Using mixtures in seemingly unrelated linear regression models with non-normal errors

被引：10

作者：

Galimberti, Giuliano ^{[1
]}

Scardovi, Elena ^{[1
]}

Soffritti, Gabriele ^{[1
]}

机构：

[1] Univ Bologna, Dept Stat Sci, Via Belle Arti 41, I-40126 Bologna, Italy

来源：

STATISTICS AND COMPUTING | 2016年 / 26卷 / 05期

关键词：

EM algorithm; Gaussian mixture model; Hessian matrix; Score vector; LEAST-SQUARES ESTIMATORS; DIRECT MONTE-CARLO; MAXIMUM-LIKELIHOOD; BAYESIAN-ANALYSIS; PREDICTION; EQUATIONS; INFERENCE; SELECTION;

D O I：

10.1007/s11222-015-9587-0

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Seemingly unrelated linear regression models are introduced in which the distribution of the errors is a finite mixture of Gaussian distributions. Identifiability conditions are provided. The score vector and the Hessian matrix are derived. Parameter estimation is performed using the maximum likelihood method and an Expectation-Maximisation algorithm is developed. The usefulness of the proposed methods and a numerical evaluation of their properties are illustrated through the analysis of simulated and real datasets.

引用

页码：1025 / 1038

页数：14

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