Multivariate Laplace's approximation with estimated error and application to limit theorems

TL;DR

This paper develops an approximation for multivariate Laplace integrals with error estimates, applicable to proving limit theorems like the law of large numbers and central limit theorem, with different limiting distributions depending on the maximum's location.

Contribution

It introduces an error-estimated approximation for multivariate Laplace integrals with large parameters, considering interior and boundary maxima, and applies it to derive limit theorems with distinct distributions.

Findings

Approximation with error estimates for multivariate Laplace integrals.

Different limiting distributions depending on maximum location: Normal or mixed distributions.

Application to prove weak law of large numbers and central limit theorem.

Abstract

In this paper we obtain an approximation for the multivariate Laplace's integral with a large parameter and estimate error term for two cases, when the maximum of the exponent is in the interior of the domain and on the boundary. We are specifically interested in the situation when the function in the exponent depends on the large parameter. As an application we prove weak law of large numbers and central limit theorem. The second result gives different limiting distributions for two cases mentioned above. When the maximum of the exponent is in the interior of the domain it is Normal distribution and if it is on the boundary, it is Exponential in one direction of integration and Normal in other directions.