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Title: Estimation for partially linear censored regression models based on heavy tailed distributions Authors:  Marcela Lemus - Campinas State University - UNICAMP (Colombia)
Victor Hugo Lachos Davila - University of Connecticut (United States)
Christian Eduardo Galarza Morales - Escuela Superior Politecnica del Litoral (Ecuador)
Larissa Matos - Campinas State University - UNICAMP (Brazil) [presenting]
Abstract: In many studies, limited or censored data are collected. This occurs, in several practical situations, for reasons such as limitations of measuring instruments or due to experimental design. So, the responses can be either left, interval or right censored. On the other hand, partially linear models are considered as a flexible generalizations of linear regression models by including a nonparametric component of some covariates in the linear predictor. We discuss an estimation procedure in partially linear censored regression models with errors following a scale mixture of normal (SMN) distributions. This family of distributions contains a group of well-known heavy-tailed distributions that are often used for robust inference of symmetrical data, such as Student t, slash and contaminated normal, among others. A simple EM type algorithm for iteratively computing maximum penalized likelihood (MPL) estimates of the parameters is presented. We evaluate the finite sample performance of the algorithm and the asymptotic properties of the MPL estimates through empirical experiments. An application to a real dataset is presented to illustrate the effectiveness of the proposed methods.