Skip to main content
Log in

Test for parameter change based on the estimator minimizing density-based divergence measures

  • Change Point
  • Published:
Annals of the Institute of Statistical Mathematics Aims and scope Submit manuscript

Abstract

In this paper we consider the problem of testing for a parameter change based on the cusum test proposed by Leeet al. (2003,Scandinavian Journal of Statistics,30, 781–796). The cusum test statistic is constructed via employing the estimator minimizing density-based divergence measures. It is shown that under regularity conditions, the test statistic has the limiting distribution of the sup of standard Brownian bridge. Simulation results demonstrate that the cusum test is robust when outliers exist.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Basu, A. and Lindsay, B. G. (1994). Minimum disparity estimation for continous models: Efficiency, distributions and robustness,Annals of the Institute of Statistical Mathematics,46, 683–705.

    Article  MATH  MathSciNet  Google Scholar 

  • Basu, A., Harris, I. R., Hjort, N. L. and Jones, M. C. (1998). Robust and efficient estimation by minimizing a density power divergence,Biometrika,85, 549–559.

    Article  MATH  MathSciNet  Google Scholar 

  • Beran, R. (1977). Minimum hellinger distance estimates for parametric models,Annals of Statistics,5, 445–463.

    MATH  MathSciNet  Google Scholar 

  • Bosq, D. (1996).Nonparametric Statistics for Stochastic Processes; Estimation and Prediction, Lecture Notes in Statistics, No. 10, Springer, New York.

    MATH  Google Scholar 

  • Bustos, O. H. (1982). GeneralM-estimates for contaminatedpth-order autoregressive processes: Consistency and asymptotic normality,Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete,59, 491–504.

    Article  MATH  MathSciNet  Google Scholar 

  • Cao, R., Cuevas, A. and Fraiman, R. (1995). Minimum distance density-based estimation,Computational Statistics and Data Analysis,20, 611–631.

    Article  MATH  MathSciNet  Google Scholar 

  • Chan, J. and Gupta, A. K. (2000),Parametric Statistical Change Point Analysis, Birkhäuser, Boston.

    Google Scholar 

  • Csörgö, M. and Horváth, L. (1997),Limit Theorems in Change-point Analysis, Wiley, New York.

    MATH  Google Scholar 

  • Denby, L. and Martin, D. (1979). Robust estimation of the first-order autoregressive parameter,Journal of the American Statistical Association,74, 140–146.

    Article  MATH  Google Scholar 

  • Ferguson, T. S. (1996).A Course in Large Sample Theory, Chapman & Hall, London.

    MATH  Google Scholar 

  • Fox, A. J. (1972). Outliers in time series,Journal of the Royal Statistical Society. Series B,34, 350–363.

    MATH  Google Scholar 

  • Hong, C. and Kim, Y. (2001). Automatic selection of the tuning parameter in the minimum density power divergence estimation,Journal of the Korean Statistical Society,30, 453–465.

    MathSciNet  Google Scholar 

  • Inclán, C. and Tiao, G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variances,Journal of the American Statistical Association,89, 913–923.

    Article  MATH  MathSciNet  Google Scholar 

  • Lee, S. and Park, S. (2001). The cusum of squares test for scale changes in infinite order moving average processes,Scandinavian Journal of Statistics,28, 625–644.

    Article  MATH  MathSciNet  Google Scholar 

  • Lee, S., Ha, J., Na, O. and Na, S. (2003). The cusum test for parameter change in time series models,Scandinavian Journal of Statistics,30, 781–796.

    Article  MATH  MathSciNet  Google Scholar 

  • Page, E. S. (1955). A test for change in a parameter occurring at an unknown point.Biometrika,42, 523–527.

    Article  MATH  MathSciNet  Google Scholar 

  • Peligrad, M. (1986). Recent advances in the central limit theorem and its weak invariance principle for mixing sequences of random variables (a survey),Dependence in Probability and Statistics eds. (E. Eberlein and M. S. Taqqu), 193–223, Birkhäuser, Boston.

    Google Scholar 

  • Simpson, D. G. (1987). Minimum hellinger distance estimation for the analysis of count data.Journal of the American Statistical Association,82, 802–807.

    Article  MATH  MathSciNet  Google Scholar 

  • Tamura, R. N. and Boos, D. D. (1986). Minimum hellinger distance estimation for multivariate location and covariance,Journal of the American Statistical Association,81, 223–239.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

About this article

Cite this article

Lee, S., Na, O. Test for parameter change based on the estimator minimizing density-based divergence measures. Ann Inst Stat Math 57, 553–573 (2005). https://doi.org/10.1007/BF02509239

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02509239

Key words and phrases

Navigation