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

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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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Authors and Affiliations

  1. Marketing Department, Graduate School of Business, Columbia University, 10027, New York, NY

    Kamel Jedidi

  2. Marketing Department School of Business Administration, University of Michigan, USA

    Venkatram Ramaswamy

  3. Marketing and Statistics Departments School of Business Administration, University of Michigan, USA

    Wayne S. Desarbo

Authors
  1. Kamel Jedidi
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  2. Venkatram Ramaswamy
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  3. Wayne S. Desarbo
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Jedidi, K., Ramaswamy, V. & Desarbo, W.S. A maximum likelihood method for latent class regression involving a censored dependent variable. Psychometrika 58, 375–394 (1993). https://doi.org/10.1007/BF02294647

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  • DOI: https://doi.org/10.1007/BF02294647

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