Electronic Journal of Statistics

Multiple changepoint detection with partial information on changepoint times

Yingbo Li, Robert Lund, and Anuradha Hewaarachchi

Full-text: Open access

Abstract

This paper proposes a new minimum description length procedure to detect multiple changepoints in time series data when some times are a priori thought more likely to be changepoints. This scenario arises with temperature time series homogenization pursuits, our focus here. Our Bayesian procedure constructs a natural prior distribution for the situation, and is shown to estimate the changepoint locations consistently, with an optimal convergence rate. Our methods substantially improve changepoint detection power when prior information is available. The methods are also tailored to bivariate data, allowing changes to occur in one or both component series.

Article information

Source
Electron. J. Statist., Volume 13, Number 2 (2019), 2462-2520.

Dates
Received: November 2017
First available in Project Euclid: 25 July 2019

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1564041629

Digital Object Identifier
doi:10.1214/19-EJS1568

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62C12: Empirical decision procedures; empirical Bayes procedures 62F10: Point estimation

Keywords
Breakpoints empirical Bayes segmentation structural breaks time series vector autoregressions

Rights
Creative Commons Attribution 4.0 International License.

Citation

Li, Yingbo; Lund, Robert; Hewaarachchi, Anuradha. Multiple changepoint detection with partial information on changepoint times. Electron. J. Statist. 13 (2019), no. 2, 2462--2520. doi:10.1214/19-EJS1568. https://projecteuclid.org/euclid.ejs/1564041629


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