Journal article
Generalizing the derivation of the schwarz information criterion
Communications in statistics. Theory and methods, Vol.28(1), pp.49-66
01/01/1999
DOI: 10.1080/03610929908832282
Abstract
The Schwarz information criterion (SIC, BTC, SBC) is one of the most widely known and used tools in statistical model selection. The criterion was derived by Schwarz (1978) to serve as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. Although the original derivation assumes that the observed data is independent, identically distributed, and arising from a probability distribution in the regular exponential family, SIC has traditionally been used in a much larger scope of model selection problems. To better justify the widespread applicability of SIC, we derive the criterion in a very general framework: one which does not assume any specific form for the likelihood function, but only requires that it satisfies certain non-restrictive regularity conditions.
Details
- Title: Subtitle
- Generalizing the derivation of the schwarz information criterion
- Creators
- Joseph E Cavanaugh - Department of Statistics , University of MissouriAndrew A Neath - Department of Mathematics and Statistics , Southern Illinois University
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Theory and methods, Vol.28(1), pp.49-66
- DOI
- 10.1080/03610929908832282
- ISSN
- 0361-0926
- eISSN
- 1532-415X
- Publisher
- Marcel Dekker, Inc
- Language
- English
- Date published
- 01/01/1999
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214717802771
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