Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models

Published

Journal Article

Markov chain Monte Carlo algorithms are widely used in the fitting of generalized linear models (GLMs). Such model fitting is somewhat of an art form, requiring suitable trickery and tuning to obtain results in which one can have confidence. A wide range of practical issues arise. The focus here is on parameter identifiability and posterior propriety. In particular, we clarify that nonidentifiability arises for usual GLMs and discuss its implications for simulation-based model fitting. Because often some part of the prior specification is vague, we consider whether the resulting posterior is proper, providing rather general and easily checked results for GLMs. We also show that if a Gibbs sampler is run with an improper posterior, then it may be possible to use the output to obtain meaningful inference for certain model unknowns. © 1999 Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Gelfand, AE; Sahu, SK

Published Date

  • March 1, 1999

Published In

Volume / Issue

  • 94 / 445

Start / End Page

  • 247 - 253

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.1999.10473840

Citation Source

  • Scopus