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Penalized regression across multiple quantiles under random censoring

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  • Tang, Yanlin
  • Wang, Huixia Judy

Abstract

In quantile regression, it is of interest to determine whether a covariate has varying or constant effect across quantiles, since in situations where the quantile coefficients share some common features we can improve the estimation efficiency through joint modeling of multiple quantiles. To automatically perform estimation and detection of the interquantile commonality, we propose a new penalization procedure with two variations of interquantile penalties for censored quantile regression. The proposed methods are shown to be consistent in separating the constant and varying effects across quantiles, and the resulting slope estimators have the same asymptotic efficiency with the oracle estimators obtained as if the true interquantile model structure is known a priori. Our simulation study suggests that the proposed estimators have competitive or higher efficiency than the existing estimator obtained by fitting censored quantile regression at each quintile level separately. The practical value of the proposed methods is further illustrated through the analysis of a renal disease data.

Suggested Citation

  • Tang, Yanlin & Wang, Huixia Judy, 2015. "Penalized regression across multiple quantiles under random censoring," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 132-146.
    • Handle: RePEc:eee:jmvana:v:141:y:2015:i:c:p:132-146
    DOI: 10.1016/j.jmva.2015.07.006
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2020. "Linear censored quantile regression: A novel minimum‐distance approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1275-1306, December.

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