Multivariate measurement error models based on scale mixtures of the skew-normal distribution

被引：29
|
作者：
Lachos, V. H. [1 ]
Labra, F. V. [1 ]
Bolfarine, H. [2 ]
Ghosh, Pulak [3 ]
机构：
[1] Univ Estadual Campinas, IMECC, Dept Estat, BR-13083859 Campinas, SP, Brazil
[2] Univ Sao Paulo, Dept Estat, Sao Paulo, Brazil
[3] Indian Inst Management, Dept Quantat Methods & Informat Sci, Bangalore 560076, Karnataka, India
来源：
STATISTICS | 2010年 / 44卷 / 06期
关键词：
EM algorithm; scale mixtures of the skew-normal distribution; Mahalanobis distance; measurement error models; MAXIMUM-LIKELIHOOD-ESTIMATION; LOCAL INFLUENCE; EM; ALGORITHM; SELECTION;
D O I：
10.1080/02331880903236926
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.
引用
收藏
页码：541 / 556
页数：16
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