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
Date Written: September 1993
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
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