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

Jiang-Feng Wang, Wei-Min Ma, Hui-Zeng Zhang and Li-Min Wen

Statistics & Probability Letters, 2013, vol. 83, issue 6, 1571-1579

Abstract: 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.

Keywords: Composite quantile regression; Local linear regression; Truncated data; Asymptotic normality (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:83:y:2013:i:6:p:1571-1579

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DOI: 10.1016/j.spl.2013.02.022

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