19 citations · 21 across the 11 of their papers we have counts for
11 papers
Orthogonalized Design Matrices Speed-ups of Bayesian Semiparametric Regression
Nurul Fitriyani, Matt P. Wand
We explain how important classes of Bayesian semiparametric regression fitting and inference procedures can be sped up, significantly, via the use of orthogonalized design matrices…
Precise Asymptotics for Linear Mixed Models with Crossed Random Effects
Jiming Jiang, Matt P. Wand, Swarnadip Ghosh
We obtain an asymptotic normality result that reveals the precise asymptotic behavior of the maximum likelihood estimators of parameters for a very general class of linear mixed mo…
The Grouped Horseshoe distribution and its statistical properties
Virginia X. He, Matt P. Wand
The Grouped Horseshoe distribution arises from hierarchical structures in the recent Bayesian methodological literature aimed at selection of groups of regression coefficients. We…
Online Semiparametric Regression via Sequential Monte Carlo
Marianne Menictas, Chris J. Oates, Matt P. Wand
We develop and describe online algorithms for performing online semiparametric regression analyses. Earlier work on this topic is in Luts, Broderick & Wand (J. Comput. Graph. Stati…
Bayesian Generalized Additive Model Selection Including a Fast Variational Option
Virginia X. He, Matt P. Wand
We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach al…
A variational inference framework for inverse problems
Luca Maestrini, Robert G. Aykroyd, Matt P. Wand
A framework is presented for fitting inverse problem models via variational Bayes approximations. This methodology guarantees flexibility to statistical model specification for a b…