Direct use of the likelihood function typically produces severely biased estimates when the
dimension of the parameter vector is large relative to the effective sample size. With linearly …

We provide the first stochastic convergence rates for a family of adaptive quadrature rules
used to normalize the posterior distribution in Bayesian models. Our results apply to the …

We examine the behaviour of the Laplace and saddlepoint approximations in the high-
dimensional setting, where the dimension of the model is allowed to increase with the …

Directional testing for high dimensional multivariate normal distributions Page 1 Electronic
Journal of Statistics Vol. 16 (2022) 6489–6511 ISSN: 1935-7524 https://doi.org/10.1214/22-EJS2089 …

With very large amounts of data, important aspects of statistical analysis may appear largely
descriptive in that the role of probability sometimes seems limited or totally absent. The main …

We first consider the sequential detection of transient signals by generalizing the moving
average chart to exponential family and study the false detection probability (FDP) and …

Testing the equality of mean vectors across $ g $ different groups plays an important role in
many scientific fields. In regular frameworks, likelihood-based statistics under the normality …

We examine the normal approximation of the modified likelihood root, an inferential tool from
higher-order asymptotic theory, for the linear exponential and location-scale family. We …

We examine several classical inferential methods in the higher-order asymptotics literature
and derive guarantees for their behaviour in high dimensions. In particular, we consider the …

We first consider the sequential detection of transient signals by generalizing the moving
average chart to exponential family and study the false detection probability (FDP) and …