Single index quantile regression for censored data

Eliana Christou () and Michael G. Akritas ()
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Eliana Christou: University of North Carolina at Charlotte
Michael G. Akritas: The Pennsylvania State University

Statistical Methods & Applications, 2019, vol. 28, issue 4, No 4, 655-678

Abstract: Abstract Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic. It is particularly relevant for the analysis of censored survival data as an alternative to proportional hazards and the accelerated failure time models. Such data occur frequently in biostatistics, environmental sciences, social sciences and econometrics. There is a large body of work for linear/nonlinear QR models for censored data, but it is only recently that the single index quantile regression (SIQR) model has received some attention. However, the only existing method for fitting the SIQR model for censored data uses an iterative algorithm and no asymptotic theory for the resulting estimator of the parametric component is given. We propose a non-iterative estimation algorithm and derive the asymptotic distribution of the proposed estimator under heteroscedasticity. Results from simulation studies evaluating the finite sample performance of the proposed estimator are reported.

Keywords: Censored data; Dimension reduction; Index model; Quantile regression (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10260-019-00450-4

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