Parallel computing in linear mixed models

Date

2020-09-01

Author

Gökalp Yavuz, Fulya

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In this study, we propose a parallel programming method for linear mixed models (LMM) generated from big data. A commonly used algorithm, expectation maximization (EM), is preferred for its use of maximum likelihood estimations, as the estimations are stable and simple. However, EM has a high computation cost. In our proposed method, we use a divide and recombine to split the data into smaller subsets, running the algorithm steps in parallel on multiple local cores and combining the results. The proposed method is used to fit LMM with dense and sparse parameters and for large number of observations. It is faster than the classical approach and generalizes for big data. Supplementary sources for the proposed method are available in the R package lmmpar.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and probability, Computational mathematics, Big data, Divide and recombine, EM, Linear mixed models, R package

URI

https://hdl.handle.net/11511/37629

Journal

COMPUTATIONAL STATISTICS

DOI

https://doi.org/10.1007/s00180-019-00950-7

Collections

Department of Statistics, Article

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Citation Formats

F. Gökalp Yavuz, “Parallel computing in linear mixed models,” COMPUTATIONAL STATISTICS, pp. 1273–1289, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37629.