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Testing for multiple change points

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  • Jaromír Antoch
  • Daniela Jarušková

Abstract

In this paper we concentrate on testing for multiple changes in the mean of a series of independent random variables. Suggested method applies a maximum type test statistic. Our primary focus is on an effective calculation of critical values for very large sample sizes comprising (tens of) thousands of observations and a moderate to large number of segments. To that end, Monte Carlo simulations and a modified Bellman’s principle of optimality are used. It is shown that, indisputably, computer memory becomes a critical bottleneck in solving a problem of such a size. Thus, minimization of the memory requirements and appropriate order of calculations appear to be the keys to success. In addition, the formula that can be used to get approximate asymptotic critical values using the theory of exceedance probability of Gaussian fields over a high level is presented. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Jaromír Antoch & Daniela Jarušková, 2013. "Testing for multiple change points," Computational Statistics, Springer, vol. 28(5), pages 2161-2183, October.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:5:p:2161-2183
    DOI: 10.1007/s00180-013-0401-1
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    References listed on IDEAS

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    1. Jarusková, Daniela & Piterbarg, Vladimir I., 2011. "Log-likelihood ratio test for detecting transient change," Statistics & Probability Letters, Elsevier, vol. 81(5), pages 552-559, May.
    2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    3. Yao, Yi-Ching, 1988. "Estimating the number of change-points via Schwarz' criterion," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 181-189, February.
    4. Nancy R. Zhang & David O. Siegmund, 2007. "A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data," Biometrics, The International Biometric Society, vol. 63(1), pages 22-32, March.
    5. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, September.
    6. Hawkins, Douglas M., 2001. "Fitting multiple change-point models to data," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 323-341, September.
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    Cited by:

    1. Marie Hušková & Zuzana Prášková, 2014. "Comments on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 265-269, June.
    2. Daniela Jarušková, 2015. "Detecting non-simultaneous changes in means of vectors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 681-700, December.
    3. Daniela Jarušková, 2018. "Estimating non-simultaneous changes in the mean of vectors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 721-743, August.
    4. Liu, Peng & Ji, Lanpeng, 2017. "Extremes of locally stationary chi-square processes with trend," Stochastic Processes and their Applications, Elsevier, vol. 127(2), pages 497-525.
    5. Jaromír Antoch & Jan Hanousek & Marie Hušková & Jiří Trešl, 2019. "Detekce změn v panelových datech: Změna parametrů Fama-French modelu u vybraných evropských akcií v období finanční krize [Detection of Changes in Panel Data: Change in Fama-French Model Parameters," Politická ekonomie, Prague University of Economics and Business, vol. 2019(1), pages 3-19.

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