Models with varying structure

TL;DR

This paper introduces a new classification method for models with time-varying structures, analyzing its theoretical properties and demonstrating its effectiveness through empirical evaluation.

Contribution

A novel classification approach for models with changing parameters, with proven asymptotic optimality and applicability to various multivariate models.

Findings

The proposed method is asymptotically optimal.

The method effectively classifies observations from models with switching mechanisms.

Empirical results support theoretical properties.

Abstract

In this paper the problems of the retrospective analysis of models with time-varying structure are considered. These models include contamination models with randomly switching parameters and multivariate classification models with an arbitrary number of classes. Our main task here is to classify observations with different stochastic generation mechanisms. A new classification method is proposed. We analyze its properties both theoretically and empirically. The asymptotic optimality of the propodsed method (by the order of convergence to zero of the estimation error) is also established. At the end of the paper we consider multivariate change-in-mean models and multivariate regression models.