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A weighted quantile regression for randomly truncated data

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  • Zhou, Weihua

Abstract

Quantile regression offers great flexibility in assessing covariate effects on the response. In this article, based on the weights proposed by He and Yang (2003), we develop a new quantile regression approach for left truncated data. Our method leads to a simple algorithm that can be conveniently implemented with R software. It is shown that the proposed estimator is strongly consistent and asymptotically normal under appropriate conditions. We evaluate the finite sample performance of the proposed estimators through extensive simulation studies.

Suggested Citation

  • Zhou, Weihua, 2011. "A weighted quantile regression for randomly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 554-566, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:554-566
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    References listed on IDEAS

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    1. Lai, Tze Leung & Ying, Zhiliang, 1992. "Linear rank statistics in regression analysis with censored or truncated data," Journal of Multivariate Analysis, Elsevier, vol. 40(1), pages 13-45, January.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Elias Ould-Saïd & Mohamed Lemdani, 2006. "Asymptotic Properties of a Nonparametric Regression Function Estimator with Randomly Truncated Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 357-378, June.
    4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
    5. Portnoy S., 2003. "Censored Regression Quantiles," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1001-1012, January.
    6. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
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    Cited by:

    1. Hong-Xia Xu & Guo-Liang Fan & Zhen-Long Chen & Jiang-Feng Wang, 2018. "Weighted quantile regression and testing for varying-coefficient models with randomly truncated data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 565-588, October.
    2. Han-Ying Liang & Elias Ould Saïd, 2018. "A weighted estimator of conditional hazard rate with left-truncated and dependent data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 155-189, February.
    3. Frumento, Paolo & Bottai, Matteo, 2017. "An estimating equation for censored and truncated quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 53-63.
    4. Du, Jiang & Zhang, Zhongzhan & Xie, Tianfa, 2018. "A weighted M-estimator for linear regression models with randomly truncated data," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 90-94.
    5. Jung-Yu Cheng & Shinn-Jia Tzeng, 2014. "Quantile regression of right-censored length-biased data using the Buckley–James-type method," Computational Statistics, Springer, vol. 29(6), pages 1571-1592, December.
    6. Hong-Xia Xu & Zhen-Long Chen & Jiang-Feng Wang & Guo-Liang Fan, 2019. "Quantile regression and variable selection for partially linear model with randomly truncated data," Statistical Papers, Springer, vol. 60(4), pages 1137-1160, August.

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