Skip to main content
Log in

Sampling Properties of U-statistics for a Class of Stationary Nonlinear Processes

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

Abstract

We consider the sampling properties of U-statistics based on a sample of realization from a class of stationary nonlinear processes which include, in particular, linear, bilinear and finite order volterra processes. It is shown that if the size n of the realization tends to infinity then certain normalized versions of the U-statistics tend to be distributed normally with zero means and finite variances.

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

  • Anderson T.W. (1971). The statistical analysis of time series. Wiley, New York

    MATH  Google Scholar 

  • Berk K.N. (1973). A central limit theorem for m-dependent random variables with unbounded m. Annals of Probability 1, 352–354

    MATH  MathSciNet  Google Scholar 

  • Borovkova S., Burton R., Dehling H. (2001). Limit theorems for functionals of mixing processes with applications to U-statistics and dimension estimation. Transaction of the American Mathematical Society 353, 4261–4318

    Article  MATH  MathSciNet  Google Scholar 

  • Bradley R.C. (1983). Approximation theorems for strongly mixing random variables. Michigan Mathematical Journal 30, 69–81

    Article  MATH  MathSciNet  Google Scholar 

  • Bradley R.C. (1986). Basic properties of strong mixing conditions. In: Eberlein E., Taquu M.S.(eds). Dependence in probability and statistics. Birkhauser, Boston, pp. 165–192

    Google Scholar 

  • Bradley R.C. (2001) Introduction to strong mixing conditions: Vol. I–III. Custom Publishing, Indiana University, Bloomington

    Google Scholar 

  • Chanda K.C. (1974). Strong mixing properties of linear stochastic processes. Journal of Applied Probability 11, 401–408

    Article  MathSciNet  Google Scholar 

  • Chanda K.C. (1991). Stationarity and central limit theorem associated with bilinear time series models. Journal of Time Series Analysis 12, 301–313

    MATH  MathSciNet  Google Scholar 

  • Chanda K.C. (2003). Density estimation for a class of stationary nonlinear processes. Annals of the Institute of Statistical Mathematics 55, 69–82

    MATH  MathSciNet  Google Scholar 

  • Davydov Y.A. (1968). Convergence of distributions generated by stationary stochastic processes. Theory Probability and its Applications 13, 691–696

    Article  MathSciNet  Google Scholar 

  • Denker M. (1986). Uniform integrability and the central limit theorem for strongly mixing processes. In: Eberlein E., Taquu M.S. (eds). Dependence in probability and statistics. Birkhauser, Boston, pp. 269–274

    Google Scholar 

  • Denker M., Keller G. (1983). On U-statistics and von Mises statistics for weakly dependent processes. Zeitschrift fuer Wahrscheinlichkeitstheorie und Verwandte Gebiete 64, 505–522

    Article  MATH  MathSciNet  Google Scholar 

  • Doukhan P. (1994). Mixing: properties and examples. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  • Goradetskii V.V. (1977). On the strong mixing properties for linear sequences. Theory of Probability and its Applications 22, 411–413

    Article  Google Scholar 

  • Hoeffding W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statististics 19, 293–325

    MathSciNet  Google Scholar 

  • Hoeffding W., Robbins H. (1948). The central limit theorem for dependent random variables. Duke Mathematematical Journal 15, 773–780

    Article  MATH  MathSciNet  Google Scholar 

  • Ibragimov I.A. (1962). Some limit theorems for stationary processes. Theory of Probability and its Applications 7, 349–382

    Article  Google Scholar 

  • Ibragimov I.A., Linnik Y.V. (1971). Independent and stationary sequences of random variables. Walter Nordhoof, Groningen

    MATH  Google Scholar 

  • Oodaira H., Yoshihara K. (1972). Functional central limit theorem for strictly stationary processes satisfying the strong mixing condition. Kodai Mathematical Journal 23, 311–334

    Article  MathSciNet  Google Scholar 

  • Phan T.D., Tran L.T. (1985). Some mixing properties of time series models. Stochastic Processes and their Applications 19, 297–303

    Article  MathSciNet  Google Scholar 

  • Priestley M.B. (1988). Nonlinear and nonstationary time series analysis. Academic, New York

    Google Scholar 

  • Rosenblatt M. (1956). A central limit theorem and a strong mixing condition. Proceedings of the National Academy of Sciences USA 42, 43–47

    Article  MATH  MathSciNet  Google Scholar 

  • Rosenblatt M. (1980). Linear processes and bispectra. Journal of Applied Probability 17, 265–270

    Article  MATH  MathSciNet  Google Scholar 

  • Sen P.K. (1972). Limiting behavior of regular functionals of empirical distribution for stationary *-mixing processes. Zeitschrift fuer Wahrscheinlichkeitstheorie und Verwandte Gebiete 25, 71–81

    Article  MATH  Google Scholar 

  • Withers C.S. (1981). Central limit theorem for dependent variables. Zeitschrift fuer Wahrscheinlichkeitstheorie und Verwandte Gebiete 57, 509–534

    Article  MATH  MathSciNet  Google Scholar 

  • Yoshihara K. (1976). Limiting behavior of U-statistics for stationary, absolutmdely regular processes. Zeitschrift fuer Wahrscheinlichkeitstheorie und Verwandte Gebiete 35, 237–252

    Article  MATH  MathSciNet  Google Scholar 

  • Yoshihara K. (1978). Probability inequalities for sums of absolutely regular processes and their applications. Zeitschrift fuer Wahrscheinlichkeitstheorie und Verwandte Gebiete 43, 319–329

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kamal C. Chanda.

About this article

Cite this article

Chanda, K.C. Sampling Properties of U-statistics for a Class of Stationary Nonlinear Processes. Ann Inst Stat Math 58, 635–646 (2006). https://doi.org/10.1007/s10463-006-0030-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10463-006-0030-3

Keywords

Navigation