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Orthogonalizing EM: A Design-Based Least Squares Algorithm
Xiong, Shifeng1; Dai, Bin2; Huling, Jared3; Qian, Peter Z. G.3
2016-08-01
Source PublicationTECHNOMETRICS
ISSN0040-1706
Volume58Issue:3Pages:285-293
AbstractWe introduce an efficient iterative algorithm, intended for various least squares problems, based on a design of experiments perspective. The algorithm, called orthogonalizing EM (OEM), works for ordinary least squares (OLS) and can be easily extended to penalized least squares. The main idea of the procedure is to orthogonalize a design matrix by adding new rows and then solve the original problem by embedding the augmented design in a missing data framework. We establish several attractive theoretical properties concerning OEM. For the OLS with a singular regression matrix, an OEM sequence converges to the Moore-Penrose generalized inverse-based least squares estimator. For ordinary and penalized least squares with various penalties, it converges to a point having grouping coherence for fully aliased regression matrices. Convergence and the convergence rate of the algorithm are examined. Finally, we demonstrate that OEM is highly efficient for large-scale least squares and penalized least squares problems, and is considerably faster than competing methods when n is much larger than p. Supplementary materials for this article are available online.
KeywordComputational statistics Design of experiments Missing data Orthogonal design SCAD The Lasso
DOI10.1080/00401706.2015.1054436
Language英语
Funding ProjectNational Natural Science Foundation of China[11271355] ; National Natural Science Foundation of China[11471172]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000381014100002
PublisherAMER STATISTICAL ASSOC
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/23290
Collection系统科学研究所
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Tower Res Capital, 377 Broadway, New York, NY 10013 USA
3.Univ Wisconsin, Dept Stat, Madison, WI 53706 USA
Recommended Citation
GB/T 7714
Xiong, Shifeng,Dai, Bin,Huling, Jared,et al. Orthogonalizing EM: A Design-Based Least Squares Algorithm[J]. TECHNOMETRICS,2016,58(3):285-293.
APA Xiong, Shifeng,Dai, Bin,Huling, Jared,&Qian, Peter Z. G..(2016).Orthogonalizing EM: A Design-Based Least Squares Algorithm.TECHNOMETRICS,58(3),285-293.
MLA Xiong, Shifeng,et al."Orthogonalizing EM: A Design-Based Least Squares Algorithm".TECHNOMETRICS 58.3(2016):285-293.
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