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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Gaussian mixtures are commonly used for modeling heavy-tailed errordistributions in robust linear regression. Combining the likelihood of amultivariate robust linear regression model with a standard improper priordistribution yields an analytically intractable posterior distribution that canbe sampled using a data augmentation algorithm. When the response matrix hasmissing entries, there are unique challenges to the application and analysis ofthe convergence properties of the algorithm. Conditions for geometricergodicity are provided when the incomplete data have a "monotone" structure.In the absence of a monotone structure, an intermediate imputation step isnecessary for implementing the algorithm. In this case, we provide sufficientconditions for the algorithm to be Harris ergodic. Finally, we show that, whenthere is a monotone structure and intermediate imputation is unnecessary,intermediate imputation slows the convergence of the underlying Monte CarloMarkov chain, while post hoc imputation does not. An R package for the dataaugmentation algorithm is provided.

Convergence Analysis of Data Augmentation Algorithms for Bayesian Robust Multivariate Linear Regression with Incomplete Data