraulic conductivity, K{sub s}, and the exponential relative permeability parameter, {alpha}, estimated using a tension infiltrometer. Two types of observation error are considered, along with one inversion-model error resulting from poor contact between the instrument and the medium. Estimates of spatial statistics, including the mean, variance, and variogram-model parameters, show significant bias across a parameter space representative of poorly- to well-sorted silty sand to very coarse sand. When only observation errors are present, spatial statistics for both parameters are best estimated in materials with high hydraulic conductivity, like very coarse sand. When simple contact errors are included, the nature of the bias changes dramatically. Spatial statistics are poorly estimated, even in highly conductive materials. Conditions that permit accurate estimation of the statistics for one of the parameters prevent accurate estimation for the other; accurate regions for the two parameters do not overlap in parameter space. False cross-correlation between estimated parameters is created because estimates of K{sub s} also depend on estimates of {alpha} and both parameters are estimated from the same data.
Apparatus and system for multivariate spectral analysis
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.
Composite density maps for multivariate trajectories.
Scheepens, Roeland; Willems, Niels; van de Wetering, Huub; Andrienko, Gennady; Andrienko, Natalia; van Wijk, Jarke J
2011-12-01
We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. [25] are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains. PMID:22034373
A Gibbs sampler for multivariate linear regression
NASA Astrophysics Data System (ADS)
Mantz, Adam B.
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.
Multivariate volume visualization through dynamic projections
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.
Flexible Linked Axes for multivariate data visualization.
Claessen, Jarry H T; van Wijk, Jarke J
2011-12-01
Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising. PMID:22034351
Challenges in bias correcting climate change simulations
NASA Astrophysics Data System (ADS)
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.
Unlearning Implicit Social Biases During Sleep **
Hu, Xiaoqing; Antony, James W.; Creery, Jessica D.; Vargas, Iliana M.; Bodenhausen, Galen V.; Paller, Ken A.
2015-01-01
Although people may endorse egalitarianism and tolerance, social biases can remain operative and drive harmful actions in an unconscious manner. Here we investigated training to reduce implicit racial and gender bias. Forty participants processed counter-stereotype information paired with one sound for each type of bias. Biases were reduced immediately after training. During subsequent slow-wave sleep, one sound was unobtrusively presented to each participant, repeatedly, to reactivate one type of training. Corresponding bias reductions were fortified in comparison to the social bias not externally reactivated during sleep. This advantage remained one week later, the magnitude of which was associated with time in slow-wave and rapid-eye-movement sleep after training. We conclude that memory reactivation during sleep enhances counter-stereotype training, and that maintaining a bias reduction is sleep-dependent. PMID:26023137
Finch, Emma C.; Iverach, Lisa; Menzies, Ross G.; Jones, Mark
2016-01-01
ABSTRACT 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. PMID:26211552
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…
Bias correction of the CCSM4 for improved regional climate modeling of the North American monsoon
NASA Astrophysics Data System (ADS)
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.
Bias correction of the CCSM4 for improved regional climate modeling of the North American monsoon
NASA Astrophysics Data System (ADS)
Meyer, Jonathan D. D.; Jin, Jiming
2015-07-01
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.
Detecting and Dealing with Outliers in Univariate and Multivariate Contexts.
ERIC Educational Resources Information Center
Wiggins, Bettie Caroline
Because multivariate statistics are increasing in popularity with social science researchers, the challenge of detecting multivariate outliers warrants attention. Outliers are defined as cases which, in regression analyses, generally lie more than three standard deviations from Yhat and therefore distort statistics. There are, however, some…
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…
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…
Reporting bias in medical research - a narrative review
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
Bioharness™ Multivariable Monitoring Device: Part. II: Reliability
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
A Multivariate Analysis of Galaxy Cluster Properties
NASA Astrophysics Data System (ADS)
Ogle, P. M.; Djorgovski, S.
1993-05-01
We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.
Multivariate statistical analysis of wildfires in Portugal
NASA Astrophysics Data System (ADS)
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).
Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study.
Bar-Haim, Yair; Lamy, Dominique; Pergamin, Lee; Bakermans-Kranenburg, Marian J; van IJzendoorn, Marinus H
2007-01-01
This meta-analysis of 172 studies (N = 2,263 anxious,N = 1,768 nonanxious) examined the boundary conditions of threat-related attentional biases in anxiety. Overall, the results show that the bias is reliably demonstrated with different experimental paradigms and under a variety of experimental conditions, but that it is only an effect size of d = 0.45. Although processes requiring conscious perception of threat contribute to the bias, a significant bias is also observed with stimuli outside awareness. The bias is of comparable magnitude across different types of anxious populations (individuals with different clinical disorders, high-anxious nonclinical individuals, anxious children and adults) and is not observed in nonanxious individuals. Empirical and clinical implications as well as future directions for research are discussed. PMID:17201568
NASA Astrophysics Data System (ADS)
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.
Social reward shapes attentional biases.
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. PMID:25941868
Asymmetric divertor biasing in MAST
NASA Astrophysics Data System (ADS)
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.
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. PMID:26603500
Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.
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. PMID:25790973
The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance
Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang
2015-01-01
The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182
Interventions That Affect Gender Bias in Hiring: A Systematic Review
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
The GRAPES variational bias correction scheme and associated preliminary experiments
NASA Astrophysics Data System (ADS)
Wang, Xiang; Li, Gang; Zhang, Hua; Wang, Hui; Guo, Rui
2011-02-01
The variational assimilation theory is generally based on unbiased observations. In practice, however, almost all observations suffer from biases arising from observational instruments, radiative transfer operator, precondition of data, and so on. Therefore, a bias correction scheme is indispensable. The current scheme for radiance bias correction in the GRAPES 3DVar system is an offline scheme. It is actually a static correction for the radiance bias before the process of cost function minimization. In consideration of its effects on forecast results, this kind of scheme has some shortcomings. Thus, this study provides a variational bias correction (VarBC) scheme for the GRAPES 3DVar system following Dee's idea. In the VarBC scheme, the observation operator is modified and a new control variable is defined by taking the predictor coefficients as the control parameters. According to the feature of the GRAPES-3DVAR, an incremental formulation is applied and the original bias correction scheme is maintained in the actual process of observations. The VarBC is designed to co-exist with the original scheme, because it is a dynamic revision to the observational operator on the basis of the old method, i.e., it adjusts the model state vector along with the control parameters to an unbiased state in the process of minimization and the assimilation system remains consistent with available information automatically. Preliminary experimental results show that the mean departures of background-minus-observation and analysis-minus-observation are reduced as expected. In a case study of the heavy rainfall that happened in South China on 11-13 June 2008, the 500-hPa geopotential height is better simulated using the analyzed field from the VarBC as the initial condition.
Publication Bias in Methodological Computational Research
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
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.
Quantum Criticality in the Biased Dicke Model.
Zhu, Hanjie; Zhang, Guofeng; Fan, Heng
2016-01-01
The biased Dicke model describes a system of biased two-level atoms coupled to a bosonic field, and is expected to produce new phenomena that are not present in the original Dicke model. In this paper, we study the critical properties of the biased Dicke model in the classical oscillator limits. For the finite-biased case in this limit, We present analytical results demonstrating that the excitation energy does not vanish for arbitrary coupling. This indicates that the second order phase transition is avoided in the biased Dicke model, which contrasts to the original Dicke model. We also analyze the squeezing and the entanglement in the ground state, and find that a finite bias will strongly modify their behaviors in the vicinity of the critical coupling point. PMID:26786239
Quantum Criticality in the Biased Dicke Model
Zhu, Hanjie; Zhang, Guofeng; Fan, Heng
2016-01-01
The biased Dicke model describes a system of biased two-level atoms coupled to a bosonic field, and is expected to produce new phenomena that are not present in the original Dicke model. In this paper, we study the critical properties of the biased Dicke model in the classical oscillator limits. For the finite-biased case in this limit, We present analytical results demonstrating that the excitation energy does not vanish for arbitrary coupling. This indicates that the second order phase transition is avoided in the biased Dicke model, which contrasts to the original Dicke model. We also analyze the squeezing and the entanglement in the ground state, and find that a finite bias will strongly modify their behaviors in the vicinity of the critical coupling point. PMID:26786239
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…
Multivariate distributions of soil hydraulic parameters
NASA Astrophysics Data System (ADS)
Qu, Wei; Pachepsky, Yakov; Huisman, Johan Alexander; Martinez, Gonzalo; Bogena, Heye; Vereecken, Harry
2014-05-01
on pedotransfer relationships not only within a given textural class but also on pedotransfer relationships within other textural classes since the pedotransfer relationships are developed across the database containing data for several textural classes. Therefore, joint multivariate parameter distributions for a specific class may not be sufficiently accurate. Currently PTF may give the best prediction of the parameter itself, but they are not designed to estimate correlations between parameters. Covariance matrices for soil hydraulic parameters present an additional type of pedotransfer information that needs to be acquired and used whenever random sets of those parameters are to be generated.
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…
Electric Control of Exchange Bias Training
NASA Astrophysics Data System (ADS)
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.
Evaluation of bias associated with high-multiplex, target-specific pre-amplification
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
The central tendency bias in color perception: effects of internal and external noise.
Olkkonen, Maria; McCarthy, Patrice F; Allred, Sarah R
2014-01-01
Perceptual estimates can be biased by previously seen stimuli in delayed estimation tasks. These biases are often toward the mean of the whole stimulus set. Recently, we demonstrated such a central tendency bias in delayed color estimation. In the Bayesian framework of perceptual inference, perceptual biases arise when noisy sensory measurements are combined with prior information about the world. Here, we investigate this idea in color perception by manipulating stimulus range and stimulus noise while characterizing delayed color estimates. First, we manipulated the experimental prior for stimulus color by embedding stimuli in collections with different hue ranges. Stimulus range affected hue bias: Hue estimates were always biased toward the mean of the current set. Next, we studied the effect of internal and external noise on the amount of hue bias. Internal noise was manipulated by increasing the delay between the reference and test from 0.4 to 4 s. External noise was manipulated by increasing the amount of chromatic noise in the reference stimulus, while keeping the delay between the reference and test constant at 2 s. Both noise manipulations had a reliable effect on the strength of the central tendency bias. Furthermore, there was a tendency for a positive relationship between variability of the estimates and bias in both noise conditions. In conclusion, observers are able to learn an experimental hue prior, and the weight on the prior can be manipulated by introducing noise in the estimation process. PMID:25194017
Bias: a review of current understanding.
Adebiyi, A O
2010-09-01
The results of many research findings have come under scrutiny in recent years due to the introduction of systematic errors at one stage or the other of the research. Over the years, literature has been rife about the issue of bias with many authors describing unique types of bias. More often, researchers have often been left in the dark about the basic concept of this important phenomenon. Using a method of cross referencing, exploded tree search, consultation of textbooks of epidemiology and conference proceedings, this article examines the basic concept of bias and the current understanding of bias as present in various literature. A simple classification of biases into conceptualization, selection and information biases is proposed. A distinction is made of confounding as describing an association that is true but potentially misleading and bias which on the other hand creates an association that is not true. The article further goes on to describe the different types of biases applicable to different study designs before concluding on the need for researchers to pay attention to the issue of bias so as to make their studies useful to readers. PMID:21416795
The truth and bias model of judgment.
West, Tessa V; Kenny, David A
2011-04-01
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 the attraction and the value is the location toward which the judgment is attracted. The model also makes a formal theoretical distinction between bias and moderator variables. Two major classes of biases are discussed: biases that are measured with variables (e.g., assumed similarity) and directional bias, which refers to the extent to which judgments are pulled toward 1 end of the judgment continuum. Moderator variables are conceptualized as variables that affect the accuracy and bias forces but that do not affect judgments directly. We illustrate the model with 4 examples. We discuss the theoretical, empirical, methodological, measurement, and design implications of the model. PMID:21480740
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.
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.
Multivariable Dynamic Ankle Mechanical Impedance With Active Muscles
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
Optimal mapping of site-specific multivariate soil properties.
Burrough, P A; Swindell, J
1997-01-01
This paper demonstrates how geostatistics and fuzzy k-means classification can be used together to improve our practical understanding of crop yield-site response. Two aspects of soil are important for precision farming: (a) sensible classes for a given crop, and (b) their spatial variation. Local site classifications are more sensitive than general taxonomies and can be provided by the method of fuzzy k-means to transform a multivariate data set with i attributes measured at n sites into k overlapping classes; each site has a membership value mk for each class in the range 0-1. Soil variation is of interest when conditions vary over patches manageable by agricultural machinery. The spatial variation of each of the k classes can be analysed by computing the variograms of mk over the n sites. Memberships for each of the k classes can be mapped by ordinary kriging. Areas of class dominance and the transition zones between them can be identified by an inter-class confusion index; reducing the zones to boundaries gives crisp maps of dominant soil groups that can be used to guide precision farming equipment. Automation of the procedure is straightforward given sufficient data. Time variations in soil properties can be automatically incorporated in the computation of membership values. The procedures are illustrated with multi-year crop yield data collected from a 5 ha demonstration field at the Royal Agricultural College in Cirencester, UK. PMID:9573478
ADS-Demo Fuel Rod Performance: Multivariate Statistical Analysis
Calabrese, R.; Vettraino, F.; Luzzi, L.
2004-07-01
A forward step in the development of Accelerator Driven System (ADS) for the Pu, MA and LLFP transmutation, is the realisation of a 80 MWt ADS-demo (XADS) whose basic objective is the system feasibility demonstration. The XADS is forecasted to adopt the UO{sub 2}-PuO{sub 2} mixed-oxides fuel already experimented in the sodium cooled fast reactors such as the french SPX-1. The present multivariate statistical analysis performed by using the Transuranus Code, was carried out for the Normal Operation at the so-called Enhanced Nominal Conditions (120% nominal reactor power), aimed at verifying that the fuel system complies with the stated design limits, i.e. centerline fuel temperature, cladding temperature and damage, during all the in-reactor lifetime. A statistical input set similar to SPX and PEC fuel case, was adopted. One most relevant assumption in the present calculations was a 30% AISI-316 cladding thickness corrosion at EOL. Relative influence of main fuel rod parameters on fuel centerline temperature was also evaluated. (authors)
Boccia, Stefania; La Torre, Giuseppe; Persiani, Roberto; D'Ugo, Domenico; van Duijn, Cornelia M; Ricciardi, Gualtiero
2007-01-01
Scientific literature may be biased because of the internal validity of studies being compromised by different forms of measurement error, and/or because of the selective reporting of positive and 'statistically significant' results. While the first source of bias might be prevented, and in some cases corrected to a degree, the second represents a pervasive problem afflicting the medical literature; a situation that can only be 'corrected' by a change in the mindset of authors, reviewers, and editors. This review focuses on the concepts of confounding, selection bias and information bias, utilising explanatory examples and simple rules to recognise and, when possible, to correct for them. Confounding is a mixing of effects resulting from an imbalance of some of the causes of disease across the compared groups. It can be prevented by randomization and restriction, and controlled by stratification, standardization or by using multivariable techniques. Selection bias stems from an absence of comparability among the groups being studied, while information bias arises from distorted information collection techniques. Publication bias of medical research results can invalidate evidence-based medicine, when a researcher attempting to collect all the published studies on a specific topic actually gathers only a proportion of them, usually the ones reporting 'positive' results. The selective publication of 'statistically significant' results represents a problem that researchers and readers have to be aware of in order to face the entire body of published medical evidence with a degree of scepticism. PMID:17359550
Alternating multivariate trigonometric functions and corresponding Fourier transforms
NASA Astrophysics Data System (ADS)
Klimyk, A. U.; Patera, J.
2008-04-01
We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group An, which is a subgroup of the permutation (symmetric) group Sn. These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel.
Multivariable synthesis with transfer functions. [applications to gas turbine engines
NASA Technical Reports Server (NTRS)
Peczkowski, J. L.
1980-01-01
A transfer function design theory for multivariable control synthesis is highlighted. The use of unique transfer function matrices and two simple, basic relationships - a synthesis equation and a design equation - are presented and illustrated. This multivariable transfer function approach provides the designer with a capability to specify directly desired dynamic relationships between command variables and controlled or response variables. At the same time, insight and influence over response, simplifications, and internal stability is afforded by the method. A general, comprehensive multivariable synthesis capability is indicated including nonminmum phase and unstable plants. Gas turbine engine examples are used to illustrate the ideas and method.
Cognitive bias in rats is not influenced by oxytocin
McGuire, Molly C.; Williams, Keith L.; Welling, Lisa L. M.; Vonk, Jennifer
2015-01-01
The effect of oxytocin on cognitive bias was investigated in rats in a modified conditioned place preference paradigm. Fifteen male rats were trained to discriminate between two different cue combinations, one paired with palatable foods (reward training), and the other paired with unpalatable food (aversive training). Next, their reactions to two ambiguous cue combinations were evaluated and their latency to contact the goal pot recorded. Rats were injected with either oxytocin (OT) or saline with the prediction that rats administered OT would display a shorter average latency to approach on ambiguous trials. There was no significant difference between latencies to approach on ambiguous trials compared to reward trials, but the rats were significantly slower on the aversive compared to the ambiguous conditions. Oxytocin did not affect approach time; however, it was unclear, after follow-up testing, whether the OT doses tested were sufficient to produce the desired effects on cognitive bias. Future research should consider this possibility. PMID:26388811
Gender bias in the evaluation of new age music.
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. PMID:12778980
Generation of multivariate autoregressive sequences with emphasis on initial values
NASA Astrophysics Data System (ADS)
Ula, Taylan A.
1992-12-01
Certain aspects of data generation are studied through multivariate autoregressive (AR) models. The main emphasis is on the preservation of certain desired moments and the effect of initial values on these moments. The problem of preservation of moments is approached in a nontraditional way by starting with the initial values. For this purpose, general AR processes with a random start and with time-varying parameters are introduced to lay a foundation for the analysis of all types of AR processes, including the periodic cases. It is shown that an AR process with a random start and with parameters obtained from the moment equations is capable of generating jointly multivariate normal vectors with any specified means and covariance matrices, and with any specified autocovariance matrices up to a given lag. With a random start, there is no transition period involved for achieving these moments. A simple solution is proposed for matrix equations of the form BBT = A which appear in the moment equations. The aggregation properties of general AR process are also studied. A more detailed analysis is given for the two-period first-order periodic autoregressive model, PAR 2(1). For the PAR 2(1) process with a random start and with parameters obtained from the moment equations, it is shown that the autocovariance function depends only on the period and the lag, and therefore the process is periodic (covariance) stationary. The PAR 2(1) process with a fixed start is also studied. It is shown that the moments of this process depend on the absolute time, in addition to the period and the lag, and therefore the process is not periodic stationary. This dependence diminishes with time, and periodic stationarity is realized if the AR parameters satisfy certain conditions. In that case, the PAR 2(1) process with a fixed start converges to that with a random start, but only after a certain transition period. This proves the superiority of a random start over a fixed start.
Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models
NASA Astrophysics Data System (ADS)
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.
Attributional bias and reactive aggression.
Hudley, C; Friday, J
1996-01-01
This article looks at a cognitive behavioral intervention designed to reduce minority youths' (Latino and African-American boys) levels of reactive peer-directed aggression. The BrainPower Program trains aggressive boys to recognize accidental causation in ambiguous interactions with peers. The objective of this research is to evaluate the effectiveness of this attribution retraining program in reducing levels of reactive, peer-directed aggression. This research hypothesizes that aggressive young boys' tendency to attribute hostile intentions to others in ambiguous social interactions causes display of inappropriate, peer-directed aggression. A reduction in attributional bias should produce a decrease in reactive physical and verbal aggression directed toward peers. A 12-session, attributional intervention has been designed to reduce aggressive students' tendency to infer hostile intentions in peers following ambiguous peer provocations. The program trains boys to (1) accurately perceive and categorize the available social cues in interactions with peers, (2) attribute negative outcomes of ambiguous causality to accidental or uncontrollable causes, and (3) generate behaviors appropriate to these retrained attributions. African-American and Latino male elementary-school students (N = 384), in grades four-six, served as subjects in one of three groups: experimental attribution retraining program, attention training, and no-attention control group. Three broad categories of outcome data were collected: teacher and administrator reports of behavior, independent observations of behavior, and self-reports from participating students. Process measures to assess implementation fidelity include videotaped training sessions, observations of intervention sessions, student attendance records, and weekly team meetings. The baseline data indicated that students who were evenly distributed across the four sites were not significantly different on the baseline indicators: student
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,…
Warnell, I; Chincholkar, M; Eccles, M
2015-01-01
Predicting risk of perioperative mortality after oesophagectomy for cancer may assist patients to make treatment choices and allow balanced comparison of providers. The aim of this systematic review of multivariate prediction models is to report their performance in new patients, and compare study methods against current recommendations. We used PRISMA guidelines and searched Medline, Embase, and standard texts from 1990 to 2012. Inclusion criteria were English language articles reporting development and validation of prediction models of perioperative mortality after open oesophagectomy. Two reviewers screened articles and extracted data for methods, results, and potential biases. We identified 11 development, 10 external validation, and two clinical impact studies. Overestimation of predicted mortality was common (5-200% error), discrimination was poor to moderate (area under receiver operator curves ranged from 0.58 to 0.78), and reporting of potential bias was poor. There were potentially important case mix differences between modelling and validation samples, and sample sizes were considerably smaller than is currently recommended. Steyerberg and colleagues' model used the most 'transportable' predictors and was validated in the largest sample. Most models have not been adequately validated and reported performance has been unsatisfactory. There is a need to clarify definition, effect size, and selection of currently available candidate predictors for inclusion in prediction models, and to identify new ones strongly associated with outcome. Adoption of prediction models into practice requires further development and validation in well-designed large sample prospective studies. PMID:25231768
NASA Astrophysics Data System (ADS)
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
Belief bias and the extinction of induced fear.
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. PMID:23679911
Biased Perception of Mean Emotion in Abstinent Heroin Abusers.
Zhang, Meng; Wang, Xuan; Hu, Chun; Liao, Huayu; Yang, Tong; Shen, Mowei
2015-01-01
Although evidence suggests that drug abusers exhibit biases when coding individual emotional facial expressions, little is known about how they process multiple expressions simultaneously. The present study evaluated the mean emotions perceived by abstinent heroin abusers. Male abstinent heroin abusers (AHs) and healthy controls (HCs) were randomly assigned into three emotional conditions (happy, sad, or angry), viewed sets of four faces (Experiment 1) or individual faces (Experiment 2) that varied in emotionality (neutral to happy/sad/angry), and judged whether a test face presented later was more/less emotional than the preceding stimuli. Average points of subjective equality were calculated to reflect participants' biases in perceiving emotions of sets or single faces. Relative to HCs, AHs overestimated mean emotions for sad and angry faces in Experiment 1; however, no such biases were found in Experiment 2. This suggests biased ensemble coding towards negative emotional facial expressions in AHs. Furthermore, when controlling for depression and anxiety, AHs' enhanced perception of mean emotion for angry or sad faces in Experiment 1 decreased, indicating a possible mediating effect of these psychopathological variables in the relationship between drug addiction history and abnormal ensemble processing for sets of emotional expressions. PMID:26595559
A unifying modeling framework for highly multivariate disease mapping.
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. PMID:25645551
Heavy flavor identification using multivariate analysis at H1
Pandurovic, Mila; Bozovic-Jelisavcic, Ivanka; Mudrinic, Mihajlo
2010-01-21
We discuss b quark identification in deep inelastic scattering of electron on proton at H1 by applying multivariate analysis method. Separation between heavy and light flavors can be further used to extract proton quark content.
Search for the top quark using multivariate analysis techniques
Bhat, P.C.; D0 Collaboration
1994-08-01
The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the e{mu} channel and neural networks to the e+jets channel.
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. PMID:25108772
ERIC Educational Resources Information Center
Greer, Carolyn Melton
This booklet is designed to help high school administrators turn their schools into bias-free learning environments. The first section covers the meaning of sex equity, the magnitude of the sex bias problem, preparation needed for bias-free learning, and implications for administrators. Section 2 lists and describes six appended activities and…
Distinctive characteristics of sexual orientation bias crimes.
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. PMID:21156686
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…
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…
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…
Systematic Error Modeling and Bias Estimation
Zhang, Feihu; Knoll, Alois
2016-01-01
This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386
Monitoring SEU parameters at reduced bias
Roth, D.R.; McNulty, P.J.; Abdel-Kader, W.G.; Strauss, L. . Dept. of Physics and Astronomy); Stassinopoulos, E.G. )
1993-12-01
SEU sensitivity of a CMOS SRAM increases with decreasing bias in such a manner that the critical charge exhibits a linear dependence on bias. This should allow proton and neutron monitoring of SEU parameters even for radiation hardened devices. The sensitivity of SEU rates to the thickness of the sensitive volume is demonstrated and procedures for determining the SEU parameters using protons are outlined.
For Sale: Subliminal Bias in Textbooks.
ERIC Educational Resources Information Center
Britton, Gwyneth E.; Lumpkin, Margaret C.
Between 1972 and 1975, textbook publishers have issued position papers and guidelines for authors which both identify the areas of bias (sex, race, and career) hidden in public school textbooks and suggest alternative measures for authors and editors to consider in order to avoid these biases. This study was begun in 1972 to determine whether…
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…
Gender Bias in Lebanese Language Classes
ERIC Educational Resources Information Center
Mougharbel, Ghada M.; Bahous, Rima
2010-01-01
Gender bias, though often implicit and unnoticed, exists in many forms and in different situations. The purpose of this study is to investigate whether gender bias exists in Lebanese language classrooms. Semi-structured interviews, questionnaires, and nonparticipant observational techniques were used for data collection. Results reveal…
Effective biasing schemes for duct streaming problems.
Broadhead, B L; Wagner, J C
2005-01-01
The effective use of biasing for the Monte Carlo solution of a void streaming problem is essential to obtaining a reasonable result in a reasonable amount of time. Most general purpose Monte Carlo shielding codes allow for the user to select the particular biasing techniques best oriented to the particular problem of interest. The biasing strategy for void streaming problems many times differs from that of a deep penetration problem. The key in void streaming is to bias particles into the streaming path, whereas in deep penetration problems the biasing is aimed at forcing particles through the shield. Until recently, the biasing scheme in the SCALE SAS4 shielding module was considered inadequate for void streaming problems due to the assumed one-dimensional nature of the automated bias prescription. A modified approach to the automated biasing in SAS4 has allowed for significant gains to be realised in the use of the code for void streaming problems. This paper applies the modified SAS4 procedures to a spent fuel storage cask model with vent ports. The results of the SAS4 analysis are compared with those of the ADVANTG methodology, which is an accelerated version of MCNP. Various options available for the implementation of the SAS4 methodology are reviewed and recommendations offered. PMID:16604687
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…
Response Bias in Needs Assessment Studies.
ERIC Educational Resources Information Center
Calsyn, Robert J.; Klinkenberg, W. Dean
1995-01-01
Agencies conducting needs assessments in which respondents are asked about their awareness of the agency must be alert to a bias that inflates awareness (agency awareness acquiescence). A study with 157 college students demonstrated such awareness bias, which was related to the impression management component of social desirability. (SLD)
Experimenter Bias Effects: A Direct Replication.
ERIC Educational Resources Information Center
Cipani, Ennio; Waite, Vicki A.
1980-01-01
This study replicates previous research by Kent and O'Leary assessing the effects of experimenter bias on behavioral recordings. Behaviors targeted for biased statements evidenced more change in observers' scorings from "baseline" to "treatment" tape segments than control behaviors. Additional analyses of observers' scorings indicated an increase…
Systematic Error Modeling and Bias Estimation.
Zhang, Feihu; Knoll, Alois
2016-01-01
This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386
Belief biases and volatility of assets
NASA Astrophysics Data System (ADS)
Lei-Sun, Wen-Zou, Hui
2014-10-01
Based on an overlapping generation model, this paper introduces the noise traders with belief biases and rational traders. With an equilibrium analysis, this paper examines the volatility of risky asset. The results show that the belief biases, the probability of economy state, and the domain capability are all the factors that have effects on the volatility of the market.
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…
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:…
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…
Bias in the Social Studies Curricula.
ERIC Educational Resources Information Center
Lamott, Stephen
There has always been a traditional bias in the learning materials used in schools. Due to a growing awareness of this bias, educators have become sensitive to the need for change in curricular materials. The trend in textbook publication has been to pretend that discrimination no longer exists in the United States. The extensive textbook…
The Antifeminist Bias in Traditional Criticism.
ERIC Educational Resources Information Center
Rogers, Katharine M.
Ten recent articles and books are cited in this paper as examples of a continuing antifeminist bias in literary criticism. Several forms of this bias are discussed, including an imperviousness to the feminist awareness, a refusal to recognize it, and open irritation by some critics that women are now finding a voice in literary criticism. A…
An uncertain journey around the tails of multivariate hydrological distributions
NASA Astrophysics Data System (ADS)
Serinaldi, Francesco
2013-10-01
Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.
Constructing multivariate distributions with generalized marginals and t-copulas
NASA Astrophysics Data System (ADS)
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.
Multivariate analysis of environmental data for two hydrographic basins
Andrade, J.M.; Prada, D.; Muniategui, S.; Gonzalez, E.; Alonso, E. )
1992-02-01
A multivariate study (PCA Analysis and Cluster analysis) of two Spanish hydrographic basins (The Mandeo and Mero basins) was made to achieve reliable conclusions about their actual physico-chemical environmental situation. Two police-samples' are defined, their effects explained, and are introduced in Cluster analysis as a way to examine sample quality. The multivariate analysis shows different qualities in the two hydrographic basins.
Pattern recognition used to investigate multivariate data in analytical chemistry
Jurs, P.C.
1986-06-06
Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.
Implicit Social Biases in People With Autism.
Birmingham, Elina; Stanley, Damian; Nair, Remya; Adolphs, Ralph
2015-11-01
Implicit social biases are ubiquitous and are known to influence social behavior. A core diagnostic criterion of autism spectrum disorders (ASD) is abnormal social behavior. We investigated the extent to which individuals with ASD might show a specific attenuation of implicit social biases, using Implicit Association Tests (IATs) involving social (gender, race) and nonsocial (nature, shoes) categories. High-functioning adults with ASD showed intact but reduced IAT effects relative to healthy control participants. We observed no selective attenuation of implicit social (vs. nonsocial) biases in our ASD population. To extend these results, we supplemented our healthy control data with data collected from a large online sample from the general population and explored correlations between autistic traits and IAT effects. We observed no systematic relationship between autistic traits and implicit social biases in our online and control samples. Taken together, these results suggest that implicit social biases, as measured by the IAT, are largely intact in ASD. PMID:26386014
Are all biases missing data problems?
Howe, Chanelle J.; Cain, Lauren E.; Hogan, Joseph W.
2015-01-01
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. PMID:26576336
Sampling effort affects multivariate comparisons of stream assemblages
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.
Gahan, D.; Hopkins, M. B.; Dolinaj, B.
2008-03-15
A retarding field energy analyzer designed to measure ion energy distributions impacting a radio-frequency biased electrode in a plasma discharge is examined. The analyzer is compact so that the need for differential pumping is avoided. The analyzer is designed to sit on the electrode surface, in place of the substrate, and the signal cables are fed out through the reactor side port. This prevents the need for modifications to the rf electrode--as is normally the case for analyzers built into such electrodes. The capabilities of the analyzer are demonstrated through experiments with various electrode bias conditions in an inductively coupled plasma reactor. The electrode is initially grounded and the measured distributions are validated with the Langmuir probe measurements of the plasma potential. Ion energy distributions are then given for various rf bias voltage levels, discharge pressures, rf bias frequencies - 500 kHz to 30 MHz, and rf bias waveforms - sinusoidal, square, and dual frequency.
Taylor, M J D; Strike, S C
2016-06-01
Turning bias, the preferential tendency to turn toward a given direction has been reported in both rodents and human participants. The observational gait method of determining turning bias in humans requires a stop prior to turning. This study removed the stop and hypothesised that turning bias would remain the same between stop and non-stop conditions if bias was solely under the control of neurochemical asymmetries. The results showed that statistically turning bias remained the same (to the left) regardless of method used but there was no agreement between the methods thus rejecting the hypothesis. It is likely that when not stopping biomechanical factors related to gait when turning influence the direction of turn rather than solely neurochemical asymmetries. PMID:26974038
Medical journal peer review: process and bias.
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
Evidence of nationalistic bias in muaythai.
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 PointsNationalistic 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
Evidence Of Nationalistic Bias In Muaythai
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
NASA Astrophysics Data System (ADS)
Ge, Hao
2012-06-01
We prove the detailed and integral multivariable fluctuation theorems in the steady-state cycle kinetics of single enzyme with competing substrates for any arbitrary time interval [0, t]. It is shown that the moment generating function for the stochastic number of each enzymatic cycle follows the multivariable fluctuation theorems in the form of Kurchan-Lebowitz-Spohn-type symmetry. These symmetry relations associated with different variables are independent of each other, which may help experimentally determine the thermodynamic affinities from the sample trajectories separately. Furthermore, we also obtain the Kawasaki equalities for the fluctuating chemical work done within each enzymatic cycle. The derivation here is directly based on the generalized Haldane equalities, which say that the forward and backward conditional dwell times for each enzymatic cycle have identical distributions. These symmetries are independent of each other, which may help experimentally determine the thermodynamic affinities from the sample trajectories separately.
Exploring biases in exoplanet spectroscopy retrievals
NASA Astrophysics Data System (ADS)
Feng, Ying; Fortney, Jonathan; Line, Michael R.; Morley, Caroline
2015-12-01
Spectra from the atmospheres of transiting planets and imaged planets are now being routinely achieved. The interpretation of these spectra gives us a window into the physics and chemistry of these atmospheres, as well as a better understanding of planet formation. Over the past several years retrievals of exoplanet spectra have been used to obtain chemical abundances and thermal structures, along with assessments of uncertainties on these quantities. However, any potential biases inherent in these methods have yet to be fully explored. The atmospheres we observe are inherently three-dimensional (3D) structures that feature gradients in temperatures and chemical abundances, as well as hot spots, cold spots, storms, and cloud patchiness. How well does the assumption of retrieving 1D hemispheric average conditions represent the true state of a 3D atmosphere? Can we be led astray? Answering these questions are important today, and will only become more important as data quality improves. Using a descendent of the CHIMERA retrieval code, here I present the results of how retrievals perform on more complex scenarios. We start with the emitted spectrum from the average of two pressure-temperature profiles, one warmer, one colder, and move on to more sophisticated, but still realistic, scenarios. Our work makes use of a new code we have developed to construct spectra from arbitrary 3D model atmospheres.
Does contextual information bias bitemark comparisons?
Osborne, Nikola K P; Woods, Sally; Kieser, Jules; Zajac, Rachel
2014-07-01
A growing body of research suggests that the interpretation of fingerprint evidence is open to contextual bias. While there has been suggestion in the literature that the same might apply to bitemarks - a form of identification evidence in which a degree of contextual information during the comparison phase is generally unavoidable - there have so far been no empirical studies to test this assertion. We explored dental and non-dental students' ability to state whether two bitemarks matched, while manipulating task ambiguity and the presence and emotional intensity of additional contextual information. Provision of the contextual information influenced participants' decisions on the ambiguous bitemarks. Interestingly, when participants were presented with highly emotional images and subliminally primed with the words 'same' and 'guilty', they made fewer matches relative to our control condition. Dental experience also played a role in decision-making, with dental students making more matches as the experiment progressed, regardless of context or task ambiguity. We discuss ways that this exploratory research can be extended in future studies. PMID:25002044
Leaf physiognomy and climate: A multivariate analysis
NASA Astrophysics Data System (ADS)
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.
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.
ERIC Educational Resources Information Center
Jarrell, Michele Glankler
This repeated measures factorial design study compared the results of two procedures for identifying multivariate outliers under varying conditions, the Mahalanobis distance and the Andrews-Pregibon statistic. Results were analyzed for the total number of outliers identified and number of false outliers identified. Simulated data were limited to…
Reward sensitivity predicts ice cream-related attentional bias assessed by inattentional blindness.
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. PMID:25681293
Large biases in regression-based constituent flux estimates: causes and diagnostic tools
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.
Selecting on treatment: a pervasive form of bias in instrumental variable analyses.
Swanson, Sonja A; Robins, James M; Miller, Matthew; Hernán, Miguel A
2015-02-01
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
Statistical framework for estimating GNSS bias
NASA Astrophysics Data System (ADS)
Vierinen, Juha; Coster, Anthea J.; Rideout, William C.; Erickson, Philip J.; Norberg, Johannes
2016-03-01
We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line-integrated electron densities (total electron content: TEC) that are scaled to equivalent vertical integrated densities. The spatiotemporal variability, instrumentation-dependent errors, and errors due to inaccurate ionospheric altitude profile assumptions are modeled as structure functions. These structure functions determine how the TEC differences are weighted in the linear least-squares minimization procedure, which is used to produce the bias estimates. A method for automatic detection and removal of outlier measurements that do not fit into a model of receiver bias is also described. The same statistical framework can be used for a single receiver station, but it also scales to a large global network of receivers. In addition to the Global Positioning System (GPS), the method is also applicable to other dual-frequency GNSS systems, such as GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema). The use of the framework is demonstrated in practice through several examples. A specific implementation of the methods presented here is used to compute GPS receiver biases for measurements in the MIT Haystack Madrigal distributed database system. Results of the new algorithm are compared with the current MIT Haystack Observatory MAPGPS (MIT Automated Processing of GPS) bias determination algorithm. The new method is found to produce estimates of receiver bias that have reduced day-to-day variability and more consistent coincident vertical TEC values.
Statistical framework for estimating GNSS bias
NASA Astrophysics Data System (ADS)
Vierinen, J.; Coster, A. J.; Rideout, W. C.; Erickson, P. J.; Norberg, J.
2015-09-01
We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line integrated electron densities (TEC) that are scaled to equivalent vertical integrated densities. The spatio-temporal variability, instrumentation dependent errors, and errors due to inaccurate ionospheric altitude profile assumptions are modeled as structure functions. These structure functions determine how the TEC differences are weighted in the linear least-squares minimization procedure, which is used to produce the bias estimates. A method for automatic detection and removal of outlier measurements that do not fit into a model of receiver bias is also described. The same statistical framework can be used for a single receiver station, but it also scales to a large global network of receivers. In addition to the Global Positioning System (GPS), the method is also applicable to other dual frequency GNSS systems, such as GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema). The use of the framework is demonstrated in practice through several examples. A specific implementation of the methods presented here are used to compute GPS receiver biases for measurements in the MIT Haystack Madrigal distributed database system. Results of the new algorithm are compared with the current MIT Haystack Observatory MAPGPS bias determination algorithm. The new method is found to produce estimates of receiver bias that have reduced day-to-day variability and more consistent coincident vertical TEC values.
Weight Bias in University Health Professions Students.
Blanton, Cynthia; Brooks, Jennifer K; McKnight, Laura
2016-01-01
Negative attitudes toward people with high body weight have been documented in pre-professional health students, prompting concern that such feelings may manifest as poor patient care in professional practice. This study assessed weight bias in university students in the non-physician health professions. A convenience sample of 206 students completed an online survey composed of a validated 14-item scale (1-5 lowest to highest weight bias) and questions regarding personal experiences of weight bias. Respondents were grouped by discipline within graduate and undergraduate levels. Weight bias was present in a majority of respondents. Overall, the percentage of responses indicative of weight bias was 92.7%. The mean total score was 3.65. ± 0.52, and the rating exceeded 3 for all 14 scale descriptors of high-weight people. In graduate students, discipline had a significant main effect on total score (p=0.01), with lower scores in dietetics (3.17 ± 0.46) vs audiology/sign language/speech language pathology (3.84 ± 0.41) and physician assistant students (3.78 ± 0.51; p<0.05). These findings show that weight bias is prevalent in health professions students at a mountain west university. Well-controlled studies that track students into professional practice would help determine whether bias-reduction interventions in college improve provider behaviors and clinical outcomes. PMID:27585618
Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L
2015-12-30
Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26303671
Applying bias correction for merging rain gauge and radar data
NASA Astrophysics Data System (ADS)
Rabiei, E.; Haberlandt, U.
2015-03-01
Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.
Mulstiscale Stochastic Generator of Multivariate Met-Ocean Time Series
NASA Astrophysics Data System (ADS)
Guanche, Yanira; Mínguez, Roberto; Méndez, Fernando J.
2013-04-01
The design of maritime structures requires information on sea state conditions that influence its behavior during its life cycle. In the last decades, there has been a increasing development of sea databases (buoys, reanalysis, satellite) that allow an accurate description of the marine climate and its interaction with a given structure in terms of functionality and stability. However, these databases have a limited timelength, and its appliance entails an associated uncertainty. To avoid this limitation, engineers try to sample synthetically generated time series, statistically consistent, which allow the simulation of longer time periods. The present work proposes a hybrid methodology to deal with this issue. It is based in the combination of clustering algorithms (k-means) and an autoregressive logistic regression model (logit). Since the marine climate is directly related to the atmospheric conditions at a synoptic scale, the proposed methodology takes both systems into account; generating simultaneously circulation patterns (weather types) time series and the sea state time series related. The generation of these time series can be summarized in three steps: (1) By applying the clustering technique k-means the atmospheric conditions are classified into a representative number of synoptical patterns (2) Taking into account different covariates involved (such as seasonality, interannual variability, trends or autoregressive term) the autoregressive logistic model is adjusted (3) Once the model is able to simulate weather types time series the last step is to generate multivariate hourly metocean parameters related to these weather types. This is done by an autoregressive model (ARMA) for each variable, including cross-correlation between them. To show the goodness of the proposed method the following data has been used: Sea Level Pressure (SLP) databases from NCEP-NCAR and Global Ocean Wave (GOW) reanalysis from IH Cantabria. The synthetical met-ocean hourly
NASA Astrophysics Data System (ADS)
Ding, Mei-Shuang; Jin, Ning-De; Gao, Zhong-Ke
2015-11-01
The simultaneous flow of oil and water through a horizontal pipe is a common occurrence during petroleum industrial processes. Characterizing the flow behavior underlying horizontal oil-water flows is a challenging problem of significant importance. In order to solve this problem, we carry out experiment to measure multivariate signals from different flow patterns and then propose a novel modality transition-based network to analyze the multivariate signals. The results suggest that the local betweenness centrality and weighted shortest path of the constructed network can characterize the transitions of flow conditions and further allow quantitatively distinguishing and uncovering the dynamic flow behavior underlying different horizontal oil-water flow patterns.
Classification of Malaysia aromatic rice using multivariate statistical analysis
Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.
2015-05-15
Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.
Multivariate meta-analysis of mixed outcomes: a Bayesian approach.
Bujkiewicz, Sylwia; Thompson, John R; Sutton, Alex J; Cooper, Nicola J; Harrison, Mark J; Symmons, Deborah P M; Abrams, Keith R
2013-09-30
Multivariate random effects meta-analysis (MRMA) is an appropriate way for synthesizing data from studies reporting multiple correlated outcomes. In a Bayesian framework, it has great potential for integrating evidence from a variety of sources. In this paper, we propose a Bayesian model for MRMA of mixed outcomes, which extends previously developed bivariate models to the trivariate case and also allows for combination of multiple outcomes that are both continuous and binary. We have constructed informative prior distributions for the correlations by using external evidence. Prior distributions for the within-study correlations were constructed by employing external individual patent data and using a double bootstrap method to obtain the correlations between mixed outcomes. The between-study model of MRMA was parameterized in the form of a product of a series of univariate conditional normal distributions. This allowed us to place explicit prior distributions on the between-study correlations, which were constructed using external summary data. Traditionally, independent 'vague' prior distributions are placed on all parameters of the model. In contrast to this approach, we constructed prior distributions for the between-study model parameters in a way that takes into account the inter-relationship between them. This is a flexible method that can be extended to incorporate mixed outcomes other than continuous and binary and beyond the trivariate case. We have applied this model to a motivating example in rheumatoid arthritis with the aim of incorporating all available evidence in the synthesis and potentially reducing uncertainty around the estimate of interest. PMID:23630081
Classification of Malaysia aromatic rice using multivariate statistical analysis
NASA Astrophysics Data System (ADS)
Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.
2015-05-01
Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.
Gravity and large-scale nonlocal bias
NASA Astrophysics Data System (ADS)
Chan, Kwan Chuen; Scoccimarro, Román; Sheth, Ravi K.
2012-04-01
For Gaussian primordial fluctuations the relationship between galaxy and matter overdensities, bias, is most often assumed to be local at the time of observation in the large-scale limit. This hypothesis is however unstable under time evolution, we provide proofs under several (increasingly more realistic) sets of assumptions. In the simplest toy model galaxies are created locally and linearly biased at a single formation time, and subsequently move with the dark matter (no velocity bias) conserving their comoving number density (no merging). We show that, after this formation time, the bias becomes unavoidably nonlocal and nonlinear at large scales. We identify the nonlocal gravitationally induced fields in which the galaxy overdensity can be expanded, showing that they can be constructed out of the invariants of the deformation tensor (Galileons), the main signature of which is a quadrupole field in second-order perturbation theory. In addition, we show that this result persists if we include an arbitrary evolution of the comoving number density of tracers. We then include velocity bias, and show that new contributions appear; these are related to the breaking of Galilean invariance of the bias relation, a dipole field being the signature at second order. We test these predictions by studying the dependence of halo overdensities in cells of fixed dark matter density: measurements in simulations show that departures from the mean bias relation are strongly correlated with the nonlocal gravitationally induced fields identified by our formalism, suggesting that the halo distribution at the present time is indeed more closely related to the mass distribution at an earlier rather than present time. However, the nonlocality seen in the simulations is not fully captured by assuming local bias in Lagrangian space. The effects on nonlocal bias seen in the simulations are most important for the most biased halos, as expected from our predictions. Accounting for these
Pickrell, M M; Rinard, P M
1992-01-01
The {sup 252}Cf shuffler assays fissile uranium and plutonium using active neutron interrogation and then counting the induced delayed neutrons. Using the shuffler, we conducted over 1700 assays of 55-gal. drums with 28 different matrices and several different fissionable materials. We measured the drums to dispose the matrix and position effects on {sup 252}Cf shuffler assays. We used several neutron flux monitors during irradiation and kept statistics on the count rates of individual detector banks. The intent of these measurements was to gauge the effect of the matrix independently from the uranium assay. Although shufflers have previously been equipped neutron monitors, the functional relationship between the flux monitor sepals and the matrix-induced perturbation has been unknown. There are several flux monitors so the problem is multivariate, and the response is complicated. Conventional regression techniques cannot address complicated multivariate problems unless the underlying functional form and approximate parameter values are known in advance. Neither was available in this case. To address this problem, we used a new technique called alternating conditional expectations (ACE), which requires neither the functional relationship nor the initial parameters. The ACE algorithm develops the functional form and performs a numerical regression from only the empirical data. We applied the ACE algorithm to the shuffler-assay and flux-monitor data and developed an analytic function for the matrix correction. This function was optimized using conventional multivariate techniques. We were able to reduce the matrix-induced-bias error for homogeneous samples to 12.7%. The bias error for inhomogeneous samples was reduced to 13.5%. These results used only a few adjustable parameters compared to the number of available data points; the data were not over fit,'' but rather the results are general and robust.
An Attempt to Target Anxiety Sensitivity via Cognitive Bias Modification
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. PMID:25692491
An attempt to target anxiety sensitivity via cognitive bias modification.
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. PMID:25692491
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…
Multivariate calibration applied to the quantitative analysis of infrared spectra
NASA Astrophysics Data System (ADS)
Haaland, David M.
1992-03-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in- situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mid- or near-infrared spectra of the blood. Progress toward the noninvasive determination of glucose levels in diabetics is an ultimate goal of this research.
The statistical analysis of multivariate serological frequency data.
Reyment, Richard A
2005-11-01
Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject. PMID:16024067
Multivariate calibration applied to the quantitative analysis of infrared spectra
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Social influence protects collective decision making from equality bias.
Hertz, Uri; Romand-Monnier, Margaux; Kyriakopoulou, Konstantina; Bahrami, Bahador
2016-02-01
A basic tenet of research on wisdom of the crowds-and key assumption of Condercet's (1785) Jury Theorem-is the independence of voters' opinions before votes are aggregated. However, we often look for others' opinions before casting our vote. Such social influence can push groups toward herding, leading to "madness of the crowds." To investigate the role of social influence in joint decision making, in Experiment 1 we had dyads of participants perform a visual oddball search task together. In the Independent (IND) condition participants initially made a private decision. If they disagreed, discussion and collective decision ensued. In the Influence (INF) condition no private decisions were made and collective decision was immediately negotiated. Dyads that did not accrue collective benefit under the IND condition improved with added social influence under the INF condition. In Experiment 2, covertly, we added noise to 1 of the dyad members' visual search display. The resulting increased heterogeneity in dyad members' performances impaired the dyadic performance under the IND condition (Bahrami et al., 2010). Importantly, dyadic performance improved with social influence under the INF condition, replicating results in Experiment 1. Further analyses revealed that under the IND condition, dyads exercised equality bias (Mahmoodi et al., 2015) by granting undue credit to the less-reliable partner. Under the INF condition, however, the more-reliable partner (correctly) dominated the joint decisions. Although social influence may impede collective success under ideal conditions, our results demonstrate how it can help the group members overcome factors such as equality bias, which could potentially lead to catastrophic failure. (PsycINFO Database Record PMID:26436525
A Bias in the Diagnosis of Schizophrenia
ERIC Educational Resources Information Center
Reade, William Kent; Wertheimer, Michael
1976-01-01
Research shows a relationship between diagnoses of schizophrenia among twins. It was studied whether information that a twin was schizophrenic would bias diagnoses. Such information almost doubled the rater's estimates of probability of schizophrenia in a hypothetical case history. (NG)
For Sale: Subliminal Bias in Textbooks
ERIC Educational Resources Information Center
Britton, Gwyneth E.; Lumpkin, Margaret C.
1977-01-01
Discusses the fact that, although publishers have acknowledged the problem of sex bias in textbooks in the United States, guidelines lack specifics, timetables, and procedures for monitoring content and enforcing compliance with the guidelines. (MB)
Autobiographical memory bias in social anxiety.
Krans, Julie; de Bree, June; Bryant, Richard A
2014-01-01
In social anxiety the psychological self is closely related to the feared stimulus. Socially anxious individuals are, by definition, concerned about how the self is perceived and evaluated by others. As autobiographical memory is strongly related to views of the self it follows that biases in autobiographical memory play an important role in social anxiety. In the present study high (n = 19) and low (n = 29) socially anxious individuals were compared on autobiographical memory bias, current goals, and self-discrepancy. Individuals high in social anxiety showed a bias towards recalling more negative and more social anxiety-related autobiographical memories, reported more current goals related to overcoming social anxiety, and showed larger self-discrepancies. The pattern of results is largely in line with earlier research in individuals with PTSD and complicated grief. This suggests that the relation between autobiographical memory bias and the self is a potentially valuable trans-diagnostic factor. PMID:24111655
Gender bias in the force concept inventory?
NASA Astrophysics Data System (ADS)
Dietz, R. D.; Pearson, R. H.; Semak, M. R.; Willis, C. W.
2012-02-01
Could the well-established fact that males tend to score higher than females on the Force Concept Inventory (FCI) be due to gender bias in the questions? The eventual answer to the question hinges on the definition of bias. We assert that a question is biased only if a factor other than ability (in this case gender) affects the likelihood that a student will answer the question correctly. The statistical technique of differential item functioning allows us to control for ability in our analysis of student performance on each of the thirty FCI questions. This method uses the total score on the FCI as the measure of ability. We conclude that the evidence for gender bias in the FCI questions is marginal at best.
FIP bias in a sigmoidal active region
NASA Astrophysics Data System (ADS)
Baker, D.; Brooks, D. H.; Démoulin, P.; van Driel-Gesztelyi, Lidia; Green, L. M.; Steed, K.; Carlyle, J.
2014-01-01
We investigate first ionization potential (FIP) bias levels in an anemone active region (AR) - coronal hole (CH) complex using an abundance map derived from Hinode/EIS spectra. The detailed, spatially resolved abundance map has a large field of view covering 359'' × 485''. Plasma with high FIP bias, or coronal abundances, is concentrated at the footpoints of the AR loops whereas the surrounding CH has a low FIP bias, ~1, i.e. photospheric abundances. A channel of low FIP bias is located along the AR's main polarity inversion line containing a filament where ongoing flux cancellation is observed, indicating a bald patch magnetic topology characteristic of a sigmoid/flux rope configuration.
Exchange bias studied with polarized neutron reflectivity
te Velthuis, S. G. E.
2000-01-05
The role of Polarized Neutron Reflectivity (PNR) for studying natural and synthetic exchange biased systems is illustrated. For a partially oxidized thin film of Co, cycling of the magnetic field causes a considerable reduction of the bias, which the onset of diffuse neutron scattering shows to be due to the loosening of the ferromagnetic domains. On the other hand, PNR measurements of a model exchange bias junction consisting of an n-layered Fe/Cr antiferromagnetic (AF) superlattice coupled with an m-layered Fe/Cr ferromagnetic (F) superlattice confirm the predicted collinear magnetization in the two superlattices. The two magnetized states of the F (along or opposite to the bias field) differ only in the relative orientation of the F and adjacent AF layer. The possibility of reading clearly the magnetic state at the interface pinpoints the commanding role that PNR is having in solving this intriguing problem.
Correcting the bias against interdisciplinary research
2014-01-01
When making decisions about funding and jobs the scientific community should recognise that most of the tools used to evaluate scientific excellence are biased in favour of established disciplines and against interdisciplinary research. PMID:24692451
Correcting the bias against interdisciplinary research.
Shapiro, Ehud
2014-01-01
When making decisions about funding and jobs the scientific community should recognise that most of the tools used to evaluate scientific excellence are biased in favour of established disciplines and against interdisciplinary research. PMID:24692451
Future research in weight bias: What next?
Alberga, Angela S; Russell-Mayhew, Shelly; von Ranson, Kristin M; McLaren, Lindsay; Ramos Salas, Ximena; Sharma, Arya M
2016-06-01
The 2015 Canadian Weight Bias Summit disseminated the newest research advances and brought together 40 experts, stakeholders, and policy makers in various disciplines in health, education, and public policy to identify future research directions in weight bias. In this paper we aim to share the results of the Summit as well as encourage international and interdisciplinary research collaborations in weight bias reduction. Consensus emerged on six research areas that warrant further investigation in weight bias: costs, causes, measurement, qualitative research and lived experience, interventions, and learning from other models of discrimination. These discussions highlighted three key lessons that were informed by the Summit, namely: language matters, the voices of people living with obesity should be incorporated, and interdisciplinary stakeholders should be included. PMID:27129601
Resolving Bias in Laser Ablation Geochronology
NASA Astrophysics Data System (ADS)
Bowring, James; Horstwood, Matthew; Gehrels, George
2013-06-01
Increasingly, scientific investigations requiring geochronology utilize laser ablation (LA)-inductively coupled plasma mass spectrometry (ICPMS), taking advantage of the efficiency and throughput possible for uranium-thorium-lead (U-Th-Pb) dating. A number of biases exist when comparing data among laboratories and an ongoing community-based effort is working to resolve and eliminate these biases to improve the accuracy of scientific interpretation based on these data.
Questions of bias in climate models
Smith, Steven J.; Wigley, Tom M.; Meinshausen, Malte; Rogelj, Joeri
2014-08-27
The recent work by Shindell usefully contributes to the debate over estimating climate sensitivity by highlighting an important aspect of the climate system: that climate forcings that occur over land result in a more rapid temperature response than forcings that are distributed more uniformly over the globe. While, as noted in this work, simple climate models may be biased by assuming the same temperature response for all forcing agents, the implication that the MAGICC model is biased in this way is not correct.
A Translational Rodent Assay of Affective Biases in Depression and Antidepressant Therapy
Stuart, Sarah A; Butler, Paul; Munafò, Marcus R; Nutt, David J; Robinson, Emma SJ
2013-01-01
The subjective measures used to study mood disorders in humans cannot be replicated in animals; however, the increasing application of objective neuropsychological methods provides opportunities to develop translational animal tasks. Here we describe a novel behavioral approach, which has enabled us to investigate similar affective biases in rodents. In our affective bias test (ABT), rats encounter two independent positive experiences—the association between food reward and specific digging substrate—during discrimination learning sessions. These are performed on separate days under either neutral conditions or during a pharmacological or affective state manipulation. Affective bias is then quantified using a preference test where both previously rewarded substrates are presented together and the rat's choices recorded. The absolute value of the experience is kept consistent and all other factors are counterbalanced so that any bias at recall can be attributed to treatment. Replicating previous findings from studies in healthy volunteers, we observe significant positive affective biases following acute treatment with typical (fluoxetine, citalopram, reboxetine, venlafaxine, clomipramine) and atypical antidepressants (agomelatine, mirtazapine), and significant negative affective biases following treatment with drugs associated with inducing negative affective states in humans (FG7142, rimonabant, 13-cis retinoic acid). We also observed that acute psychosocial stress and environmental enrichment induce significant negative and positive affective biases, respectively, and provide evidence that these affective biases involve memory consolidation. The positive and negative affective biases induced in our test also mirror the antidepressant and pro-depressant effects of these drugs in patients suggesting our test has both translational and predictive validity. Our results suggest that cognitive affective biases could contribute to drug- or stress-induced mood changes
A translational rodent assay of affective biases in depression and antidepressant therapy.
Stuart, Sarah A; Butler, Paul; Munafò, Marcus R; Nutt, David J; Robinson, Emma Sj
2013-08-01
The subjective measures used to study mood disorders in humans cannot be replicated in animals; however, the increasing application of objective neuropsychological methods provides opportunities to develop translational animal tasks. Here we describe a novel behavioral approach, which has enabled us to investigate similar affective biases in rodents. In our affective bias test (ABT), rats encounter two independent positive experiences--the association between food reward and specific digging substrate--during discrimination learning sessions. These are performed on separate days under either neutral conditions or during a pharmacological or affective state manipulation. Affective bias is then quantified using a preference test where both previously rewarded substrates are presented together and the rat's choices recorded. The absolute value of the experience is kept consistent and all other factors are counterbalanced so that any bias at recall can be attributed to treatment. Replicating previous findings from studies in healthy volunteers, we observe significant positive affective biases following acute treatment with typical (fluoxetine, citalopram, reboxetine, venlafaxine, clomipramine) and atypical antidepressants (agomelatine, mirtazapine), and significant negative affective biases following treatment with drugs associated with inducing negative affective states in humans (FG7142, rimonabant, 13-cis retinoic acid). We also observed that acute psychosocial stress and environmental enrichment induce significant negative and positive affective biases, respectively, and provide evidence that these affective biases involve memory consolidation. The positive and negative affective biases induced in our test also mirror the antidepressant and pro-depressant effects of these drugs in patients suggesting our test has both translational and predictive validity. Our results suggest that cognitive affective biases could contribute to drug- or stress-induced mood changes
NASA Astrophysics Data System (ADS)
Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali
2011-02-01
Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.
NASA Astrophysics Data System (ADS)
Ng, W.; Rasmussen, P. F.; Panu, U. S.
2009-12-01
Stochastic weather modeling is subject to a number of challenges including varied spatial-dependency and the existence of missing observations. Daily precipitation possesses unique statistical characteristics in distribution, such as the existence of high frequency of zero records and the high skewness of the distribution of precipitation amount. To address for these difficulties, a methodology based on the multivariate truncated Normal distribution model is proposed. The methodology transforms the skewed distribution of precipitation amounts at multiple sites into a multivariate Normal distribution model. The missing observations are then be estimated through the conditional mean and variance obtained from the multivariate Normal distribution model. The adequacy of the proposed model structure was first verified using a synthetic data set. Subsequently, 30 years of historical daily precipitation records from 10 Canadian meteorological stations were used to evaluate the performance of the model. The result of the evaluation shows that the proposed model reasonably can preserve the statistical characteristics of the historical records in estimated the missing records at multiple sites.
NASA Astrophysics Data System (ADS)
Gómez Araújo, Iván; Laier, Jose Elias
2015-09-01
In recent years, transmissibility functions have been used as alternatives to identify the modal parameters of structures under operating conditions. The scalar power spectrum density transmissibility (PSDT), which relates only two responses, was proposed to extract modal parameters by combining different PSDTs with different transferring outputs. In this sense, this paper proposes extending the scalar PSDT concept to multivariable PSDT by relating multiple responses instead of only two. This extension implies the definition of a transmissibility matrix, relating the cross-spectral density matrix among the responses at coordinates Z and U with the cross-spectral density matrix among the responses at coordinates Z and K. The coordinates in Z are known as the transferring outputs. By defining the same coordinates K and U, but with different transferring outputs Z, we prove that the multivariable PSDT converges to the same matrix when it approaches the system poles. This property is used to define only one matrix with different multivariable PSDTs with same coordinates K and U, but with different transferring outputs. The resulting matrix is singular at the system poles, meaning that by applying the inverse of the matrix, the modal parameters can be identified. Here, a numeric example of a beam model subjected to excitations and data from an operational vibration bridge test shows that the proposed method is capable of identifying modal parameters. Furthermore, the results demonstrate the possibility of estimating the same modal parameters by changing only the coordinates K and U, providing greater reliability during modal parameter identification.
Robust multivariable strategy and its application to a powered wheelchair.
Nguyen, Nghia; Nguyen, Hung T; Su, Steven
2009-01-01
The paper proposes a systematic robust multivariable control strategy based on combination of systematic triangularization technique and robust control strategies. Two design stages are required. In the first design stage, multivariable control problem is reduced into a series of scalar control problems via triangularization technique. For each specific scalar system, two advanced control strategies are proposed and implemented in the second design stage. The first one is based on Model Predictive Control, which is an iterative, finite horizon optimization procedure. The second control strategy is known as Neuro-Sliding Mode Control, which integrates Sliding Mode Control (SMC) and Neural Network Design to achieve both chattering-free and system robustness. Real-time implementation on a powered wheelchair system confirms that robustness and desired performance of a multivariable system under model uncertainties and unknown external disturbances can indeed be achieved by the combination of triangularization technique and Neuro-Sliding Mode Control. PMID:19963948
Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level
Tunga, M. Alper; Demiralp, Metin
2011-09-14
Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.
An update on multivariate return periods in hydrology
NASA Astrophysics Data System (ADS)
Gräler, Benedikt; Petroselli, Andrea; Grimaldi, Salvatore; De Baets, Bernard; Verhoest, Niko
2016-05-01
Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.Moreover, we preliminary investigate the ability of the multivariate return period definitions to select maximal events from a time series. Starting from a rich simulated data set, we show how similar the selection of events from a data set is. It can be deduced from the study and theoretically underpinned that the strength of correlation in the sample influences the differences between the selection of maximal events.
Are propensity scores really superior to standard multivariable analysis?
Biondi-Zoccai, Giuseppe; Romagnoli, Enrico; Agostoni, Pierfrancesco; Capodanno, Davide; Castagno, Davide; D'Ascenzo, Fabrizio; Sangiorgi, Giuseppe; Modena, Maria Grazia
2011-09-01
Clinicians often face difficult decisions despite the lack of evidence from randomized trials. Thus, clinical evidence is often shaped by non-randomized studies exploiting multivariable approaches to limit the extent of confounding. Since their introduction, propensity scores have been used more and more frequently to estimate relevant clinical effects adjusting for established confounders, especially in small datasets. However, debate persists on their real usefulness in comparison to standard multivariable approaches such as logistic regression and Cox proportional hazard analysis. This holds even truer in light of key quantitative developments such as bootstrap and Bayesian methods. This qualitative review aims to provide a concise and practical guide to choose between propensity scores and standard multivariable analysis, emphasizing strengths and weaknesses of both approaches. PMID:21616172
A note on rank reduction in sparse multivariate regression
Chen, Kun; Chan, Kung-Sik
2016-01-01
A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model. PMID:26997938
Scalable Software for Multivariate Integration on Hybrid Platforms
NASA Astrophysics Data System (ADS)
de Doncker, E.; Yuasa, F.; Kapenga, J.; Olagbemi, O.
2015-09-01
The paper describes the software infrastructure of the PARINT package for multivariate numerical integration, layered over a hybrid parallel environment with distributed memory computations (on MPI). The parallel problem distribution is typically performed on the region level in the adaptive partitioning procedure. Our objective has been to provide the end-user with state of the art problem solving power packaged as portable software. We will give test results of the multivariate ParInt engine, with significant speedups for a set of 3-loop Feynman integrals. An extrapolation with respect to the dimensional regularization parameter (ε) is applied to sequences of multivariate ParInt results Q(ε) to obtain the leading asymptotic expansion coefficients as ε → 0. This paper further introduces a novel method for a parallel computation of the Q(ε) sequence as the components of the integral of a vector function.
Properties of multivariable root loci. M.S. Thesis
NASA Technical Reports Server (NTRS)
Yagle, A. E.
1981-01-01
Various properties of multivariable root loci are analyzed from a frequency domain point of view by using the technique of Newton polygons, and some generalizations of the SISO root locus rules to the multivariable case are pointed out. The behavior of the angles of arrival and departure is related to the Smith-MacMillan form of G(s) and explicit equations for these angles are obtained. After specializing to first order and a restricted class of higher order poles and zeros, some simple equations for these angles that are direct generalizations of the SISO equations are found. The unusual behavior of root loci on the real axis at branch points is studied. The SISO root locus rules for break-in and break-out points are shown to generalize directly to the multivariable case. Some methods for computing both types of points are presented.
Neuro-sliding mode multivariable control of a powered wheelchair.
Nguyen, Nghia; Nguyen, Hung T; Su, Steven
2008-01-01
This paper proposes a neuro-sliding mode multivariable control approach for the control of a powered wheelchair system. In the first stage, a systematic decoupling technique is applied to the wheelchair system in order to reduce the multivariable control problem into two independent scalar control problems. Then two Neuro-Sliding Mode Controllers (NSMCs) are designed for these independent subsystems to guarantee system robustness under model uncertainties and unknown external disturbances. Both off-line and on-line trainings are involved in the second stage. Real-time experimental results confirm that robust performance for this multivariable wheelchair control system under model uncertainties and unknown external disturbances can indeed be achieved. PMID:19163456
Power system stability improvement with multivariable self-tuning control
Fan, J.Y.; Ortmeyer, T.H.; Mukundan, R. )
1990-02-01
A multivariable self-tuning adaptive control scheme is presented. This scheme is of a decentralized nature and is implemented locally for individual generating units. A discrete multivariable auto-regressive-moving-average model is developed to represent a generating unit. The recursive-least-squares (RLS) estimation algorithm with variable-forgetting factor and the generalized-minimum-variance control technique are utilized to synthesize the local controllers. A dynamic goal-point-generating model is introduced to provide varying goal point for the local controller which leads the subsystem output to its equilibrium gradually. Extensive simulations are performed on the IEEE 10-machine test system. The results show that the proposed multivariable adaptive control scheme is effective in damping the severe oscillations after large disturbances as well as improving the system dynamics under small oscillations and is better than the conventional PSS method. The controller demonstrates robustness and is compatible with the existing conventional controllers in multimachine systems.
Gender Bias in U.S. Pediatric Growth Hormone Treatment.
Grimberg, Adda; Huerta-Saenz, Lina; Grundmeier, Robert; Ramos, Mark Jason; Pati, Susmita; Cucchiara, Andrew J; Stallings, Virginia A
2015-01-01
Growth hormone (GH) treatment of idiopathic short stature (ISS), defined as height <-2.25 standard deviations (SD), is approved by U.S. FDA. This study determined the gender-specific prevalence of height <-2.25 SD in a pediatric primary care population, and compared it to demographics of U.S. pediatric GH recipients. Data were extracted from health records of all patients age 0.5-20 years with ≥ 1 recorded height measurement in 28 regional primary care practices and from the four U.S. GH registries. Height <-2.25 SD was modeled by multivariable logistic regression against gender and other characteristics. Of the 189,280 subjects, 2073 (1.1%) had height <-2.25 SD. No gender differences in prevalence of height <-2.25 SD or distribution of height Z-scores were found. In contrast, males comprised 74% of GH recipients for ISS and 66% for all indications. Short stature was associated (P < 0.0001) with history of prematurity, race/ethnicity, age and Medicaid insurance, and inversely related (P < 0.0001) with BMI Z-score. In conclusion, males outnumbered females almost 3:1 for ISS and 2:1 for all indications in U.S. pediatric GH registries despite no gender difference in height <-2.25 SD in a large primary care population. Treatment and/or referral bias was the likely cause of male predominance among GH recipients. PMID:26057697
Composite biasing in Monte Carlo radiative transfer
NASA Astrophysics Data System (ADS)
Baes, Maarten; Gordon, Karl D.; Lunttila, Tuomas; Bianchi, Simone; Camps, Peter; Juvela, Mika; Kuiper, Rolf
2016-05-01
Biasing or importance sampling is a powerful technique in Monte Carlo radiative transfer, and can be applied in different forms to increase the accuracy and efficiency of simulations. One of the drawbacks of the use of biasing is the potential introduction of large weight factors. We discuss a general strategy, composite biasing, to suppress the appearance of large weight factors. We use this composite biasing approach for two different problems faced by current state-of-the-art Monte Carlo radiative transfer codes: the generation of photon packages from multiple components, and the penetration of radiation through high optical depth barriers. In both cases, the implementation of the relevant algorithms is trivial and does not interfere with any other optimisation techniques. Through simple test models, we demonstrate the general applicability, accuracy and efficiency of the composite biasing approach. In particular, for the penetration of high optical depths, the gain in efficiency is spectacular for the specific problems that we consider: in simulations with composite path length stretching, high accuracy results are obtained even for simulations with modest numbers of photon packages, while simulations without biasing cannot reach convergence, even with a huge number of photon packages.
Hot-hand bias in rhesus monkeys.
Blanchard, Tommy C; Wilke, Andreas; Hayden, Benjamin Y
2014-07-01
Human decision-makers often exhibit the hot-hand phenomenon, a tendency to perceive positive serial autocorrelations in independent sequential events. The term is named after the observation that basketball fans and players tend to perceive streaks of high accuracy shooting when they are demonstrably absent. That is, both observing fans and participating players tend to hold the belief that a player's chance of hitting a shot are greater following a hit than following a miss. We hypothesize that this bias reflects a strong and stable tendency among primates (including humans) to perceive positive autocorrelations in temporal sequences, that this bias is an adaptation to clumpy foraging environments, and that it may even be ecologically rational. Several studies support this idea in humans, but a stronger test would be to determine whether nonhuman primates also exhibit a hot-hand bias. Here we report behavior of 3 monkeys performing a novel gambling task in which correlation between sequential gambles (i.e., temporal clumpiness) is systematically manipulated. We find that monkeys have better performance (meaning, more optimal behavior) for clumped (positively correlated) than for dispersed (negatively correlated) distributions. These results identify and quantify a new bias in monkeys' risky decisions, support accounts that specifically incorporate cognitive biases into risky choice, and support the suggestion that the hot-hand phenomenon is an evolutionary ancient bias. PMID:25545977
Investigating bias in squared regression structure coefficients
Nimon, Kim F.; Zientek, Linda R.; Thompson, Bruce
2015-01-01
The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients. PMID:26217273