Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4664761 | Acta Mathematica Scientia | 2006 | 10 Pages |
Abstract
A kernel-type estimator of the quantile function Q(p) = inf {t : F(t) ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematics (General)