English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this article, we investigate the asymptotic properties of Bayesianmultiple testing procedures under general dependent setup, when the sample sizeand the number of hypotheses both tend to infinity. Specifically, weinvestigate strong consistency of the procedures and asymptotic properties ofdifferent versions of false discovery and false non-discovery rates under thehigh dimensional setup. We particularly focus on a novel Bayesian non-marginalmultiple testing procedure and its associated error rates in this regard. Ourresults show that the asymptotic convergence rates of the error rates aredirectly associated with the Kullback-Leibler divergence from the true model,and the results hold even when the postulated class of models is misspecified.For illustration of our high-dimensional asymptotic theory, we consider aBayesian variable selection problem in a time-varying covariate selectionframework, with autoregressive response variables. We particularly focus on thesetup where the number of hypotheses increases at a faster rate compared to thesample size, which is the so-called ultra-high dimensional situation.

High-dimensional Asymptotic Theory of Bayesian Multiple Testing Procedures Under General Dependent Setup and Possible Misspecification