A pairwise likelihood-based approach for changepoint detection in multivariate time series models

Fung, Ting; Yau, Chun Yip

doi:10.1093/biomet/asw002

Cited by 19 publications

(13 citation statements)

References 28 publications

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“…There has been a recent explosion in methods for detecting changes (e.g. Frick et al, 2014;Fryzlewicz, 2014;Cao and Wu, 2015;Haynes et al, 2017b;Ma and Yau, 2016, and references therein) in recent years, in part motivated by the range of applications for which changepoint detection is important.…”

Section: Introductionmentioning

confidence: 99%

Changepoint Detection in the Presence of Outliers

Fearnhead

Rigaill

2018

Journal of the American Statistical Association

142

138

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Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in order to fit the outliers. To overcome this problem, data often needs to be pre-processed to remove outliers, though this is difficult for applications where the data needs to be analysed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalised cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitable -as we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalised cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analysed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analysing well-log data, detecting copy number variation, and detecting tampering of wireless devices.

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Section: Introductionmentioning

confidence: 99%

Changepoint Detection in the Presence of Outliers

Fearnhead

Rigaill

2018

Journal of the American Statistical Association

142

138

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“…On the other hand, including distally located pairs may even lead to a loss in statistical efficiency in some cases 26,27 . Following the commonly used strategy for conducting composite likelihood 13,24,28,29 analysis, we suggest choosing r to be small for computational gains, followed by adequate sensitivity analysis of the MCLE using various choices of r , whenever necessary. Moreover, with the dependence captured by the model, any reasonable choice of r would not lead to offset the geostatistical model, which overcomes the computational complexity substantially while modeling the latent process.…”

Section: Model Fittingmentioning

confidence: 99%

Composite likelihood inference for ordinal periodontal data with replicated spatial patterns

Wang

Fung

Bandyopadhyay

et al. 2021

Statistics in Medicine

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Spatial ordinal data observed separately for multiple subjects are common in biomedical research, yet statistical methodology for such ordinal data analysis is limited. The existing methodology often assumes a single realization of spatial ordinal data without replications, a commonplace in disease mapping studies, and thus are not directly applicable. Motivated by a dataset evaluating periodontal disease (PD) status, we propose a multisubject spatial ordinal model that assumes a geostatistical spatial structure within a regression framework through an elegant latent variable representation. For achieving computational scalability within a classical inferential framework, we develop a maximum composite likelihood method for parameter estimation, and establish the asymptotic properties of the parameter estimates. Another major contribution is the development of model diagnostic measures for our dependent data scenario using generalized surrogate residuals. A simulation study suggests sound finite sample properties of the proposed methods. We also illustrate our proposed methodology via application to the motivating PD dataset. A companion R package clordr is available for easy implementation.

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“…Changepoint detection for multivariate data has received significant attention in recent years, e.g., Cho and Fryzlewicz (2015); Kirch et al (2015); Preuss et al (2015); Ma and Yau (2016). In Davis et al (2006), the automatic MDL is applied to multivariate AR series, where changepoints affect all component series.…”

Section: Introductionmentioning

confidence: 99%

Multiple changepoint detection with partial information on changepoint times

Li¹,

Lund²,

Hewaarachchi³

2019

Electron. J. Statist.

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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.

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A pairwise likelihood-based approach for changepoint detection in multivariate time series models

Cited by 19 publications

References 28 publications

Changepoint Detection in the Presence of Outliers

Changepoint Detection in the Presence of Outliers

Composite likelihood inference for ordinal periodontal data with replicated spatial patterns

Multiple changepoint detection with partial information on changepoint times

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