國立政治大學統計學系
學 術 演 講
主講人：王昱博助理教授學 術 演 講
School of Mathematical and Statistical Sciences, Clemson University
題 目：Estimation of l0 Norm Penalized Models: A Statistical Treatment
時 間：民國113年5月13日 (星期一) 下午1：30
地 點：國立政治大學逸仙樓050101教室
摘 要：
Fitting penalized models for the purpose of merging the estimation and model selection problem has become common place in statistical practice. Of the various regularization strategies that can be leveraged to this end, the use of the l0 norm to penalize parameter estimation poses the most daunting model fitting task. In fact, this particular strategy requires an end user to solve a non-convex NP-hard optimization problem irregardless of the underlying data model. For this reason, the use of the l0 norm as a regularization strategy has been woefully under utilized. To obviate this difficulty, herein we propose a strategy to solve such problems that is generally accessible by the statistical community. Our approach can be adopted to solve l0 norm penalized problems across a very broad class of models, can be implemented using existing software, and is computationally efficient. We demonstrate the performance of our method through in depth numerical experiments and through using it to analyze several prototypical data sets.
