Noirrit K. Chandra | Searcharxiv

arXiv2020

Asymptotic Theory of Dependent Bayesian Multiple Testing Procedures Under Possible Model Misspecification

We study asymptotic properties of Bayesian multiple testing procedures and provide sufficient conditions for strong consistency under general dependence structure. We also consider a novel Bayesian multiple testing procedure and associated error measures that coherently accounts for the dependence structure present in the model. We advocate posterior versions of FDR and FNR as appropriate error rates and show that their asymptotic convergence rates are directly associated with the Kullback-Leibler divergence from the true model. Our results hold even when the class of postulated models is misspecified. We illustrate our results in a variable selection problem with autoregressive response variables, and compare the new Bayesian procedure with some existing methods through extensive simulation studies in the variable selection problem. Superior performance of the new procedure compared to the others vindicate that proper exploitation of the dependence structure by multiple testing methods is indeed important. Moreover, we obtain encouraging results in a real, maize data context, where we select influential marker variables.

math.ST↗

arXiv2018

Non-marginal Decisions: A Novel Bayesian Multiple Testing Procedure

In this paper we consider the problem of multiple testing when the hypotheses are dependent. In most of the existing literature, either Bayesian or non-Bayesian, the decision rules mainly focus on the validity of the test procedure rather than actually utilizing the dependency to increase efficiency. Moreover, the decisions regarding different hypotheses are marginal in the sense that they do not depend upon each other directly. However, in realistic situations, the hypotheses are usually dependent, and hence it is desirable that the decisions regarding the dependent hypotheses are taken jointly. In this article we develop a novel Bayesian multiple testing procedure that coherently takes this requirement into consideration. Our method, which is based on new notions of error and non-error terms, substantially enhances efficiency by judicious exploitation of the dependence structure among the hypotheses. We prove that our method minimizes the posterior expected loss associated with a an additive "0-1" loss function, we also prove theoretical results on the relevant error probabilities, establishing the coherence and usefulness of our method. The optimal decision configuration is not available in closed form and we propose a novel and efficient simulated annealing algorithm for the purpose of optimization, which is also generically applicable to binary optimization problems. Numerical studies demonstrate that in dependent situations, our method performs significantly better than some existing popular conventional multiple testing methods, in terms of accuracy and power control. Moreover, application of our ideas to a real, spatial data set associated with radionuclide concentration in Rongelap islands yielded insightful results.

stat.ME↗