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Quantile Regression with Left-Truncated and Right-Censored Data in a Reproducing Kernel Hilbert Space

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  • Jinho Park

Abstract

Li et al. (2007) developed an estimation method for quantile functions in a reproducing kernel Hilbert space for complete data, and Park and Kim (2011) proposed an estimation method using the ε-insensitive loss. This article extends these estimation methods to left-truncated and right-censored data. As a measure of goodness of fit, the check loss and the ε-insensitive loss were used to estimate the quantile function. The ε-insensitive loss can shrink the estimated coefficients toward zero; hence, it can reduce the variability of the estimates. Simulation studies show that the estimated quantile functions based on the ε-insensitive loss perform slightly better when ε is adequately chosen.

Suggested Citation

  • Jinho Park, 2015. "Quantile Regression with Left-Truncated and Right-Censored Data in a Reproducing Kernel Hilbert Space," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(7), pages 1523-1536, April.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:7:p:1523-1536
    DOI: 10.1080/03610926.2013.777741
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    Cited by:

    1. Park, Jinho, 2017. "Solution path for quantile regression with epsilon-insensitive loss in a reproducing kernel Hilbert space," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 205-211.

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