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Jump-preserving monitoring of dependent time series using pilot estimators

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  • Steland, Ansgar

Abstract

An important problem of the statistical analysis of time series is to detect change-points in the mean structure. Since this problem is a one-dimensional version of the higher dimensional problem of detecting edges in images, we study detection rules which benefit from results obtained in image processing. For the sigma-filter studied there to detect edges, asymptotic bounds for the normed delay have been established for independent data. These results are considerably extended in two directions. First, we allow for dependent processes satisfying a certain conditional mixing property. Second, we allow for more general pilot estimators, e.g., the median, resulting in better detection properties. A simulation study indicates that our new procedure indeed performs much more better.

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  • Steland, Ansgar, 2003. "Jump-preserving monitoring of dependent time series using pilot estimators," Technical Reports 2004,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200403
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    References listed on IDEAS

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    1. Pawlak, Mirek & Rafajlowicz, Ewaryst & Steland, Ansgar, 2003. "On detecting jumps in time series: Nonparametric setting," Technical Reports 2003,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Basrak, Bojan & Davis, Richard A. & Mikosch, Thomas, 2002. "Regular variation of GARCH processes," Stochastic Processes and their Applications, Elsevier, vol. 99(1), pages 95-115, May.
    3. Andrews, Donald W. K. & Lee, Inpyo & Ploberger, Werner, 1996. "Optimal changepoint tests for normal linear regression," Journal of Econometrics, Elsevier, vol. 70(1), pages 9-38, January.
    4. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    5. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    6. Spokoiny, Vladimir G., 1998. "Estimation of a function with discontinuities via local polynomial fit with an adaptive window choice," SFB 373 Discussion Papers 1998,1, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Ferger Dietmar, 1996. "On The Asymptotic Behavior Of Change-Point Estimators In Case Of No Change With Applications To Testing," Statistics & Risk Modeling, De Gruyter, vol. 14(2), pages 137-144, February.
    9. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    10. Steland, Ansgar, 2003. "Sequential control of time series by functionals of kernel-weighted empirical processes under local alternatives," Technical Reports 2003,19, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Jaehee H. Kim & Jeffrey D. Hart, 1998. "Tests for Change in a Mean Function when the Data are Dependent," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(4), pages 399-424, July.
    12. Ferger, D., 1994. "Nonparametric detection of changepoints for sequentially observed data," Stochastic Processes and their Applications, Elsevier, vol. 51(2), pages 359-372, July.
    13. Yanhong Wu, 1996. "A less sensitive linear detector for the change point based on kernel smoothing method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 43(1), pages 43-55, December.
    14. Carrasco, Marine & Chen, Xiaohong, 2002. "Mixing And Moment Properties Of Various Garch And Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 18(1), pages 17-39, February.
    15. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
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