5 citations · 5 across the 12 of their papers we have counts for
13 papers
Score-based confidence intervals for variance-covariance parameters in linear mixed models
Matias Shedden, Karl Oskar Ekvall
We propose confidence intervals with near-nominal coverage of variance-covariance parameters in linear mixed models, even when interest or nuisance parameters are near the boundary…
Asymptotics for likelihood ratio tests of boundary points with singular information and unidentifiable nuisance parameters
Karl Oskar Ekvall, Ola Hössjer, Matteo Bottai +1
We establish the asymptotic distribution of likelihood ratio tests when nuisance parameters are unidentifiable under the null hypothesis, parameters of interest lie on the boundary…
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…
Fast and reliable confidence intervals for a variance component
Yiqiao Zhang, Karl Oskar Ekvall, Aaron J. Molstad
We show that confidence intervals in a variance component model, with asymptotically correct uniform coverage probability, can be obtained by inverting certain test-statistics base…
Direct covariance matrix estimation with compositional data
Aaron J. Molstad, Karl Oskar Ekvall, Piotr M. Suder
Compositional data arise in many areas of research in the natural and biomedical sciences. One prominent example is in the study of the human gut microbiome, where one can measure…
Concave likelihood-based regression with finite-support response variables
Karl Oskar Ekvall, Matteo Bottai
We propose a unified framework for likelihood-based regression modeling when the response variable has finite support. Our work is motivated by the fact that, in practice, observed…
Confidence Regions Near Singular Information and Boundary Points With Applications to Mixed Models
Karl Oskar Ekvall, Matteo Bottai
We propose confidence regions with asymptotically correct uniform coverage probability of parameters whose Fisher information matrix can be singular at important points of the para…
Mixed-type multivariate response regression with covariance estimation
Karl Oskar Ekvall, Aaron J. Molstad
We propose a new method for multivariate response regression and covariance estimation when elements of the response vector are of mixed types, for example some continuous and some…
Targeted Principal Components Regression
Karl Oskar Ekvall
We propose a principal components regression method based on maximizing a joint pseudo-likelihood for responses and predictors. Our method uses both responses and predictors to sel…
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…
Likelihood-Based Inference with Separable Correlation Matrices
Karl Oskar Ekvall
This paper proposes methods for likelihood-based inference in multivariate linear regressions when the correlation matrix of the responses is separable; that is, it has a Kronecker…