Article ID Journal Published Year Pages File Type
415862 Computational Statistics & Data Analysis 2012 12 Pages PDF
Abstract

Statistical inference in censored quantile regression is challenging, partly due to the unsmoothness of the quantile score function. A new procedure is developed to estimate the variance of the Bang and Tsiatis inverse-censoring-probability weighted estimator for censored quantile regression by employing the idea of induced smoothing. The proposed variance estimator is shown to be asymptotically consistent. In addition, a numerical study suggests that the proposed procedure performs well in finite samples, and it is computationally more efficient than the commonly used bootstrap method.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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