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A Review on High-Dimensional Frequentist Model Averaging
School of Health Care Management, Shandong University, Jinan, China
Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, USA
Key Laboratory of Health Economics and Policy Research, NHFPC (Shandong University), Jinan, China
- 1 School of Health Care Management, Shandong University, Jinan, China
- 2 Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, USA
- 3 Key Laboratory of Health Economics and Policy Research, NHFPC (Shandong University), Jinan, China
Open Journal of Statistics·Volume 08 (2018)·Pages 513–518·Published 9 May 2018·DOI10.4236/ojs.2018.83033
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Abstract
Model averaging has attracted increasing attention in recent years for the analysis of high-dimensional data. By weighting several competing statistical models suitably, model averaging attempts to achieve stable and improved prediction. To obtain a better understanding of the available model averaging methods, their properties and the relationships between them, this paper is devoted to make a review on some recent progresses in high-dimensional model averaging from the frequentist perspective. Some future research topics are also discussed.
KeywordsModel AveragingHigh-Dimensional Regression ModelsStable Prediction
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