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May 2011 Calibrated Bayes, for Statistics in General, and Missing Data in Particular
Roderick Little
Statist. Sci. 26(2): 162-174 (May 2011). DOI: 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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Roderick Little. "Calibrated Bayes, for Statistics in General, and Missing Data in Particular." Statist. Sci. 26 (2) 162 - 174, May 2011.


Published: May 2011
First available in Project Euclid: 1 August 2011

zbMATH: 1246.62054
MathSciNet: MR2858391
Digital Object Identifier: 10.1214/10-STS318

Keywords: maximum likelihood , multiple imputation , penalized splines , propensity models , sequential regression multivariate imputation

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.26 • No. 2 • May 2011
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