AIMS: Average information matrix splitting

Shengxin Zhu; Tongxiang Gu; Xingping Liu

doi:10.3934/mfc.2020012

Article Contents

2020, Volume 3, Issue 4: 301-308. Doi: 10.3934/mfc.2020012

This issue Previous Article Support vector machine classifiers by non-Euclidean margins Next Article

AIMS: Average information matrix splitting

1.
Laboratory for Intelligent Computing and Financial Technology, Department of Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
2.
Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China

^* Corresponding author: Shengxin Zhu
^* Corresponding author: Shengxin Zhu

Received: October 31, 2019

Revised: January 31, 2020

Early access: June 2020

Published: November 2020

This research is supported by Foundation of LCP(6142A05180501), Jiangsu Science and Technology Basic Research Program (BK20171237), Key Program Special Fund of XJTLU (KSF-E-21, KSF-P-02), Research Development Fund of XJTLU (RDF-2017-02-23) and partially supported by NSFC (No.11771002, 11571047, 11671049, 11671051, 6162003, and 11871339)

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Abstract

For linear mixed models with co-variance matrices which are not linearly dependent on variance component parameters, we prove that the average of the observed information and the Fisher information can be split into two parts. The essential part enjoys a simple and computational friendly formula, while the other part which involves a lot of computations is a random zero matrix and thus is negligible.

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Mathematics Subject Classification: Primary: 65J12, 62P10; Secondary: 65C60, 65F99.

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