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

Kamel Jedidi, Venkatram Ramaswamy, Wayne S. Desarbo

Research output: Contribution to journalArticlepeer-review

45 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 1993

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'A maximum likelihood method for latent class regression involving a censored dependent variable'. Together they form a unique fingerprint.

Cite this