4 papers
Universal inference for variance components
Yiqiao Zhang, Karl Oskar Ekvall, Aaron J. Molstad
We consider universal inference in variance components models, focusing on settings where the parameter is near or at the boundary of the parameter set. Two cases, which are not ha…
Uniform inference in linear mixed models
Karl Oskar Ekvall, Matteo Bottai
We provide finite-sample distribution approximations, that are uniform in the parameter, for inference in linear mixed models. Focus is on variances and covariances of random effec…
Convergence Analysis of a Collapsed Gibbs Sampler for Bayesian Vector Autoregressions
Karl Oskar Ekvall, Galin L. Jones
We study the convergence properties of a collapsed Gibbs sampler for Bayesian vector autoregressions with predictors, or exogenous variables. The Markov chain generated by our algo…
Consistent Maximum Likelihood Estimation Using Subsets with Applications to Multivariate Mixed Models
Karl Oskar Ekvall, Galin L. Jones
We present new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. Our theory builds on the idea of using subsets of the full data t…