Asymptotic normality for a local composite quantile regression estimator of regression function with truncated data

In this paper, we construct a local linear composite quantile regression (CQR) estimator of regression function for left-truncated data, which extends the CQR method to the left-truncated model. The asymptotic normality of the proposed estimator is also established. The estimator is much more efficient than the local linear regression estimator for commonly-used non-normal error distributions via simulations.

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Year of publication:	2013
Authors:	Wang, Jiang-Feng ; Ma, Wei-Min ; Zhang, Hui-Zeng ; Wen, Li-Min
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 6, p. 1571-1579
Publisher:	Elsevier
Subject:	Composite quantile regression \| Local linear regression \| Truncated data \| Asymptotic normality

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

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