Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator

Abstract

We obtain explicit p-Wasserstein distance error bounds between the distribution of the multi-parameter MLE and the multivariate normal distribution. Our general bounds are given for possibly high-dimensional, independent and identically distributed random vectors. Our general bounds are of the optimal $\mathcal{O}(n^{-1∕2})$ order. Explicit numerical constants are given when $p\in (1,2]$, and in the case $p>2$ the bounds are explicit up to a constant factor that only depends on p. We apply our general bounds to derive Wasserstein distance error bounds for the multivariate normal approximation of the MLE in several settings; these being single-parameter exponential families, the normal distribution under canonical parametrisation, and the multivariate normal distribution under non-canonical parametrisation. In addition, we provide upper bounds with respect to the bounded Wasserstein distance when the MLE is implicitly defined.