Bayesian posterior estimation of logit parameters with small samples

F. Galindo-Garre, J.K. Vermunt, W.P. Bergsma

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)


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 x 2 x 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.
Original languageEnglish
Pages (from-to)88-117
JournalSociological Methods and Research
Issue number1
Publication statusPublished - 2004


Dive into the research topics of 'Bayesian posterior estimation of logit parameters with small samples'. Together they form a unique fingerprint.

Cite this