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政大典藏 > College of Commerce > Department of International Business > Periodical Articles > Item 140.119/142865

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142865

Title:	An attention algorithm for solving large scale structured L0-norm penalty estimation problems
Authors:	顏佑銘 Yen, Yu-Min Yen, Tso-Jung
Contributors:	國貿系
Keywords:	Blockwise coordinate descent algorithms;Model selection;Nonconvex optimization;Proximal operators;Randomized algorithms
Date:	2021-01
Issue Date:	2022-12-27 10:57:04 (UTC+8)
Abstract:	Technology advances have enabled researchers to collect large amounts of data with lots of covariates. Because of the high volume (large n) and high variety (large p) properties, model estimation with such big data has posed great challenges for statisticians. In this paper, we focus on the algorithmic aspect of these challenges. We propose a numerical procedure for solving large scale regression estimation problems involving a structured l0-norm penalty function. This numerical procedure blends the ideas of randomization, blockwise coordinate descent algorithms, and a closed-form representation of the proximal operator of the structured l0-norm penalty function. In particular, it adopts an “attention” mechanism that exploits the iteration errors to build a sampling distribution for picking up regression coefficients for updates. Simulation study shows the proposed numerical procedure is competitive when comparing with other algorithms for sparse estimation in terms of runtime and statistical accuracy when both the sample size and the number of covariates become large.
Relation:	Japanese Journal of Statistics and Data Science, Vol.4, pp.345-371
Data Type:	article
DOI link:	https://doi.org/10.1007/s42081-020-00101-z
DOI:	10.1007/s42081-020-00101-z
Appears in Collections:	[Department of International Business] Periodical Articles

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