<b>mosum</b>: A Package for Moving Sums in Change-Point Analysis

Meier, Alexander; Kirch, Claudia; Cho, Haeran

doi:10.18637/jss.v097.i08

Cited by 31 publications

(28 citation statements)

References 37 publications

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“…In practice, provided that the asymmetric bandwidth is not too unbalanced, its use can improve small sample performance of the MOSUM procedure, see Figure 6 of Meier et al (2021b) for an illustration. Their Theorem 1 extends the asymptotic null distribution of the MOSUM test statistic to the asymmetric case and similarly, we can extend Theorem 2.1 and derive the asymptotic distribution of the corresponding (oracle) change point estimators obtained as in (4), i.e.…”

Section: Asymmetric Bandwidthsmentioning

confidence: 99%

“…For algorithmic descriptions of the above procedures and further information about the R package mosum implementing them, see Meier et al (2021b).…”

Section: B Mosum-based Procedures For Multiple Change Point Estimationmentioning

confidence: 99%

“…In all of the above, the error distributions are restricted to belong to an exponential family, or Gaussian specifically. Meier et al (2021b) outlines the bootstrap construction of confidence intervals around the change points based on the moving sum (MOSUM) procedure proposed in Eichinger and Kirch (2018). In this paper, we provide the validity of the bootstrap procedure theoretically, and show its good performance via numerical experiments.…”

Section: Introductionmentioning

confidence: 96%

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Bootstrap confidence intervals for multiple change points based on moving sum procedures

Cho¹,

Kirch²

2021

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In this paper, we address the problem of quantifying uncertainty about the locations of multiple change points. We first establish the asymptotic distribution of the change point estimators obtained as the local maximisers of moving sum statistics, where the limit distributions differ depending on whether the corresponding size of changes is local, i.e. tends to zero as the sample size increases, or fixed. Then, we propose a bootstrap procedure for confidence interval generation which adapts to the unknown size of changes and guarantees asymptotic validity both for local and fixed changes. Simulation studies show good performance of the proposed bootstrap procedure, and we provide some discussions about how it can be extended to serially dependent errors.

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Section: Asymmetric Bandwidthsmentioning

confidence: 99%

“…For algorithmic descriptions of the above procedures and further information about the R package mosum implementing them, see Meier et al (2021b).…”

Section: B Mosum-based Procedures For Multiple Change Point Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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Bootstrap confidence intervals for multiple change points based on moving sum procedures

Cho¹,

Kirch²

2021

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“…has been investigated by Meier et al [106]. Cho and Kirch [34] propose the following scheme for asymmetric bandwidth selection based on the Fibonacci…”

Section: The Use Of Asymmetric Bandwidthsmentioning

confidence: 99%

Data segmentation algorithms: Univariate mean change and beyond

Cho¹,

Kirch²

2020

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Data segmentation a.k.a. multiple change point analysis has received considerable attention due to its importance in time series analysis and signal processing, with applications in a variety of fields including natural and social sciences, medicine, engineering and finance.The goal of this survey article is twofold: In the first part, we review the existing literature on the canonical data segmentation problem which aims at detecting and localising multiple change points in the mean of univariate time series. We provide an overview of popular methodologies on their computational complexity and theoretical properties. In particular, our theoretical discussion focuses on the separation rate relating to which change points are detectable by a given procedure, and the localisation rate quantifying the precision of corresponding change point estimators, and we distinguish between whether a homogeneous or multiscale viewpoint has been adopted in their derivation. We further highlight that the latter viewpoint provides the most general setting for investigating the optimality of data segmentation algorithms.Arguably, the canonical segmentation problem has been the most popular framework to propose new data segmentation algorithms and study their efficiency in the last decades. In the second part of this survey, we motivate the importance of attaining an in-depth understanding of strengths and weaknesses of methodologies for the change point problem in a simpler, univariate setting, as a stepping stone for the development of methodologies for more complex problems. We illustrate this with a range of examples showcasing the connections between complex distributional changes and those in the mean. We also discuss extensions towards high-dimensional change point problems where we demonstrate that the challenges arising from high dimensionality are orthogonal to those in dealing with multiple change points.

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“…We generate G as detailed in Section 3.5 ofMeier et al (2019b) with G1 = 1 but only use bandwidths ≥ 2 due to the necessity of local variance estimation.…”

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confidence: 99%

Discussion of 'Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection'

Cho

Kirch

2020

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We congratulate the author for this interesting paper (Fryzlewicz, 2020) which introduces a novel method for the data segmentation problem that works well in a classical change point setting as well as in a frequent jump situation. Most notably, the paper introduces a new model selection step based on finding the 'steepest drop to low levels' (SDLL). Since the new model selection requires a complete (or at least relatively deep) solution path ordering the change point candidates according to some measure of importance, a new recursive variant of the Wild Binary Segmentation (Fryzlewicz, 2014, WBS) named WBS2, has been proposed for candidate generation.

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mosum: A Package for Moving Sums in Change-Point Analysis

Cited by 31 publications

References 37 publications

Bootstrap confidence intervals for multiple change points based on moving sum procedures

Bootstrap confidence intervals for multiple change points based on moving sum procedures

Data segmentation algorithms: Univariate mean change and beyond

Discussion of 'Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection'

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