Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions

被引：5

作者：

Cao, Chunzheng ^{[1
]}

Chen, Mengqian ^{[1
]}

Wang, Yahui ^{[1
]}

Shi, Jian Qing ^{[2
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Sch Math & Stat, Nanjing, Jiangsu, Peoples R China

[2] Newcastle Univ, Sch Math & Stat, Newcastle Upon Tyne, Tyne & Wear, England

来源：

COMPUTATIONAL STATISTICS | 2018年 / 33卷 / 01期

基金：

美国国家科学基金会;

关键词：

Scale mixtures of skew-normal distributions; Maximum likelihood estimates; EM algorithm; Robustness; SCALE MIXTURES; INFLUENCE DIAGNOSTICS; VARIABLES; LIKELIHOOD; REGRESSION; INFERENCE; SUBJECT; VARY; EM;

D O I：

10.1007/s00180-017-0720-8

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：

We propose a heteroscedastic replicated measurement error model based on the class of scale mixtures of skew-normal distributions, which allows the variances of measurement errors to vary across subjects. We develop EM algorithms to calculate maximum likelihood estimates for the model with or without equation error. An empirical Bayes approach is applied to estimate the true covariate and predict the response. Simulation studies show that the proposed models can provide reliable results and the inference is not unduly affected by outliers and distribution misspecification. The method has also been used to analyze a real data of plant root decomposition.

引用

页码：319 / 338

页数：20

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