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2015 Selecting massive variables using an iterated conditional modes/medians algorithm
Vitara Pungpapong, Min Zhang, Dabao Zhang
Electron. J. Statist. 9(1): 1243-1266 (2015). DOI: 10.1214/15-EJS1034


Empirical Bayes methods are designed in selecting massive variables, which may be inter-connected following certain hierarchical structures, because of three attributes: taking prior information on model parameters, allowing data-driven hyperparameter values, and free of tuning parameters. We propose an iterated conditional modes/medians (ICM/M) algorithm to implement empirical Bayes selection of massive variables, while incorporating sparsity or more complicated a priori information. The iterative conditional modes are employed to obtain data-driven estimates of hyperparameters, and the iterative conditional medians are used to estimate the model coefficients and therefore enable the selection of massive variables. The ICM/M algorithm is computationally fast, and can easily extend the empirical Bayes thresholding, which is adaptive to parameter sparsity, to complex data. Empirical studies suggest competitive performance of the proposed method, even in the simple case of selecting massive regression predictors.


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Vitara Pungpapong. Min Zhang. Dabao Zhang. "Selecting massive variables using an iterated conditional modes/medians algorithm." Electron. J. Statist. 9 (1) 1243 - 1266, 2015.


Received: 1 September 2014; Published: 2015
First available in Project Euclid: 11 June 2015

zbMATH: 1327.62409
MathSciNet: MR3355757
Digital Object Identifier: 10.1214/15-EJS1034

Primary: 62J05
Secondary: 62C12 , 62F07

Keywords: Empirical Bayes variable selection , high dimensional data , prior , Sparsity

Rights: Copyright © 2015 The Institute of Mathematical Statistics and the Bernoulli Society


Vol.9 • No. 1 • 2015
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