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A Maximum Likelihood Method for Latent Class Regression Involving a Censored Dependent Variable

Published online by Cambridge University Press:  01 January 2025

Kamel Jedidi,
Venkatram Ramaswamy and
Wayne S. Desarbo
Kamel Jedidi*
Affiliation:
Marketing Department, Graduate School of Business, Columbia University
Venkatram Ramaswamy
Affiliation:
Marketing Department, School of Business Administration, University of Michigan
Wayne S. Desarbo
Affiliation:
Marketing and Statistics Departments School of Business Administration, University of Michigan
*
Requests for reprints should be sent to Kamel Jedidi, Marketing Department, Graduate School of Business, Columbia University, New York, NY 10027.
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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.

Keywords

censored regressionlatent class analysismaximum likelihood estimationconsumer psychology

Information

Type
Original Paper
Information
Psychometrika , Volume 58 , Issue 3 , September 1993, pp. 375 - 394
Copyright
Copyright © 1993 The Psychometric Society

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Footnotes

We thank the Editor and referees for their insightful comments and suggestions.

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