Parallel computing in linear mixed models

Yavuz F., Schloerke B.

COMPUTATIONAL STATISTICS, cilt.35, sa.3, ss.1273-1289, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00180-019-00950-7
  • Dergi Adı: COMPUTATIONAL STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1273-1289
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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 packagelmmpar.