Determining the Effective Sample Size of a Parametric Prior
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Abstract:
We present a definition for the effective sample size of a parametric prior distribution in a Bayesian model, and propose methods for computing the effective sample size in a variety of settings. Our approach first constructs a prior chosen to be vague in a suitable sense, and updates this prior to obtain a sequence of posteriors corresponding to each of a range of sample sizes. We then compute a distance between each posterior and the parametric prior, defined in terms of the curavature of the logarithm of each distribution, and the posterior minimizing the distance defines the effective sample size of the prior. For cases where the distance cannot be computed analytically, we provide a numerical approximation based on Monte Carlo simulation. We provide general guidelines for application, illustrate the method in several standard cases where the answer seems obvious, and then apply it to some non-standard settings.
Subject Area:
Computation, General Biostatistics, Health Services Research, Statistical Theory and Methods
Suggested Citation:
Satochi Morita, Peter F. Thall, and Peter Mueller, "Determining the Effective Sample Size of a Parametric Prior" (May 2007). UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series. Working Paper 36.
http://www.bepress.com/mdandersonbiostat/paper36