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Relating and Comparing Methods for Detecting Changes in Mean

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Relating and Comparing Methods for Detecting Changes in Mean. / Fearnhead, Paul; Rigaill, Guillem.
In: Stat, Vol. 9, No. 1, e291, 01.12.2020.

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Fearnhead P, Rigaill G. Relating and Comparing Methods for Detecting Changes in Mean. Stat. 2020 Dec 1;9(1):e291. Epub 2020 Apr 20. doi: 10.1002/sta4.291

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Fearnhead, Paul ; Rigaill, Guillem. / Relating and Comparing Methods for Detecting Changes in Mean. In: Stat. 2020 ; Vol. 9, No. 1.

Bibtex

@article{af3f91bcb92b4abead6228f4b70ac540,
title = "Relating and Comparing Methods for Detecting Changes in Mean",
abstract = "In recent years there have been a large number of proposed approaches to detecting changes in mean. A natural question for an analyst is which method is most appropriate for their application. Answering this question is difficult because current empirical studies often give conflicting conclusions. This paper aims to show the similarities and differences between different changepoint methods. We highlight that there are two aspects to estimating changepoints: estimating the number of changes and estimating their location, and that comparisons should separately evaluate these two aspects. We perform an extensive comparison of different methods across a range of simulation scenarios, and provide code and full results for an interested practitioner to extend this comparison to more methods or different scenarios.",
author = "Paul Fearnhead and Guillem Rigaill",
year = "2020",
month = dec,
day = "1",
doi = "10.1002/sta4.291",
language = "English",
volume = "9",
journal = "Stat",
issn = "2049-1573",
publisher = "Wiley-Blackwell Publishing Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Relating and Comparing Methods for Detecting Changes in Mean

AU - Fearnhead, Paul

AU - Rigaill, Guillem

PY - 2020/12/1

Y1 - 2020/12/1

N2 - In recent years there have been a large number of proposed approaches to detecting changes in mean. A natural question for an analyst is which method is most appropriate for their application. Answering this question is difficult because current empirical studies often give conflicting conclusions. This paper aims to show the similarities and differences between different changepoint methods. We highlight that there are two aspects to estimating changepoints: estimating the number of changes and estimating their location, and that comparisons should separately evaluate these two aspects. We perform an extensive comparison of different methods across a range of simulation scenarios, and provide code and full results for an interested practitioner to extend this comparison to more methods or different scenarios.

AB - In recent years there have been a large number of proposed approaches to detecting changes in mean. A natural question for an analyst is which method is most appropriate for their application. Answering this question is difficult because current empirical studies often give conflicting conclusions. This paper aims to show the similarities and differences between different changepoint methods. We highlight that there are two aspects to estimating changepoints: estimating the number of changes and estimating their location, and that comparisons should separately evaluate these two aspects. We perform an extensive comparison of different methods across a range of simulation scenarios, and provide code and full results for an interested practitioner to extend this comparison to more methods or different scenarios.

U2 - 10.1002/sta4.291

DO - 10.1002/sta4.291

M3 - Journal article

VL - 9

JO - Stat

JF - Stat

SN - 2049-1573

IS - 1

M1 - e291

ER -