Estimating parameters in the single-index model under censoring: a comparative study
نویسندگان
چکیده
In this paper we propose a new method for estimating parameters in a single-index model under censoring based on the Beran estimator for conditional distribution function. This, likelihood based, method is also used for the bandwidth selection. Hence the proposed method is a useful and simple tool for selecting the bandwidth also for the Beran estimator with one dimentional covariate. Additionally, we recall an another method which base on Kaplan-Meier integrals and compare the two approaches in a simulation study. We apply both methods to primary biliary cirrhosis data set and propose the bootstrap test for the parameters.
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