Bayesian hierarchical analysis of within-units variances in repeated measures experiments

1994 ◽  
Vol 13 (18) ◽  
pp. 1841-1852 ◽  
Author(s):  
Thomas R. Ten Have ◽  
Vernon M. Chinchilli
Keyword(s):  
Repeated Measures ◽  
Hierarchical Analysis ◽  
Bayesian Hierarchical

scholarly journals Theta-gamma cross-frequency transcranial alternating current stimulation over the trough impairs cognitive control

2020 ◽  
Author(s):  
Zsolt Turi ◽  
Matthias Mittner ◽  
Albert Lehr ◽  
Hannah Bürger ◽  
Andrea Antal ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Repeated Measures ◽  
Instrumental Learning ◽  
Learning Task ◽  
Alternating Current ◽  
Double Blind ◽  
Bayesian Hierarchical ◽  
Theta Wave ◽  
Gamma Frequency ◽  
Transcranial Alternating Current Stimulation

Cognitive control is a hypothetical mental process, which underlies adaptive goal-directed decisions. Previous studies have linked cognitive control to electrophysiological fluctuations in the theta band and theta-gamma cross-frequency coupling (CFC) arising from the cingulate and frontal cortices. Yet, to date the behavioral consequences of different forms of theta-gamma CFC remain elusive. Here, we studied the behavioral effects of the theta-gamma CFC via transcranial alternating current stimulation (tACS) designed to stimulate the frontal and cingulate cortices. Using a double-blind, randomized, repeated measures study design, 24 healthy participants were subjected to three main, active CFC-tACS protocols: Short gamma frequency bursts (80 Hz) were coupled to an ongoing theta cycle (4 Hz) to coincide with either the peaks or the troughs of the theta wave. In a third condition, the amplitude of the gamma oscillation was modulated by the phase of a theta cycle. In the fourth, control protocol, gamma was continuously superimposed over the theta cycle, therefore lacking any phase-specificity in the CFC. During the 20-minute stimulations, the participants performed a Go/NoGo monetary reward- and punishment-based instrumental learning task. A Bayesian hierarchical logistic regression analysis revealed that CFC-tACS over peak had no effects on the behavioral performance, whereas CFC-tACS over trough and, to a lesser extent, amplitude-modulated tACS reduced performance in conflicting trials. Our results suggest that cognitive control depends on the phase-specificity of the theta-gamma CFC.

scholarly journals Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments

2019 ◽  
Vol 11 (1) ◽  
pp. 57-91 ◽  
Author(s):  
Rachael Meager
Keyword(s):  
External Validity ◽  
Hierarchical Models ◽  
Hierarchical Analysis ◽  
Randomized Experiments ◽  
Average Effect ◽  
Bayesian Hierarchical ◽  
Heterogeneous Effects ◽  
Average Impact ◽  
The Impact ◽  
Better Than

Despite evidence from multiple randomized evaluations of microcredit, questions about external validity have impeded consensus on the results. I jointly estimate the average effect and the heterogeneity in effects across seven studies using Bayesian hierarchical models. I  find the impact on household business and consumption variables is unlikely to be transformative and may be negligible. I find reasonable external validity: true heterogeneity in effects is moderate, and approximately 60 percent of observed heterogeneity is sampling variation. Households with previous business experience have larger but more heterogeneous effects. Economic features of microcredit interventions predict variation in effects better than studies’ evaluation protocols. (JEL D14, G21, I38, O12, O16, P34, P36)

scholarly journals MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Brian E. Vestal ◽  
Camille M. Moore ◽  
Elizabeth Wynn ◽  
Laura Saba ◽  
Tasha Fingerlin ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Repeated Measures ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical

Bayesian Hierarchical Analysis for Multiple Health Endpoints in a Toxicity Study

2010 ◽  
Vol 15 (3) ◽  
pp. 290-307 ◽  
Author(s):  
Taeryon Choi ◽  
Mark J. Schervish ◽  
Ketra A. Schmitt ◽  
Mitchell J. Small
Keyword(s):  
Toxicity Study ◽  
Hierarchical Analysis ◽  
Bayesian Hierarchical ◽  
Multiple Health

scholarly journals What’s in a Name: A Bayesian Hierarchical Analysis of the Name-Letter Effect

2012 ◽  
Vol 3 ◽  
Author(s):  
Oliver Dyjas ◽  
Raoul P. P. P. Grasman ◽  
Ruud Wetzels ◽  
Han L. J. van der Maas ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Hierarchical Analysis ◽  
Bayesian Hierarchical

scholarly journals Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature

2017 ◽  
Author(s):  
Rachael Meager
Keyword(s):  
Treatment Effects ◽  
Randomized Trials ◽  
Economic Knowledge ◽  
Hierarchical Analysis ◽  
Discrete Variables ◽  
Unknown Parameters ◽  
Distributional Effects ◽  
Continuous Outcome ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework

This paper develops methods to aggregate evidence on distributional treatment effects from multiple studies conducted in different settings, and applies them to the microcredit literature. Several randomized trials of expanding access to microcredit found substantial effects on the tails of household outcome distributions, but the extent to which these findings generalize to future settings was not known. Aggregating the evidence on sets of quantile effects poses additional challenges relative to average effects because distributional effects must imply monotonic quantiles and pass information across quantiles. Using a Bayesian hierarchical framework, I develop new models to aggregate distributional effects and assess their generalizability. For continuous outcome variables, the methodological challenges are addressed by applying transforms to the unknown parameters. For partially discrete variables such as business profits, I use contextual economic knowledge to build tailored parametric aggregation models. I find generalizable evidence that microcredit has negligible impact on the distribution of various household outcomes below the 75th percentile, but above this point there is no generalizable prediction.

Demographic and Behavioral Modifiers of Arsenic Exposure Pathways: A Bayesian Hierarchical Analysis of NHEXAS Data

2008 ◽  
Vol 42 (15) ◽  
pp. 5607-5614 ◽  
Author(s):  
Thomas J. Santner ◽  
Peter F. Craigmile ◽  
Catherine A. Calder ◽  
Rajib Paul
Keyword(s):  
Arsenic Exposure ◽  
Hierarchical Analysis ◽  
Bayesian Hierarchical ◽  
Exposure Pathways

scholarly journals A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures

2017 ◽  
Vol 16 (5-6) ◽  
Author(s):  
Christopher McMahan ◽  
James Baurley ◽  
William Bridges ◽  
Chase Joyner ◽  
Muhamad Fitra Kacamarga ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Posteriori Probability ◽  
Computationally Efficient ◽  
Bayesian Hierarchical ◽  
A Posteriori Probability ◽  
Different Seasons ◽  
Genomic Studies ◽  
Rice Yields ◽  
Growing Conditions ◽  
Shrinkage Prior

AbstractGenomic studies of plants often seek to identify genetic factors associated with desirable traits. The process of evaluating genetic markers one by one (i.e. a marginal analysis) may not identify important polygenic and environmental effects. Further, confounding due to growing conditions/factors and genetic similarities among plant varieties may influence conclusions. When developing new plant varieties to optimize yield or thrive in future adverse conditions (e.g. flood, drought), scientists seek a complete understanding of how the factors influence desirable traits. Motivated by a study design that measures rice yield across different seasons, fields, and plant varieties in Indonesia, we develop a regression method that identifies significant genomic factors, while simultaneously controlling for field factors and genetic similarities in the plant varieties. Our approach develops a Bayesian maximum a posteriori probability (MAP) estimator under a generalized double Pareto shrinkage prior. Through a hierarchical representation of the proposed model, a novel and computationally efficient expectation-maximization (EM) algorithm is developed for variable selection and estimation. The performance of the proposed approach is demonstrated through simulation and is used to analyze rice yields from a pilot study conducted by the Indonesian Center for Rice Research.

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