Changepoint detection with the use of the RESPERM method – a Monte Carlo study

Grzegorz Kończak; Katarzyna Stąpor

doi:https://doi.org/10.59170/stattrans-2023-069

Changepoint detection with the use of the RESPERM method – a Monte Carlo study

Grzegorz Kończak Department of Statistics, Econometrics and Mathematics, University of Economics in Katowice, Poland ORCID:https://orcid.org/0000-0002-4696-8215 , Katarzyna Stąpor Department of Applied Informatics, Silesian University of Technology, Poland ORCID:https://orcid.org/0000-0003-3003-6592 Statistics in Transition new series, vol. 24, 2023, 5, pages: 167-184 Published online: 7 December 2023 https://doi.org/10.59170/stattrans-2023-069 Citation: Kończak, G., Stąpor, K., 2023. Changepoint detection with the use of the RESPERM method – a Monte Carlo study. Statistics in Transition new series, 24(5), pp. 167-184. https://doi.org/10.59170/stattrans-2023-069

539 Views 44 Downloads

ARTICLE

(English) PDF

ABSTRACT

RESPERM (residuals permutation-based method) is a single changepoint detection method based on regression residuals permutation, which can be applied to many physiological situations where the regression slope can change suddenly at a given point. This article presents the results of a Monte Carlo study on the properties of the RESPERM method for single changepoint detection in a linear regression model. We compared our method with a well-known segmented method for detection breakpoint in linear models. The Monte Carlo study showed that when the input data are very noisy, the RESPERM method outperforms the segmented approach in terms of variance, and in the case of bias, the results of the two methods are comparable.

KEYWORDS

changepoint detection, RESPERM, permutation methods

REFERENCES

Aminikhanghahi, S., Cook, D. J. (2017). A Survey of Methods for Time Series Changepoint Detection. Knowl Inf Syst., 2017 May, 51(2), pp. 339–367. doi:10.1007/s10115-016-0987-z.

Applied Regression Analysis, (2023). https://online.stat.psu.edu/stat462/node/185/ (24.07.2023).

Bacon, D. W., Watts, D. G., (1971). Estimating the Transition Between Two Intersecting Straight Lines. Biometrika, 58, pp. 525–534.

Chow, G., (1960) Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28, pp. 591–05.

Cohen, J., (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Publishers, New York.

Davidson, A. C., Hinkley, D. V., (1997). Bootstrap Methods and Their Application. Cambridge University Press, New York.

Julious, S. A., (2001) Inference and Estimation in a Changepoint Regression Problem. The Statistician, 50, pp. 51–61.

Lerman, P. M., (1980). Fitting Segmented Regression Models by Grid Search. Applied Statistics, 29, pp. 77–84.

Muggeo, V. M. R., (2003). Estimating Regression Models with Unknown Break-Points. Statistics in Medicine, 22, pp. 3055–3071.

Muggeo, V. M. R., (2008). Segmented: an R package to fit regression models with broken-line relationships. R News, 8/1, pp. 20–25.

Quandt, R. E., (1958). The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes. Journal of American Statistical Association, 53, pp. 873–880.

Quandt, R. E., (1960). Tests of the Hypotheses that a Linear Regression System Obeys Two Separate Regimes. Journal of American Statistical Association 55, p. 324.

Sheykhfard A., Haghighi F., Nordfiaern T., Soltaninejad M., (2020). Structural equation modelling of potential risk factors for pedestrian accidents in rural and urban roads, International Journal of Injury Control and Safety Promotion, 10.1080/17457300.2020.1835991, 28, 1, pp. 46–57.

Sommer, W., Stapor K., Kończak G., Kotowski K., Fabian P., Ochab J., Bereś A., Ślusarczyk G., (2022). Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM). Benchmarking and Application to Single Trial ERPs, Brain Sciences, Vol. 12, iss. 5, nr art. 525.

Truong, Ch., Oudre L., Vayatis N., (2020). Selective Review of Offline Changepoint Detection Methods. Signal Processing, Vol. 167, February 2020, 107299.

Ulm, K., (1991). A Statistical Method for Assessing a Threshold in Epidemiological Studies. Statistics in Medicine, 10, pp. 341–349.