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 many practices, scientists are particularly interested in detecting whichof the predictors are truly associated with a multivariate response. It is moreaccurate to model multiple responses as one vector rather than separating eachcomponent one by one. This is particularly true for complex traits havingmultiple correlated components. A Bayesian multivariate variable selection(BMVS) approach is proposed to select important predictors influencing themultivariate response from a candidate pool with an ultrahigh dimension. Byapplying the sample-size-dependent spike and slab priors, the BMVS approachsatisfies the strong selection consistency property under certain conditions,which represents the advantages of BMVS over other existing Bayesianmultivariate regression-based approaches. The proposed approach considers thecovariance structure of multiple responses without assuming independence andintegrates the estimation of covariance-related parameters together with allregression parameters into one framework through a fast updating MCMCprocedure. It is demonstrated through simulations that the BMVS approachoutperforms some other relevant frequentist and Bayesian approaches. Theproposed BMVS approach possesses the flexibility of wide applications,including genome-wide association studies with multiple correlated phenotypesand a large scale of genetic variants and/or environmental variables, asdemonstrated in the real data analyses section. The computer code and test dataof the proposed method are available as an R package.

An Approach of Bayesian Variable Selection for Ultrahigh Dimensional Multivariate Regression