科研成果详情

题名Fast calculation of restricted maximum likelihood methods for unstructured high-throughput data
作者
发表日期2017-10-20
会议名称2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017
会议录名称2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017
页码40-43
会议日期10 March 2017- 12 March 2017
会议地点Beijing
摘要

Linear mixed models are often used for analysing unbalanced data with certain missing values in a broad range of applications. The restricted maximum likelihood method is often preferred to estimate co-variance parameters in such models due to its unbiased estimation of the underlying variance parameters. The restricted log-likelihood function involves log determinants of a complicated co-variance matrix which are computational prohibitive. An efficient statistical estimate of the underlying model parameters and quantifying the accuracy of the estimation requires the observed or the Fisher information matrix. Standard approaches to compute the observed and Fisher information matrix are computationally prohibitive. Customized algorithms are of highly demand to keep the restricted log-likelihood method scalable for increasing high-throughput unbalanced data sets. In this paper, we explore how to leverage an information splitting technique and dedicate matrix transform to significantly reduce computations. Together with a fill-in reducing multi-frontal sparse direct solvers, this approach improves performance of the computation process.

关键词fill-in reducing algorithm Fisher-scoring algorithm linear mixed model multi-frontal factorization restricted log-likelihood unbalanced data
DOI10.1109/ICBDA.2017.8078871
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语种英语English
Scopus入藏号2-s2.0-85040000546
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文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11511
专题个人在本单位外知识产出
通讯作者Zhu, Shengxin
作者单位
Department of Mathematics,Xi'An Jiaotong-Liverpool University,Laboratory of Computational Physics,Institute of Applied Physics and Computational Mathematics,Beijing,PO. Box 8009,100088,China
推荐引用方式
GB/T 7714
Zhu, Shengxin. Fast calculation of restricted maximum likelihood methods for unstructured high-throughput data[C], 2017: 40-43.
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