Minimum density power divergence estimator for covariance matrix based on skew t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document} distribution

被引：0
作者：
Byungsoo Kim
Sangyeol Lee
机构：
[1] Yeungnam University,Department of Statistics
[2] Seoul National University,Department of Statistics
来源：
Statistical Methods & Applications | 2014年 / 23卷 / 4期
关键词：
Minimum density power divergence; Robust estimation; Covariance matrix; Skew ; distribution;
D O I：
10.1007/s10260-014-0284-5
中图分类号：
学科分类号：
摘要：
In this paper, we study the problem of estimating the covariance matrix of stationary multivariate time series based on the minimum density power divergence method that uses a multivariate skew t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document} distribution family. It is shown that under regularity conditions, the proposed estimator is strongly consistent and asymptotically normal. A simulation study is provided for illustration.
引用
收藏
页码：565 / 575
页数：10
相关论文
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