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

Psychometrika, Volume 58, Issue 3, pp 375-394 (1993)

20 Pages Posted: 4 Jun 2016

See all articles by Kamel Jedidi

Kamel Jedidi

Columbia Business School - Marketing

Venkatram Ramaswamy

University of Michigan, Stephen M. Ross School of Business

Wayne S. DeSarbo

Pennsylvania State University

Date Written: September 1993

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 regression, latent class analysis, maximum likelihood estimation, consumer psychology

Suggested Citation

Jedidi, Kamel and Ramaswamy, Venkatram and DeSarbo, Wayne S., A Maximum Likelihood Method for Latent Class Regression Involving a Censored Dependent Variable (September 1993). Psychometrika, Volume 58, Issue 3, pp 375-394 (1993). Available at SSRN: https://ssrn.com/abstract=2789063

Kamel Jedidi

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

Venkatram Ramaswamy

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109-1234
United States
734-763-5932 (Phone)
734-936-0279 (Fax)

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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