A screening selection procedure for nonparametric regression and survival analysis

Abstract

This thesis aims at proposing a new method of solving the nonparametric and non-additive regression problem in presence of ultra-high dimensional data. In this context, there are two relevant aspects: variable selection and structure discovery, such as identification of the variables that affect the response variable and the type of effects (linear or non linear), respectively. In this thesis we propose a nonparametric method of variable selection that works in two stages. At the first stage, a screening procedure is performed: selecting a subset of variables which contains the true covariates with probability 1. .. [edited by the Author]