Details of Research Outputs

Status已发表Published
TitleAims: Average information matrix splitting
Creator
Date Issued2020-11-01
Source PublicationMathematical Foundations of Computing
Volume3Issue:4Pages:301-308
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.

Keywordaverage information Fisher information matrix linear mixed model Newton method Observed information matrix variance parameter estimation
DOI10.3934/mfc.2020012
URLView source
Indexed ByESCI
Language英语English
WOS Research AreaComputer Science
WOS SubjectComputer Science ; Theory & Methods
WOS IDWOS:000593772000007
Scopus ID2-s2.0-85088536475
Citation statistics
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/11500
CollectionResearch outside affiliated institution
Corresponding AuthorZhu, Shengxin
Affiliation
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
Recommended Citation
GB/T 7714
Zhu, Shengxin,Gu, Tongxiang,Liu, Xingping. Aims: Average information matrix splitting[J]. Mathematical Foundations of Computing, 2020, 3(4): 301-308.
APA Zhu, Shengxin, Gu, Tongxiang, & Liu, Xingping. (2020). Aims: Average information matrix splitting. Mathematical Foundations of Computing, 3(4), 301-308.
MLA Zhu, Shengxin,et al."Aims: Average information matrix splitting". Mathematical Foundations of Computing 3.4(2020): 301-308.
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Zhu, Shengxin]'s Articles
[Gu, Tongxiang]'s Articles
[Liu, Xingping]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, Shengxin]'s Articles
[Gu, Tongxiang]'s Articles
[Liu, Xingping]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, Shengxin]'s Articles
[Gu, Tongxiang]'s Articles
[Liu, Xingping]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.