A0185
Title: Multivariate contaminated normal censored regression model: Properties and maximum likelihood inference
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Abstract: The multivariate contaminated normal (MCN) distribution, which contains two extra parameters with respect to parameters of the multivariate normal distribution, one for controlling the proportion of mild outliers and the other for specifying the degree of contamination, has been widely applied in robust statistics in the case of elliptically heavy-tailed empirical distributions. The MCN model is extended to data with possibly censored values due to limits of quantification, referred to as the MCN with censoring (MCN-C) model, and further establishes the censored multivariate linear regression model where the random errors have the MCN distribution, named as the MCN censored regression (MCN-CR) model. Two computationally feasible expectation conditional maximization (ECM) algorithms are developed for the maximum likelihood estimation of MCN-C and MCN-CR models. An information-based method is used to approximate the standard errors of location parameters and regression coefficients. The capability and superiority of the proposed models are illustrated by a real-data example and simulation studies.
