Journal article
Bayesian multiple comparisons and model selection
Wiley Interdisciplinary Reviews: Computational Statistics, Vol.10(2), pp.e1420-n/a
03/2018
DOI: 10.1002/wics.1420
Abstract
The testing of multiple hypotheses is an important consideration in many statistical analyses. A theme for multiple comparisons problems under a frequentist paradigm is the need for an adjustment to control the overall error probability for the false detection of null effects. Our review will focus on Bayesian approaches to multiple comparisons problems. Under a Bayesian paradigm, multiplicity adjustments arise from a concern that many of the effects to be tested are null. We will discuss how Bayesian models provide a multiplicity adjustment through a prior placing increased probability on null effects, or through hierarchical modeling. We will also show how the Bayesian information criterion for model selection fits naturally into the study of multiple comparisons problems. WIREs Comput Stat 2018, 10:e1420. doi: 10.1002/wics.1420 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms Data: Types and Structure > Traditional Statistical Data Estimated mean differences in the log‐transformed creatine kinase levels. (1) corresponds to empirical group means; (2) corresponds to estimated group means under Bayesian model averaging; (3) corresponds to estimated group means under the highest posterior probability model.
Details
- Title: Subtitle
- Bayesian multiple comparisons and model selection
- Creators
- Andrew A Neath - Southern Illinois UniversityJavier E Flores - University of Iowa, Iowa CityJoseph E Cavanaugh - University of Iowa, Iowa City
- Resource Type
- Journal article
- Publication Details
- Wiley Interdisciplinary Reviews: Computational Statistics, Vol.10(2), pp.e1420-n/a
- DOI
- 10.1002/wics.1420
- ISSN
- 1939-5108
- eISSN
- 1939-0068
- Publisher
- John Wiley & Sons, Inc; Hoboken, USA
- Number of pages
- 9
- Language
- English
- Date published
- 03/2018
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983985990902771
Metrics
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