Local linear quantile regression with truncated and dependent data

In this paper, we construct a nonparametric regression quantile estimator by using the local linear fitting for left-truncated data, and establish the Bahadur-type representation and asymptotic normality of the proposed estimator when the observations form a stationary α-mixing sequence. Finite-sample performance of the estimator is investigated via simulation studies.

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Year of publication:	2015
Authors:	Wang, Jiang-Feng ; Ma, Wei-Min ; Fan, Guo-Liang ; Wen, Li-Min
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 96.2015, C, p. 232-240
Publisher:	Elsevier
Subject:	Asymptotic normality \| Regression quantile \| Local linear fitting \| Truncated data \| α-mixing

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011115976