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An extended variable inclusion and shrinkage algorithm for correlated variables

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  • Mkhadri, Abdallah
  • Ouhourane, Mohamed

Abstract

The problem of variable selection for linear regression in a high dimension model is considered. A new method, called Extended-VISA (Ext-VISA), is proposed to simultaneously select variables and encourage a grouping effect where strongly correlated predictors tend to be in or out of the model together. Moreover, Ext-VISA is capable of selecting a sparse model while avoiding the overshrinkage of a Lasso-type estimator. It combines the idea of the VISA algorithm which avoids the overshrinkage problem of regression coefficients and those of the Lasso-type estimators, based on ℓ1+ℓ2 penalty, that overcome the limitation of the grouping effect of Lasso in high dimension. It is based on a modified VISA algorithm, so it is also computationally efficient. Three interesting cases of Ext-VISA are examined. The first case is Smooth-VISA (SVISA), where the variations among successive regression coefficients are low. The second case is VISA-Net (VNET), where the correlations between predictors are taken into account. The third case is Laplacian-VISA (LVISA), where the predictors are measured on an undirected graph. A theoretical property on sparsity inequality of Ext-VISA is established. A detailed simulation study in small and high dimensional settings is performed, which illustrates the advantages of the new approach in relation to several other possible methods. Finally, we apply VNET, SVISA and LVISA to a GC-retention data set.

Suggested Citation

  • Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:631-644
    DOI: 10.1016/j.csda.2012.07.023
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    References listed on IDEAS

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    Cited by:

    1. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
    2. JinXing Che & YouLong Yang, 2017. "Stochastic correlation coefficient ensembles for variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1721-1742, July.
    3. Chun-Xia Zhang & Guan-Wei Wang & Jun-Min Liu, 2015. "RandGA: injecting randomness into parallel genetic algorithm for variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 630-647, March.
    4. Korzeń, M. & Jaroszewicz, S. & Klęsk, P., 2013. "Logistic regression with weight grouping priors," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 281-298.
    5. Abdallah Mkhadri & Mohamed Ouhourane, 2015. "A group VISA algorithm for variable selection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 41-60, March.

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