On Asymptotically Distribution Free Tests with Parametric Hypothesis for Ergodic Diffusion Processes

TL;DR

This paper develops a method for constructing asymptotically distribution free tests for ergodic diffusion processes using Cramer-von Mises type statistics based on local time estimators and maximum likelihood estimators.

Contribution

It introduces a linear transformation that ensures the convergence of the test statistic to a Wiener process integral, making the test asymptotically distribution free.

Findings

The proposed test statistic converges to the integral of a Wiener process.

The method allows for distribution-free hypothesis testing in ergodic diffusion processes.

The approach is based on local time estimators and maximum likelihood estimation.

Abstract

We consider the problem of the construction of the asymptotically distribution free test by the observations of ergodic diffusion process. It is supposedd that under the basic hypothesis the trend coefficient depends on the finite dimensional parameter and we study the Cramer-von Mises type statistics. The underlying statistics depends on the deviation of the local time estimator from the invariant density with parameter replaced by the maximum likelihood estimator. We propose a linear transformation which yields the convergence of the test statistics to the integral of Wiener process. Therefore the test based on this statistics is asymptotically distribution free.