Autori: Christou, Eliana , Akritas, Michael
Titolo: Single index quantile regression for censored data
Periodico: Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2019 - Volume: 28 - Fascicolo: 4 - Pagina iniziale: 655 - Pagina finale: 678

Quantile regression (QR) has become a popular method of data analysis, especiallywhen the error term is heteroscedastic. It is particularly relevant for the analysis ofcensored survival data as an alternative to proportional hazards and the accelerated fail-ure time models. Such data occur frequently in biostatistics, environmental sciences,social sciences and econometrics. There is a large body of work for linear/nonlinearQR models for censored data, but it is only recently that the single index quantileregression (SIQR) model has received some attention. However, the only existingmethod for fitting the SIQR model for censored data uses an iterative algorithm andno asymptotic theory for the resulting estimator of the parametric component is given.We propose a non-iterative estimation algorithm and derive the asymptotic distributionof the proposed estimator under heteroscedasticity. Results from simulation studiesevaluating the finite sample performance of the proposed estimator are reported.


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SICI: 1618-2510(2019)28:4<655:SIQRFC>2.0.ZU;2-Q

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