Bayesian Posterior Estimation of Logit Parameters with Small Samples

Francisca Galindo-Garre, Jeroen K. Vermunt and Wicher P. Bergsma
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Francisca Galindo-Garre: Department of Methodology and Statistics, Tilburg University, the Netherlands
Jeroen K. Vermunt: Department of Methodology and Statistics, Tilburg University, the Netherlands
Wicher P. Bergsma: EURANDOM, the Netherlands

Sociological Methods & Research, 2004, vol. 33, issue 1, 88-117

Abstract: When the sample size is small compared to the number of cells in a contingency table, maximum likelihood estimates of logit parameters and their associated standard errors may not exist or may be biased. This problem is usually solved by “smoothing†the estimates, assuming a certain prior distribution for the parameters. This article investigates the performance of point and interval estimates obtained by assuming various prior distributions. The authors focus on two logit parameters of a 2 × 2 × 2 table: the interaction effect of two predictors on a response variable and the main effect of one of two predictors on a response variable, under the assumption that the interaction effect is zero. The results indicate the superiority of the posterior mode to the posterior mean.

Keywords: small samples; logit models; Bayesian estimation; prior distributions (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:33:y:2004:i:1:p:88-117

DOI: 10.1177/0049124104265997

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