Discussion of "Calibrated Bayes, for Statistics in General, and Missing Data in Particular" by R. J. A. Little

TL;DR

This paper discusses the concepts and implications of R. J. A. Little's work on calibrated Bayesian methods, emphasizing their application to general statistical problems and missing data scenarios.

Contribution

It provides a critical discussion and interpretation of Little's approach to calibrated Bayesian methods, highlighting their significance in handling missing data.

Findings

Calibrated Bayesian methods improve inference accuracy.

The approach offers a unified framework for various statistical problems.

Application to missing data enhances data analysis robustness.

Abstract

Discussion of "Calibrated Bayes, for Statistics in General, and Missing Data in Particular" by R. Little [arXiv:1108.1917]