Consistency of objective Bayes factors for nonnested linear models and increasing model dimension

2010 ◽  
Vol 104 (1) ◽  
pp. 57-67 ◽  
Author(s):  
F. Javier Girón ◽  
Elías Moreno ◽  
George Casella ◽  
M. L. Martínez
Keyword(s):  
Linear Models ◽  
Bayes Factors ◽  
Objective Bayes ◽  
Model Dimension

scholarly journals Consistency of objective Bayes factors as the model dimension grows

2010 ◽  
Vol 38 (4) ◽  
pp. 1937-1952 ◽  
Author(s):  
Elías Moreno ◽  
F. Javier Girón ◽  
George Casella
Keyword(s):  
Bayes Factors ◽  
Objective Bayes ◽  
Model Dimension

scholarly journals Estimating Bayes factors from minimal summary statistics in repeated measures analysis of variance designs

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Thomas Faulkenberry
Keyword(s):  
Analysis Of Variance ◽  
Repeated Measures ◽  
Bayes Factor ◽  
Linear Models ◽  
Repeated Measurements ◽  
Bayes Factors ◽  
Complex Method ◽  
Summary Statistics ◽  
Minimal Set ◽  
Repeated Measures Analysis

In this paper, I develop a formula for estimating Bayes factors directly from minimal summary statistics produced in repeated measures analysis of variance designs. The formula, which requires knowing only the F-statistic, the number of subjects, and the number of repeated measurements per subject, is based on the BIC approximation of the Bayes factor, a common default method for Bayesian computation with linear models. In addition to providing computational examples, I report a simulation study in which I demonstrate that the formula compares favorably to a recently developed, more complex method that accounts for correlation between repeated measurements. The minimal BIC method provides a simple way for researchers to estimate Bayes factors from a minimal set of summary statistics, giving users a powerful index for estimating the evidential value of not only their own data, but also the data reported in published studies.

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