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 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)

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 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

Reduced sampling protocols with Bayesian hierarchical analysis during minimal model of IVGTT

2003 ◽  
Vol 36 (15) ◽  
pp. 421-425
Author(s):  
O.F. Agbaje ◽  
S.D. Luzio ◽  
A.I.S. Albarrak ◽  
D.J. Lunn ◽  
D.R. Owens ◽  
...  
Keyword(s):  
Minimal Model ◽  
Hierarchical Analysis ◽  
Bayesian Hierarchical ◽  
Sampling Protocols

A Bayesian hierarchical analysis of stock–recruit data: quantifying structural and parameter uncertainties

2004 ◽  
Vol 61 (6) ◽  
pp. 1032-1047 ◽  
Author(s):  
Catherine GJ Michielsens ◽  
Murdoch K McAllister
Keyword(s):  
Alternative Model ◽  
Prior Probability ◽  
Stock Assessment ◽  
Population Variation ◽  
Data Sets ◽  
Model Structure ◽  
Hierarchical Analysis ◽  
Parameter Uncertainties ◽  
Bayesian Hierarchical ◽  
Parameter Values

Stock–recruit functions are important in fisheries stock assessment, but there is often uncertainty surrounding the appropriate stock–recruit model and its parameter values. Combining different stock–recruit data sets of related species through Bayesian hierarchical analysis can decrease these uncertainties and help to characterize appropriate stock–recruit forms and ranges of plausible parameter values. Two different stock–recruit functions (Beverton–Holt and Ricker) have been parameterized in terms of the steepness, which is a parameter that is comparable between populations. In the hierarchical analysis, the prior probability distribution of parameters for the cross-population variation in steepness is determined through a concise model structure. By calculating the Bayes' posteriors for alternative model forms, model uncertainty is accounted for. This methodology has been applied to Atlantic salmon (Salmo salar) stock–recruit data to provide predictions for the steepness of the stock–recruit function for Baltic salmon for which no stock–recruit data exist.

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