Fitting and testing log-linear subpopulation models with known support

Dave J. Hessen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this paper, the support of the joint probability distribution of categorical variables in the total population is treated as unknown. From a general total population model with unknown support, a general subpopulation model with its support equal to the set of all observed score patterns is derived. In maximum likelihood estimation of the parameters of any such subpopulation model, the evaluation of the log-likelihood function only requires the summation over a number of terms equal to at most the sample size. It is made clear that the parameters of a hypothesized total population model are consistently and asymptotically efficiently estimated by the values that maximize the log-likelihood function of the corresponding subpopulation model. Next, new likelihood ratio goodness-of-fit tests are proposed as alternatives to the Pearson chi-square goodness-of-fit test and the likelihood ratio test against the saturated model. In a simulation study, the asymptotic bias and efficiency of maximum likelihood estimators and the asymptotic performance of the goodness-of-fit tests are investigated.
Original languageEnglish
Pages (from-to)917-939
Number of pages23
JournalPsychometrika
Volume88
Issue number3
Early online date14 Jun 2023
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Pearson chi-square test
  • categorical variables
  • log-linear model
  • normalizing constant
  • pseudo-likelihood

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