Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 9, Number 1 (2015), 1243-1266.
Selecting massive variables using an iterated conditional modes/medians algorithm
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.
Electron. J. Statist., Volume 9, Number 1 (2015), 1243-1266.
Received: September 2014
First available in Project Euclid: 11 June 2015
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62J05: Linear regression
Secondary: 62C12: Empirical decision procedures; empirical Bayes procedures 62F07: Ranking and selection
Pungpapong, Vitara; Zhang, Min; Zhang, Dabao. Selecting massive variables using an iterated conditional modes/medians algorithm. Electron. J. Statist. 9 (2015), no. 1, 1243--1266. doi:10.1214/15-EJS1034. https://projecteuclid.org/euclid.ejs/1433982945