Electronic Journal of Statistics

Bayes multiple decision functions

Wensong Wu and Edsel A. Peña

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Abstract

This paper deals with the problem of simultaneously making many ($M$) binary decisions based on one realization of a random data matrix $\mathbf{X}$. $M$ is typically large and $\mathbf{X}$ will usually have $M$ rows associated with each of the $M$ decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix $\mathbf{X}$ are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting through simulation studies. The procedure is also applied to a subset of a microarray data set from a colon cancer study.

Article information

Source
Electron. J. Statist. Volume 7 (2013), 1272-1300.

Dates
First available in Project Euclid: 3 May 2013

Permanent link to this document
http://projecteuclid.org/euclid.ejs/1367585004

Digital Object Identifier
doi:10.1214/13-EJS813

Mathematical Reviews number (MathSciNet)
MR3063608

Zentralblatt MATH identifier
1336.62040

Keywords
Archimedean copula Bayesian framework decision theoretic framework false discovery rate frailty multiple testing sequential Monte Carlo

Citation

Wu, Wensong; Peña, Edsel A. Bayes multiple decision functions. Electron. J. Statist. 7 (2013), 1272--1300. doi:10.1214/13-EJS813. http://projecteuclid.org/euclid.ejs/1367585004.


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