On Compound Poisson Processes Arising in Change-Point Type Statistical Models as Limiting Likelihood Ratios

Different change-point type models encountered in statistical inference for stochastic processes give rise to different limiting likelihood ratio processes. In a previous paper of one of the authors it was established that one of these likelihood ratios, which is an exponential functional of a two-sided Poisson process driven by some parameter, can be approximated (for sufficiently small values of the parameter) by another one, which is an exponential functional of a two-sided Brownian motion. In this paper we consider yet another likelihood ratio, which is the exponent of a two-sided compound Poisson process driven by some parameter. We establish, that similarly to the Poisson type one, the compound Poisson type likelihood ratio can be approximated by the Brownian type one for sufficiently small values of the parameter. We equally discuss the asymptotics for large values of the parameter and illustrate the results by numerical simulations

Exact asymptotic bias for estimators of the Ornstein–Uhlenbeck process

Ornstein–Uhlenbeck process, Asymptotic bias, Asymptotic efficiency, Maximum likelihood, Conditional maximum likelihood, Empirical estimator, Bias derivative, 62F10, 62F12, 62M,

The normal approximation rate for the drift estimator of multidimensional diffusions

Multidimensional diffusion process, Nonparametric estimation, Drift estimation, Uniform central limit theorem,