Accelerating Large-Scale Statistical Computation With the GOEM Algorithm

Xiao Nie, Jared Huling, Peter Z.G. Qian

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Large-scale data analysis problems have become increasingly common across many disciplines. While large volume of data offers more statistical power, it also brings computational challenges. The orthogonalizing expectation–maximization (EM) algorithm by Xiong et al. is an efficient method to deal with large-scale least-square problems from a design point of view. In this article, we propose a reformulation and generalization of the orthogonalizing EM algorithm. Computational complexity and convergence guarantees are established. The reformulation of the orthogonalizing EM algorithm leads to a reduction in computational complexity for least-square problems and penalized least-square problems. The reformulation, named the GOEM (generalized orthogonalizing EM) algorithm, can incorporate a wide variety of convex and nonconvex penalties, including the lasso, group lasso, and minimax concave penalty penalties. The GOEM algorithm is further extended to a wider class of models including generalized linear models and Cox's proportional hazards model. Synthetic and real data examples are included to illustrate its use and efficiency compared with standard techniques. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)416-425
Number of pages10
JournalTechnometrics
Volume59
Issue number4
DOIs
StatePublished - Oct 2 2017
Externally publishedYes

Keywords

  • Big data
  • Computational statistics
  • OEM
  • Optimization
  • Proximal algorithm

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