TY - JOUR
T1 - mosum
T2 - A package for moving sums in change point analysis
AU - Meier, Alexander
AU - Kirch, Claudia
AU - Cho, Haeran
N1 - Funding Information:
This research was supported by the research training group Mathematical Complexity Reduction DFG-GRK 2297 and partially supported by DFG grant KI 1443/3-1. Haeran Cho’s work was supported by the Engineering and Physical Sciences Research Council grant no. EP/N024435/1. The authors would like to thank Kerstin Reckrühm and Rebecca Killick for the constructive suggestions for improvement, as well as the referees and the Editor for the helpful comments.
Funding Information:
This research was supported by the research training group Mathematical Complexity ReductionDFG-GRK 2297 and partially supported by DFG grant KI 1443/3-1. Haeran Cho?s work was supported by the Engineering and Physical Sciences Research Council grant no. EP/N024435/1. The authors would like to thank Kerstin Reckruhm and Rebecca Killick for the constructive suggestions for improvement, as well as the referees and the Editor for the helpful comments.
Publisher Copyright:
© 2021, American Statistical Association. All rights reserved.
PY - 2021/3/19
Y1 - 2021/3/19
N2 - Understanding changes plays a crucial role in many fields of science, economy, technology and medicine. Whenever time series data, i.e., temporally ordered data, is described by means of a stationary stochastic model, it is of interest to verify the stationarity assumption on the basis of the data, and change point analysis provides mathematical tools for this purpose. A particularly important, and thus widely studied, problem is to detect changes in the mean at unknown time points. In this paper, we present the R package mosum, which implements elegant and mathematically well-justified procedures to themultiple mean change problem using moving sum (MOSUM) statistics.
AB - Understanding changes plays a crucial role in many fields of science, economy, technology and medicine. Whenever time series data, i.e., temporally ordered data, is described by means of a stationary stochastic model, it is of interest to verify the stationarity assumption on the basis of the data, and change point analysis provides mathematical tools for this purpose. A particularly important, and thus widely studied, problem is to detect changes in the mean at unknown time points. In this paper, we present the R package mosum, which implements elegant and mathematically well-justified procedures to themultiple mean change problem using moving sum (MOSUM) statistics.
KW - MOSUM
KW - change-point analysis
KW - time series
U2 - 10.18637/jss.v097.i08
DO - 10.18637/jss.v097.i08
M3 - Article (Academic Journal)
SN - 1548-7660
VL - 97
SP - 1
EP - 42
JO - Journal of Statistical Software
JF - Journal of Statistical Software
IS - 8
ER -