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Shrinkage and model selection with correlated variables via weighted fusion

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  • Daye, Z. John
  • Jeng, X. Jessie

Abstract

In this paper, we propose the weighted fusion, a new penalized regression and variable selection method for data with correlated variables. The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection. Weighted fusion is also useful when the number of predictors p is larger than the number of observations n. It allows the selection of more than n variables in a motivated way. Real data and simulation examples show that weighted fusion can improve variable selection and prediction accuracy.

Suggested Citation

  • Daye, Z. John & Jeng, X. Jessie, 2009. "Shrinkage and model selection with correlated variables via weighted fusion," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1284-1298, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1284-1298
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    Cited by:

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    2. Siwei Xia & Yuehan Yang & Hu Yang, 2022. "Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 255-277, March.
    3. Yang, Yuehan & Xia, Siwei & Yang, Hu, 2023. "Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    4. Yue, Lili & Li, Gaorong & Lian, Heng & Wan, Xiang, 2019. "Regression adjustment for treatment effect with multicollinearity in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 17-35.
    5. Oliver J. Rutz & Michael Trusov & Randolph E. Bucklin, 2011. "Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?," Marketing Science, INFORMS, vol. 30(4), pages 646-665, July.
    6. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
    7. Marion, Rebecca & Lederer, Johannes & Govaerts, Bernadette & von Sachs, Rainer, 2021. "VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering," LIDAM Discussion Papers ISBA 2021040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Philip Kostov & Thankom Arun & Samuel Annim, 2014. "Financial Services to the Unbanked: the case of the Mzansi intervention in South Africa," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 8(2), June.

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