| Autor: |
Panga L; Department of Statistics, North Carolina State University, Raleigh, NC 27606, U.S.A., Lu W, Wang HJ |
| Jazyk: |
angličtina |
| Zdroj: |
Computational statistics & data analysis [Comput Stat Data Anal] 2012 Apr 01; Vol. 56 (4), pp. 785-796. Date of Electronic Publication: 2010 Apr 21. |
| DOI: |
10.1016/j.csda.2010.10.018 |
| Abstrakt: |
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 Bang and Tsiatis's 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, numerical study suggests that the proposed procedure performs well in finite samples, and it is computationally more efficient than the commonly used bootstrap method. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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