Version 1
: Received: 6 April 2017 / Approved: 7 April 2017 / Online: 7 April 2017 (12:21:27 CEST)
How to cite:
Zhu, S. Approximate Information and Accelerating for High-throughput Heterogeneous Data Analysis with Linear Mixed Models. Preprints2017, 2017040044. https://doi.org/10.20944/preprints201704.0044.v1
Zhu, S. Approximate Information and Accelerating for High-throughput Heterogeneous Data Analysis with Linear Mixed Models. Preprints 2017, 2017040044. https://doi.org/10.20944/preprints201704.0044.v1
Zhu, S. Approximate Information and Accelerating for High-throughput Heterogeneous Data Analysis with Linear Mixed Models. Preprints2017, 2017040044. https://doi.org/10.20944/preprints201704.0044.v1
APA Style
Zhu, S. (2017). Approximate Information and Accelerating for High-throughput Heterogeneous Data Analysis with Linear Mixed Models. Preprints. https://doi.org/10.20944/preprints201704.0044.v1
Chicago/Turabian Style
Zhu, S. 2017 "Approximate Information and Accelerating for High-throughput Heterogeneous Data Analysis with Linear Mixed Models" Preprints. https://doi.org/10.20944/preprints201704.0044.v1
Abstract
Linear mixed models are frequently used for analysing heterogeneous data 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. An efficient statistical estimate of the underlying model parameters and quantifying the accuracy of the estimation requires the first derivatives and the second derivatives of the restricted log-likelihood function, i.e., the observed information. Standard approaches to compute the observed information and its expectation, the Fisher information, is computationally prohibitive for linear mixed models with thousands random and fixed effects. Customized algorithms are of highly demand to keep mixed models analysis scalable for increasing high-throughput heterogeneous data sets. In this paper, we explore how to leverage an averaged information splitting technique and dedicate matrix transform to significantly reduce computations and to accelerate computing. Together with a fill-in reducing multi-frontal sparse direct solver, the averaged information splitting approach improves the performance of the computation process.
Keywords
observed information; fisher information; averaged information splitting; approximate information
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The commenter has declared there is no conflict of interests.