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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.

Suggested Citation

  • 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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    1. Rahim Alhamzawi & Keming Yu, 2012. "Variable selection in quantile regression via Gibbs sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 799-813, August.
    2. Tian, Yuzhu & Zhu, Qianqian & Tian, Maozai, 2016. "Estimation of linear composite quantile regression using EM algorithm," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 183-191.
    3. Yuzhu Tian & Maozai Tian & Qianqian Zhu, 2014. "Linear Quantile Regression Based on EM Algorithm," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(16), pages 3464-3484, August.
    4. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    5. Yu, Keming & Stander, Julian, 2007. "Bayesian analysis of a Tobit quantile regression model," Journal of Econometrics, Elsevier, vol. 137(1), pages 260-276, March.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    8. Mroz, Thomas A, 1987. "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," Econometrica, Econometric Society, vol. 55(4), pages 765-799, July.
    9. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
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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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