A maximum likelihood method for latent class regression involving a censored dependent variable |
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| Authors: | Kamel Jedidi Venkatram Ramaswamy Wayne S Desarbo |
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| Institution: | (1) Marketing Department, Graduate School of Business, Columbia University, 10027 New York, NY;(2) Marketing Department School of Business Administration, University of Michigan, USA;(3) Marketing and Statistics Departments School of Business Administration, University of Michigan, USA |
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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. |
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| Keywords: | censored regression latent class analysis maximum likelihood estimation consumer psychology |
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