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Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman & Donald B. Rubin, and Robert M. Hauser, and a Rejoinder) (1995)
| Venue: | SOCIOLOGICAL METHODOLOGY 1995, EDITED BY PETER V. MARSDEN, CAMBRIDGE,; MASS.: BLACKWELLS. |
| Citations: | 583 - 21 self |
BibTeX
@MISC{Raftery95bayesianmodel,
author = {Adrian Raftery},
title = {Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman & Donald B. Rubin, and Robert M. Hauser, and a Rejoinder)},
year = {1995}
}
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Abstract
It is argued that P-values and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a single model, they ignore model uncertainty and so underestimate the uncertainty about quantities of interest. The Bayesian approach to hypothesis testing, model selection and accounting for model uncertainty is presented. Implementing this is straightforward using the simple and accurate BIC approximation, and can be done using the output from standard software. Specific results are presented for most of the types of model commonly used in sociology. It is shown that this approach overcomes the difficulties with P values and standard model selection procedures based on them. It also allows easy comparison of non-nested models, and permits the quantification of the evidence for a null hypothesis...
Keyphrases
social research andrew gelman donald bayesian model selection model uncertainty misleading result standard software non-nested model standard model selection procedure standard variable selection procedure easy comparison many candidate independent variable specific result single model bayesian approach large sample accurate bic approximation null hypothesis model selection unsatisfactory result
