Covariance matrix and transfer function of dynamic generalized linear models

被引：0

作者：

Guo, Guangbao ^{[1
,2
]}

You, Wenjie ^{[3
]}

Lin, Lu ^{[1
]}

Qian, Guoqi ^{[4
]}

机构：

[1] Shandong Univ, Sch Math, Jinan 250100, Peoples R China

[2] Shandong Univ Technol, Dept Stat, Zibo 255000, Peoples R China

[3] Fujian Normal Univ, Sch Elect & Informat Engn, Fuqing 350300, Peoples R China

[4] Univ Melbourne, Sch Math & Stat, Parkville, Vic 3010, Australia

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2016年 / 296卷

关键词：

Dynamic generalized linear models; Covariance matrix; Transfer function; CHAIN MONTE-CARLO;

D O I：

10.1016/j.cam.2015.10.015

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Statistical inference for dynamic generalized linear models (DGLMs) is challenging due to the time varying nature of the unknown parameters in these models. In this paper, we focus on the covariance matrix and the transfer function, the two key components in DGLMs. We first establish some convergence results for the covariance matrix estimation. We then provide an in-depth study of the transfer function on its stability and Fourier transformation, which is necessary for parameter estimation in DGLMs. Implications of our results on estimation in DGLMs are illustrated in the paper through a simulation study and a real data example. Our understanding on DGLMs has substantially improved though this study. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：613 / 624

页数：12

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