Additive models in censored regression

Jacobo de Uña Álvarez and Javier Roca Pardiñas

Computational Statistics & Data Analysis, 2009, vol. 53, issue 9, 3490-3501

Abstract: Additive models in censored regression are considered. A randomly weighted version of the backfitting algorithm that allows for the nonparametric estimation of the effects of the covariates on the response is provided. 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 expected.

Date: 2009
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