Additive models in censored regression
نویسندگان
چکیده
In this paper we consider additive models in censored regression. We propose a randomly weighted version of the backfitting algorithm that allows for the nonparametric estimation of the effects of the covariates on the response. Given the high computational cost involved, binning techniques are used to speed up the computation in the estimation and testing process. Simulation results and the application to real data reveal that the predictor obtained with the additive model performs well, and that it is a convenient alternative to the linear predictor when some nonlinear effects are suspected. 1
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عنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 53 شماره
صفحات -
تاریخ انتشار 2009