IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i17p3669-d1225150.html
   My bibliography  Save this article

Quantile Regression Based on the Weighted Approach with Dependent Truncated Data

Author

Listed:
  • Jin-Jian Hsieh

    (Department of Mathematics, National Chung Cheng University, Chia-Yi 621301, Taiwan)

  • Cheng-Chih Hsieh

    (Department of Mathematics, National Chung Cheng University, Chia-Yi 621301, Taiwan)

Abstract

This paper discusses the estimation of parameters in the quantile regression model for dependent truncated data. To account for the dependence between the survival time and the truncated time, the Archimedean copula model is used to construct the association. The parameters of the Archimedean copula model are estimated using certain existing approaches. An inference procedure based on a weighted approach is proposed, where the weights are set according to the variables of interest in the quantile regression model. The finite sample performance of the proposed approach is examined through simulations, and the method is applied to analyze two real datasets: the transfusion-related AIDS dataset and the retirement community center dataset.

Suggested Citation

  • Jin-Jian Hsieh & Cheng-Chih Hsieh, 2023. "Quantile Regression Based on the Weighted Approach with Dependent Truncated Data," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3669-:d:1225150
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/17/3669/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/17/3669/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    2. Peng, Limin & Fine, Jason P., 2009. "Competing Risks Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1440-1453.
    3. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    4. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    5. Pao-sheng Shen, 2009. "A class of rank-based test for left-truncated and right-censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 461-476, June.
    6. Stephen Portnoy & Guixian Lin, 2010. "Asymptotics for censored regression quantiles," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(1), pages 115-130.
    7. Yin, Guosheng & Zeng, Donglin & Li, Hui, 2008. "Power-Transformed Linear Quantile Regression With Censored Data," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1214-1224.
    8. Lajmi Lakhal Chaieb & Louis-Paul Rivest & Belkacem Abdous, 2006. "Estimating survival under a dependent truncation," Biometrika, Biometrika Trust, vol. 93(3), pages 655-669, September.
    9. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin-Jian Hsieh & Hong-Rui Wang, 2018. "Quantile regression based on counting process approach under semi-competing risks data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 395-419, April.
    2. Xiaoyan Sun & Limin Peng & Yijian Huang & HuiChuan J. Lai, 2016. "Generalizing Quantile Regression for Counting Processes With Applications to Recurrent Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 145-156, March.
    3. Peng, Limin, 2012. "Self-consistent estimation of censored quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 368-379.
    4. P. Čížek & S. Sadikoglu, 2018. "Bias-corrected quantile regression estimation of censored regression models," Statistical Papers, Springer, vol. 59(1), pages 215-247, March.
    5. Jiang, Rong & Qian, Weimin & Zhou, Zhangong, 2012. "Variable selection and coefficient estimation via composite quantile regression with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 308-317.
    6. Zhou, Xiuqing & Wang, Jinde, 2005. "A genetic method of LAD estimation for models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 451-466, March.
    7. Khan, Shakeeb & Powell, James L., 2001. "Two-step estimation of semiparametric censored regression models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 73-110, July.
    8. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    9. Ludsteck, Johannes & Haupt, Harald, 2007. "An Empirical Test of the Reder Hypothesis," Discussion Papers in Economics 1397, University of Munich, Department of Economics.
    10. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes," De Economist, Springer, vol. 168(4), pages 519-540, December.
    11. Liu, Haoming & Park, Cheolsung, 2004. "The evolution of the graduation-publication process," Economics of Education Review, Elsevier, vol. 23(5), pages 519-531, October.
    12. Tae-Hwan Kim & Halbert White, 2003. "Estimation, Inference, And Specification Testing For Possibly Misspecified Quantile Regression," Advances in Econometrics, in: Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later, pages 107-132, Emerald Group Publishing Limited.
    13. Blundell, Richard & Powell, James L., 2007. "Censored regression quantiles with endogenous regressors," Journal of Econometrics, Elsevier, vol. 141(1), pages 65-83, November.
    14. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    15. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    16. Pang, Lei & Lu, Wenbin & Wang, Huixia Judy, 2012. "Variance estimation in censored quantile regression via induced smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 785-796.
    17. Chen, Songnian & Khan, Shakeeb, 2003. "Rates of convergence for estimating regression coefficients in heteroskedastic discrete response models," Journal of Econometrics, Elsevier, vol. 117(2), pages 245-278, December.
    18. Machado, José A.F. & Santos Silva, J.M.C. & Wei, Kehai, 2016. "Quantiles, corners, and the extensive margin of trade," European Economic Review, Elsevier, vol. 89(C), pages 73-84.
    19. Oberhofer, Walter & Haupt, Harry, 2005. "Consistency of nonlinear regression quantiles under Type I censoring weak dependence and general covariate design," University of Regensburg Working Papers in Business, Economics and Management Information Systems 406, University of Regensburg, Department of Economics.
    20. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Dispersion estimation; Earnings risk; Censoring; Quantile regression; Occupational choice; Sorting; Risk preferences; SOEP; IABS," ECONtribute Discussion Papers Series 028, University of Bonn and University of Cologne, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3669-:d:1225150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.