Work

Multiscale change point detection for dependent data

Year: 2020

Type: article

Abstract: In this article we study the theoretical properties of the simultaneous multiscale change point estimator (SMUCE) in piecewise‐constant signal models with dependent error processes. Empirical studies suggest that in this case the change point estimate is inconsistent, but it is not known if alternatives suggested in the literature for correlated data are consistent. We propose a modification of SMUCE scaling the basic statistic by the long run variance of the error process, which is estimated by a difference‐type variance estimator calculated from local means from different blocks. For this modification we prove model consistency for physical‐dependent error processes and illustrate the finite sample performance by means of a simulation study.

Source: Scandinavian Journal of Statistics

DOI: 10.1111/sjos.12465

Authors: Holger Dette, Theresa Eckle, Mathias Vetter

Institutions: Ruhr University Bochum, Christian-Albrechts-Universität zu Kiel

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,500

Funder: Deutsche Forschungsgemeinschaft