Calibrated Bayes, for Statistics in General, and Missing Data in Particular

Details Calibrated Bayes, for Statistics in General, and Missing Data in Particular Calibrated Bayes, for Statistics in General, and Missing Data in Particular

2011-08-09

arxiv.org/abs/1108.1917v1

Statistical Science 2011, Vol. 26, No. 2, 162-174

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/10-STS318 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M

Scientific paper

10.1214/10-STS318

It is argued that the Calibrated Bayesian (CB) approach to statistical inference capitalizes on the strength of Bayesian and frequentist approaches to statistical inference. In the CB approach, inferences under a particular model are Bayesian, but frequentist methods are useful for model development and model checking. In this article the CB approach is outlined. Bayesian methods for missing data are then reviewed from a CB perspective. The basic theory of the Bayesian approach, and the closely related technique of multiple imputation, is described. Then applications of the Bayesian approach to normal models are described, both for monotone and nonmonotone missing data patterns. Sequential Regression Multivariate Imputation and Penalized Spline of Propensity Models are presented as two useful approaches for relaxing distributional assumptions.

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