Underestimation of Standard Errors in Multi-Site Time Series Studies

Multi-site time series studies of air pollution and mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating acute effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and estimate pooled air pollution effects taking into account both within-site statistical uncertainty, and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, highly correlated predictors, non linear confounding etc.) make modelling all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined. In this paper we investigate the impact of variance underestimation on the pooled relative rate estimate. We focus on two-stage normal-normal hierarchical models and on under- estimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation does not affect the pooled estimate substantially. However, some sensitivity of the pooled estimate to variance underestimation is observed when the number of sites is small and underestimation is severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results, and they can be easily extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity Mortality Air Pollution Study and we found that variance underestimation as much as 40% has little effect on the national average.

QUANTIFYING UNCERTAINTY IN GENOTYPE CALLS

Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Mircorarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs)simultaneously. The starting point for the statistical analyses used by GWAS, to determine association between loci and disease, are genotype calls (AA, AB, or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays, and different sample batches has substantial inuence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability, GWAS run the risk of adversely affecting the quality of reported findings. In this paper we present solutions based on a multi-level mixed model. Software implementation of the method described in this paper is available as free and open source code in the crlmm R/BioConductor.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

MODIFIED TEST STATISTICS BY INTER-VOXEL VARIANCE SHRINKAGE WITH AN APPLICATION TO fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels in the statistical analysis of fMRI data is desirable in order to improve efficiency. Here we construct a hierarchical model and apply an Empirical Bayes framework on the analysis of group fMRI data, employing techniques used in high throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels, and subsequently to construct an improved test statistic in lieu of the classical t-statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances towards a common value. The shrunken estimator for voxelspecific variance components on the group analyses outperforms the classical residual error estimator in terms of mean squared error. Moreover, the shrunken test-statistic decreases false positive rate when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.

Direct Democracy: Protest Catalyst or Protest Alternative?

This paper presents the first investigation of whether direct democracy supplements or undermines the attendance of demonstrations as a form of protest behavior. A first approach assumes that direct democracy is associated with fewer protests, as they function as a valve that integrates voters’ opinions, preferences, and emotions into the political process. A competing hypothesis proposes a positive relationship between direct democracy and this unconventional form of political participation due to educative effects. Drawing on individual data from recent Swiss Electoral Studies, we apply multilevel analysis and estimate a hierarchical model of the effect of the presence as well as the use of direct democratic institutions on individual protest behavior. Our empirical findings suggest that the political opportunity of direct democracy is associated with a lower individual probability to attend demonstrations.

Correlating the Ancient Maya and Modern European Calendars with High-Precision AMS 14C Dating

The reasons for the development and collapse of Maya civilization remain controversial and historical events carved on stone monuments throughout this region provide a remarkable source of data about the rise and fall of these complex polities. Use of these records depends on correlating the Maya and European calendars so that they can be compared with climate and environmental datasets. Correlation constants can vary up to 1000 years and remain controversial. We report a series of high-resolution AMS C-14 dates on a wooden lintel collected from the Classic Period city of Tikal bearing Maya calendar dates. The radiocarbon dates were calibrated using a Bayesian statistical model and indicate that the dates were carved on the lintel between AD 658-696. This strongly supports the Goodman-Martinez-Thompson (GMT) correlation and the hypothesis that climate change played an important role in the development and demise of this complex civilization.

Performance on auditory and visual temporal informationprocessing is related to psychometric intelligence

The present study investigated the relationship between psychometric intelligence and temporal resolution power (TRP) as simultaneously assessed by auditory and visual psychophysical timing tasks. In addition, three different theoretical models of the functional relationship between TRP and psychometric intelligence as assessed by means of the Adaptive Matrices Test (AMT) were developed. To test the validity of these models, structural equation modeling was applied. Empirical data supported a hierarchical model that assumed auditory and visual modality-specific temporal processing at a first level and amodal temporal processing at a second level. This second-order latent variable was substantially correlated with psychometric intelligence. Therefore, the relationship between psychometric intelligence and psychophysical timing performance can be explained best by a hierarchical model of temporal information processing.

Active Testing for Face Detection and Localization

We provide a novel search technique which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images. We show exponential gains in computation over traditional sliding window approaches, while keeping similar performance levels.

Negative emotional stimuli enhance vestibular processing

Recent studies have shown that vestibular stimulation can influence affective processes. In the present study, we examined whether emotional information can also modulate vestibular perception. Participants performed a vestibular discrimination task on a motion platform while viewing emotional pictures. Six different picture categories were taken from the International Affective Picture System: mutilation, threat, snakes, neutral objects, sports and erotic pictures. Using a Bayesian hierarchical approach we were able to show that vestibular discrimination improved when participants viewed emotionally negative pictures (mutilation, threat, snake) when compared to neutral objects. There was no difference in vestibular discrimination while viewing emotionally positive compared to neutral pictures. We conclude that some of the mechanisms involved in the processing of vestibular information are also sensitive to emotional content. Emotional information signals importance and mobilizes the body for action. In case of danger, a successful motor response requires precise vestibular processing. Therefore, negative emotional information improves processing of vestibular information.

Negative emotional stimuli enhance vestibular processing

Recent studies have shown that vestibular stimulation can influence affective processes. In the present study, we examined whether emotional information can also modulate vestibular perception. Participants performed a vestibular discrimination task on a motion platform while viewing emotional pictures. Six different picture categories were taken from the International Affective Picture System: mutilation, threat, snakes, neutral objects, sports and erotic pictures. Using a Bayesian hierarchical approach we were able to show that vestibular discrimination improved when participants viewed emotionally negative pictures (mutilation, threat, snake) when compared to neutral objects. There was no difference in vestibular discrimination while viewing emotionally positive compared to neutral pictures. We conclude that some of the mechanisms involved in the processing of vestibular information are also sensitive to emotional content. Emotional information signals importance and mobilizes the body for action. In case of danger, a successful motor response requires precise vestibular processing. Therefore, negative emotional information improves processing of vestibular information.

Exploring occasion specificity in the assessment of DSM-5 maladaptive personality traits: A latent state-trait theory analysis.

The alternative classification system for personality disorders in DSM-5 features a hierarchical model of maladaptive personality traits. This trait model comprises five broad trait domains and 25 specific trait facets that can be reliably assessed using the Personality Inventory for DSM-5 (PID-5). Although there is a steadily growing literature on the validity of the PID-5, issues of temporal stability and situational influences on test scores are currently unexplored. We addressed these issues using a sample of 611 research participants who completed the PID-5 three times, with time intervals of two months. Latent state-trait (LST) analyses for each of the 25 PID-5 trait facets showed that, on average, 79.5% of the variance was due to stable traits (i.e., consistency), and 7.7% of the variance was due to situational factors (i.e., occasion specificity). Our findings suggest that the PID-5 trait facets predominantly capture individual differences that are stable across time.

Reciprocity as a trigger of social cooperation in contemporary immigration societies?

While the system stabilizing function of reciprocity is widely acknowledged, much less attention has been paid to the argument that reciprocity might initiate social cooperation in the first place. This paper tests Gouldner’s early assumption that reciprocity may act as a ‘starting mechanism’ of social cooperation in consolidating societies. The empirical test scenario builds on unequal civic engagement between immigrants and nationals, as this engagement gap can be read as a lack of social cooperation in consolidating immigration societies. Empirical analyses using survey data on reciprocal norms and based on Bayesian hierarchical modelling lend support for Gouldner’s thesis, underlining thereby the relevance of reciprocity in today’s increasingly diverse societies: individual norms of altruistic reciprocity elevate immigrants’ propensity to volunteer, reducing thereby the engagement gap between immigrants and natives in the area of informal volunteering. In other words, compliance with altruistic reciprocity may trigger cooperation in social strata, where it is less likely to occur. The positive moderation of the informal engagement gap through altruistic reciprocity turns out to be most pronounced for immigrants who are least likely to engage in informal volunteering, meaning low, but also high educated immigrants.

Negative Emotional Stimuli Enhance Vestibular Processing

Recent studies have shown that vestibular stimulation can influence affective processes. In the present study, we examined whether emotional information can also modulate vestibular perception. Participants performed a vestibular discrimination task on a motion platform while viewing emotional pictures. Six different picture categories were taken from the International Affective Picture System: mutilation, threat, snakes, neutral objects, sports, and erotic pictures. Using a Bayesian hierarchical approach, we were able to show that vestibular discrimination improved when participants viewed emotionally negative pictures (mutilation, threat, snake) when compared to neutral/positive objects. We conclude that some of the mechanisms involved in the processing of vestibular information are also sensitive to emotional content. Emotional information signals importance and mobilizes the body for action. In case of danger, a successful motor response requires precise vestibular processing. Therefore, negative emotional information improves processing of vestibular information.