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A Stochastic EM Algorithm for Quantile and Censored Quantile Regression Models

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  • Fengkai Yang

    (Shandong University
    Shandong University)

Abstract

We proposed a stochastic EM algorithm for quantile and censored quantile regression models in order to circumvent some limitations of the EM algorithm and Gibbs sampler. We conducted several simulation studies to illustrate the performance of the algorithm and found that the procedure performs as better as the Gibbs sampler, and outperforms the EM algorithm in uncensored situation. Finally we applied the methodology to the classical Engel food expenditure data and the labour supply data with left censoring, finding that the SEM algorithm behaves more satisfying than the Gibbs sampler does.

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  • Fengkai Yang, 2018. "A Stochastic EM Algorithm for Quantile and Censored Quantile Regression Models," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 555-582, August.
    • Handle: RePEc:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9704-6
    DOI: 10.1007/s10614-017-9704-6
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    References listed on IDEAS

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    7. Yu, Keming & Stander, Julian, 2007. "Bayesian analysis of a Tobit quantile regression model," Journal of Econometrics, Elsevier, vol. 137(1), pages 260-276, March.
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    Cited by:

    1. Hongbin Zhang, 2023. "Stochastic EM Algorithm for Joint Model of Logistic Regression and Mechanistic Nonlinear Model in Longitudinal Studies," Mathematics, MDPI, vol. 11(10), pages 1-14, May.

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