A weighted quantile regression for randomly truncated data

Weihua Zhou

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 554-566

Abstract: Quantile regression offers great flexibility in assessing covariate effects on the response. In this article, based on the weights proposed by He and Yang (2003), we develop a new quantile regression approach for left truncated data. Our method leads to a simple algorithm that can be conveniently implemented with R software. It is shown that the proposed estimator is strongly consistent and asymptotically normal under appropriate conditions. We evaluate the finite sample performance of the proposed estimators through extensive simulation studies.

Keywords: Weighted; quantile; regression; Truncated; data; Consistency; Asymptotic; normality (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:554-566

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