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A Maximum Likelihood Method for Piecewise Regression Models with a Continuous Dependent Variable

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  • Asher Tishler
  • Isreal Zang

Abstract

This paper develops a maximum likelihood method for estimating a piecewise regression model with a continuous dependent variable. The method employs an approximation to the “max” operator in the likelihood function. The use of this method is very simple and involves almost no numerical difficulties, because it allows the use of analytical derivatives of the likelihood function. The method proposed converges rapidly and may be used with an arbitrary number of regimes and variables.

Suggested Citation

  • Asher Tishler & Isreal Zang, 1981. "A Maximum Likelihood Method for Piecewise Regression Models with a Continuous Dependent Variable," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(2), pages 116-124, June.
    • Handle: RePEc:bla:jorssc:v:30:y:1981:i:2:p:116-124
    DOI: 10.2307/2346380
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    Cited by:

    1. Jin Liu & Robert A. Perera & Le Kang & Roy T. Sabo & Robert M. Kirkpatrick, 2022. "Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spaces," Journal of Educational and Behavioral Statistics, , vol. 47(2), pages 167-201, April.

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