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
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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 |
DOI | 10.1109/ICBDA.2017.8078871 |
URL | 查看来源 |
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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