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

Galimberti Giuliano; Scardovi Elena; Soffritti Gabriele

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

机译：在看似无关的具有非正态误差的线性回归模型中使用混合

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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.

机译：似乎引入了不相关的线性回归模型，其中误差的分布是高斯分布的有限混合。提供了可识别性条件。得出得分向量和黑森州矩阵。使用最大似然法执行参数估计，并开发了Expectation-Maximisation算法。通过对模拟数据集和真实数据集的分析，说明了所提出方法的有用性及其性质的数值评估。

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来源
《Statistics and computing》 |2016年第5期|1025-1038|共14页
作者
Galimberti Giuliano; Scardovi Elena; Soffritti Gabriele;
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作者单位

Univ Bologna, Dept Stat Sci, Via Belle Arti 41, I-40126 Bologna, Italy;

Univ Bologna, Dept Stat Sci, Via Belle Arti 41, I-40126 Bologna, Italy;

Univ Bologna, Dept Stat Sci, Via Belle Arti 41, I-40126 Bologna, Italy;

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原文格式 PDF
正文语种 eng
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关键词
EM algorithm; Gaussian mixture model; Hessian matrix; Score vector;

机译：EM算法;高斯混合模型;Hessian矩阵;得分向量;

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7. Using mixtures in seemingly unrelated linear regression models with non-normal errors [O] . Giuliano Galimberti, Elena Scardovi, Gabriele Soffritti 2015

机译：在看似无关的线性回归模型中使用混合物，具有非正常误差

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

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