We consider a multidimensional Itô process Y = (Yt)t∈[0,T ] with some unknown drift coefficient process bt and volatility coefficient σ(Xt, θ) with covariate process X = (Xt)t∈[0,T ], the function σ(x, θ) being known up to θ ∈ Θ. For this model we consider a change point problem for the parameter θ in the volatility component. The change is supposed to occur at some point t∗ ∈ (0, T ). Given di...

The usual mean change-point detecting method based on normal linear regression is not robust to heavy-tailed data with potential outlying points. We propose a estimation procedure quantile model asymmetric Laplace error distribution and develop non-iterative sampling algorithm from Bayesian perspective. can generate independently identically distributed samples approximately the posterior of po...

The contribution deals with use of overcomplete models and sparse parameter estimation for change point detection in one–dimensional stochastic processes. These processes are estimated by ’Heaviside’ functions. The BASIS PURSUIT algorithm is used to get sparse parameter estimation. The mentioned method of change point detection in stochastic processes is compared with standard methods by simula...

This paper presents an approach for online parameter estimation within particle filters. Current research has mainly been focused towards the estimation of static parameters. However, in scenarios of target maneuverability, it is often necessary to update the parameters of the model to meet the changing conditions of the target. The novel aspect of the proposed approach lies in the estimation o...

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and eff...