Accession Number:

ADA151242

Title:

Dropping Observations without Affecting Posterior Predictive Distributions,

Descriptive Note:

Corporate Author:

WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER

Personal Author(s):

• Draper, N. R.
• Guttman, I.

Report Date:

1983-01-01

Pagination or Media Count:

4.0

Abstract:

Suppose in a distribution problem, the sample information w is split into two pieces W1 and W2, and the parameters involved are split into two sets, phi containing the parameters of interest, and theta containing nuisance parameters. It is shown that, under certain conditions, the posterior distribution of phi does not depend on the data W2, which can thus be ignored. This also has con-sequences for the predictive distribution of future or missing observations. In fact, under similar conditions, the predictive distributions using W or just W are identical. Keywords include Dropping observations, Posterior distribution, Predictive distribution, Bayesian inference.

Descriptors:

• *STATISTICAL DISTRIBUTIONS
• BAYES THEOREM
• MATHEMATICAL PREDICTION
• REPRINTS
• STATISTICAL INFERENCE

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE