Aims: Average information matrix splitting

Shengxin Zhu; Tongxiang Gu; Xingping Liu

doi:10.3934/mfc.2020012

Aims: Average information matrix splitting

Shengxin Zhu^*, Tongxiang Gu, Xingping Liu

^*Corresponding author for this work

School of Mathematics and Physics

IAPCM

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	301-308
Number of pages	8
Journal	Mathematical Foundations of Computing
Volume	3
Issue number	4
DOIs	https://doi.org/10.3934/mfc.2020012
Publication status	Published - Nov 2020

Keywords

Fisher information matrix
Newton method
Observed information matrix
average information
linear mixed model
variance parameter estimation

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10.3934/mfc.2020012

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title = "Aims: Average information matrix splitting",

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.",

keywords = "Fisher information matrix, Newton method, Observed information matrix, average information, linear mixed model, variance parameter estimation",

author = "Shengxin Zhu and Tongxiang Gu and Xingping Liu",

note = "Funding Information: 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). ∗ Corresponding author: Shengxin Zhu. Publisher Copyright: {\textcopyright} American Institute of Mathematical Sciences",

year = "2020",

month = nov,

doi = "10.3934/mfc.2020012",

language = "English",

volume = "3",

pages = "301--308",

journal = "Mathematical Foundations of Computing",

issn = "2577-8838",

number = "4",

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TY - JOUR

T1 - Aims

T2 - Average information matrix splitting

AU - Zhu, Shengxin

AU - Gu, Tongxiang

AU - Liu, Xingping

N1 - Funding Information: 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). ∗ Corresponding author: Shengxin Zhu. Publisher Copyright: © American Institute of Mathematical Sciences

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

KW - Fisher information matrix

KW - Newton method

KW - Observed information matrix

KW - average information

KW - linear mixed model

KW - variance parameter estimation

UR - http://www.scopus.com/inward/record.url?scp=85088536475&partnerID=8YFLogxK

U2 - 10.3934/mfc.2020012

DO - 10.3934/mfc.2020012

M3 - Article

AN - SCOPUS:85088536475

SN - 2577-8838

VL - 3

SP - 301

EP - 308

JO - Mathematical Foundations of Computing

JF - Mathematical Foundations of Computing

IS - 4

ER -

Aims: Average information matrix splitting

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Keywords

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