Modified likelihood root in high dimensions

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2020 · Volume 82 · Issue 5

Abstract

We examine a higher order approximation to the significance function with increasing numbers of nuisance parameters, based on the normal approximation to an adjusted log-likelihood root. We show that the rate of the correction for nuisance parameters is larger than the correction for non-normality, when the parameter dimensionpisO(nα) forα<12. We specialize the results to linear exponential families and location–scale families and illustrate these with simulations.

Keywords

High dimensional data, Higher order asymptotics, Modified likelihood root, Nuisance parameter, Statistical inference

Enable JavaScript for citation tools and additional paper interactions.