6 papers
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 (LRTs) in settings where some of the nuisance parameters are unidentifiable under the null hypothesis, parameters…
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…
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…