A maximum likelihood method for latent class regression involving a censored dependent variable

Kamel Jedidi, Venkatram Ramaswamy, Wayne S. Desarbo

Research output: Contribution to journalArticle

36 Scopus citations

Abstract

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.

Original languageEnglish (US)
Pages (from-to)375-394
Number of pages20
JournalPsychometrika
Volume58
Issue number3
DOIs
StatePublished - Sep 1 1993

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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