The quantile process under random censoring
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Date
2012-02-28
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Abstract
In this paper we discuss the asymptotical properties of quantile processes under random
censoring. In contrast to most work in this area we prove weak convergence of an appropriately standardized quantile process under the assumption that the quantile regression model is only linear in the region, where the process is investigated. Additionally, we also discuss
properties of the quantile process in sparse regression models including quantile processes obtained from the Lasso and adaptive Lasso. The results are derived by a combination of modern empirical process theory, classical martingale methods and a recent result of Kato (2009).
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Keywords
censored data, quantile regression, variable selection, weak convergence