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  • 标题:Robust Group Identification and Variable Selection in Regression
  • 本地全文:下载
  • 作者:Ali Alkenani ; Tahir R. Dikheel
  • 期刊名称:Journal of Probability and Statistics
  • 印刷版ISSN:1687-952X
  • 电子版ISSN:1687-9538
  • 出版年度:2017
  • 卷号:2017
  • DOI:10.1155/2017/2170816
  • 出版社:Hindawi Publishing Corporation
  • 摘要:The elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients are the two issues raised in searching for the true model. Pairwise Absolute Clustering and Sparsity (PACS) achieves both goals. Unfortunately, PACS is sensitive to outliers due to its dependency on the least-squares loss function which is known to be very sensitive to unusual data. In this article, the sensitivity of PACS to outliers has been studied. Robust versions of PACS (RPACS) have been proposed by replacing the least squares and nonrobust weights in PACS with MM-estimation and robust weights depending on robust correlations instead of person correlation, respectively. A simulation study and two real data applications have been used to assess the effectiveness of the proposed methods.
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