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      Improved two-stage model averaging for high-dimensional linear regression, with application to Riboflavin data analysis

      research-article
      BMC Bioinformatics
      BioMed Central
      High-dimensional regression, Model averaging, Variable selection, Cross-validation, Jackknife

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          Abstract

          Background

          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. In this paper, we develop a two-stage model averaging procedure to enhance accuracy and stability in prediction for high-dimensional linear regression. First we employ a high-dimensional variable selection method such as LASSO to screen redundant predictors and construct a class of candidate models, then we apply the jackknife cross-validation to optimize model weights for averaging.

          Results

          In simulation studies, the proposed technique outperforms commonly used alternative methods under high-dimensional regression setting, in terms of minimizing the mean of the squared prediction error. We apply the proposed method to a riboflavin data, the result show that such method is quite efficient in forecasting the riboflavin production rate, when there are thousands of genes and only tens of subjects.

          Conclusions

          Compared with a recent high-dimensional model averaging procedure (Ando and Li in J Am Stat Assoc 109:254–65, 2014), the proposed approach enjoys three appealing features thus has better predictive performance: (1) More suitable methods are applied for model constructing and weighting. (2) Computational flexibility is retained since each candidate model and its corresponding weight are determined in the low-dimensional setting and the quadratic programming is utilized in the cross-validation. (3) Model selection and averaging are combined in the procedure thus it makes full use of the strengths of both techniques. As a consequence, the proposed method can achieve stable and accurate predictions in high-dimensional linear models, and can greatly help practical researchers analyze genetic data in medical research.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-021-04053-3.

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          Most cited references22

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          Regression Shrinkage and Selection Via the Lasso

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            Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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              The Adaptive Lasso and Its Oracle Properties

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                Author and article information

                Contributors
                pan@rowan.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                25 March 2021
                25 March 2021
                2021
                : 22
                Affiliations
                GRID grid.262671.6, ISNI 0000 0000 8828 4546, Department of Mathematics, , Rowan University, ; Glassboro, NJ 08028 USA
                Article
                4053
                10.1186/s12859-021-04053-3
                7992957
                a9f32193-51de-41b1-a2ab-92edf0904409
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Bioinformatics & Computational biology
                high-dimensional regression,model averaging,variable selection,cross-validation,jackknife

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