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A novel change point approach for the detection of gas emission sources using remotely contained concentration data

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A novel change point approach for the detection of gas emission sources using remotely contained concentration data. / Eckley, Idris; Kirch, Claudia; Weber, Silke.
In: Annals of Applied Statistics, Vol. 14, No. 3, 01.10.2020, p. 1258-1284.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Eckley, I, Kirch, C & Weber, S 2020, 'A novel change point approach for the detection of gas emission sources using remotely contained concentration data', Annals of Applied Statistics, vol. 14, no. 3, pp. 1258-1284. https://doi.org/10.1214/20-AOAS1345

APA

Eckley, I., Kirch, C., & Weber, S. (2020). A novel change point approach for the detection of gas emission sources using remotely contained concentration data. Annals of Applied Statistics, 14(3), 1258-1284. https://doi.org/10.1214/20-AOAS1345

Vancouver

Eckley I, Kirch C, Weber S. A novel change point approach for the detection of gas emission sources using remotely contained concentration data. Annals of Applied Statistics. 2020 Oct 1;14(3):1258-1284. doi: 10.1214/20-AOAS1345

Author

Eckley, Idris ; Kirch, Claudia ; Weber, Silke. / A novel change point approach for the detection of gas emission sources using remotely contained concentration data. In: Annals of Applied Statistics. 2020 ; Vol. 14, No. 3. pp. 1258-1284.

Bibtex

@article{eef1387d1c5c481f9ff38b62a07cbb38,
title = "A novel change point approach for the detection of gas emission sources using remotely contained concentration data",
abstract = "Motivated by an example from remote sensing of gas emission sources, we derive two novel change point procedures for multivariate time series where, in contrast to classical change point literature, the changes are not required to be aligned in the different components of the time series. Instead the change points are described by a functional relationship where the precise shape depends on unknown parameters of interest such as the source of the gas emission in the above example. Two different types of tests and the corresponding estimators for the unknown parameters describing the change locations are proposed. We derive the null asymptotics for both tests under weak assumptions on the error time series and show asymptotic consistency under alternatives. Furthermore, we prove consistency for the corresponding estimators of the parameters of interest. The small sample behavior of the methodology is assessed by means of a simulation study and the above remote sensing example analyzed in detail.",
keywords = "non-aligned change points, epidemic model, projection methods, dependent errors, multivariate change points",
author = "Idris Eckley and Claudia Kirch and Silke Weber",
year = "2020",
month = oct,
day = "1",
doi = "10.1214/20-AOAS1345",
language = "English",
volume = "14",
pages = "1258--1284",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "3",

}

RIS

TY - JOUR

T1 - A novel change point approach for the detection of gas emission sources using remotely contained concentration data

AU - Eckley, Idris

AU - Kirch, Claudia

AU - Weber, Silke

PY - 2020/10/1

Y1 - 2020/10/1

N2 - Motivated by an example from remote sensing of gas emission sources, we derive two novel change point procedures for multivariate time series where, in contrast to classical change point literature, the changes are not required to be aligned in the different components of the time series. Instead the change points are described by a functional relationship where the precise shape depends on unknown parameters of interest such as the source of the gas emission in the above example. Two different types of tests and the corresponding estimators for the unknown parameters describing the change locations are proposed. We derive the null asymptotics for both tests under weak assumptions on the error time series and show asymptotic consistency under alternatives. Furthermore, we prove consistency for the corresponding estimators of the parameters of interest. The small sample behavior of the methodology is assessed by means of a simulation study and the above remote sensing example analyzed in detail.

AB - Motivated by an example from remote sensing of gas emission sources, we derive two novel change point procedures for multivariate time series where, in contrast to classical change point literature, the changes are not required to be aligned in the different components of the time series. Instead the change points are described by a functional relationship where the precise shape depends on unknown parameters of interest such as the source of the gas emission in the above example. Two different types of tests and the corresponding estimators for the unknown parameters describing the change locations are proposed. We derive the null asymptotics for both tests under weak assumptions on the error time series and show asymptotic consistency under alternatives. Furthermore, we prove consistency for the corresponding estimators of the parameters of interest. The small sample behavior of the methodology is assessed by means of a simulation study and the above remote sensing example analyzed in detail.

KW - non-aligned change points

KW - epidemic model

KW - projection methods

KW - dependent errors

KW - multivariate change points

U2 - 10.1214/20-AOAS1345

DO - 10.1214/20-AOAS1345

M3 - Journal article

VL - 14

SP - 1258

EP - 1284

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

SN - 1932-6157

IS - 3

ER -