# A simple recipe for making accurate parametric inference in finite sample

## TL;DR

This paper proposes a new method for finite-sample parametric inference that offers accurate results without relying solely on asymptotic approximations, addressing a key challenge in statistical testing.

## Contribution

It introduces a theoretical framework with general conditions ensuring accurate finite-sample inference, providing an alternative to traditional asymptotic methods like the bootstrap.

## Key findings

- The method guarantees finite-sample accuracy under specified conditions.
- Theoretical demonstration of the method's validity in finite samples.
- Provides a practical approach for exact inference in finite-sample scenarios.

## Abstract

Constructing tests or confidence regions that control over the error rates in the long-run is probably one of the most important problem in statistics. Yet, the theoretical justification for most methods in statistics is asymptotic. The bootstrap for example, despite its simplicity and its widespread usage, is an asymptotic method. There are in general no claim about the exactness of inferential procedures in finite sample. In this paper, we propose an alternative to the parametric bootstrap. We setup general conditions to demonstrate theoretically that accurate inference can be claimed in finite sample.