A1165
Title: Finite mixtures of multivariate contaminated normal censored regression models
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Tsung-I Lin - National Chung Hsing University (Taiwan)
Abstract: The complexity of model-based clustering grows as outliers become more prevalent, compounded by restrictions imposed by the detection of quantification. A finite mixture of multivariate contaminated normal censored regression (FM-MCNCR) model tailored for handling censored data in a linear regression scenario is introduced. For the estimation of model parameters, a computationally analytical alternating expectation conditional maximization (AECM) algorithm is devised. Additionally, an information matrix-based formula to approximate the asymptotic standard errors of parameter estimates is presented. Importantly, the AECM algorithm serves a dual role by not only facilitating parameter estimation but also providing methods to recover censored measurements and detect outlier data points as a by-product when it converges. The efficacy and advantages of the proposed methodology are illustrated through a simulation study and an example related to U.S. women's labor force participation data.
