The Study of the Simulation of the Efficiency of Wavelet Estimation of Trend Functions under Long-term Dependence Errors

Document Type : Scientific Research

Authors

1 Assistant Professor, Department of Statistics‎, ‎PayameNoor University

2 Department of Statistics‎, ‎PayameNoor University

Abstract

In this paper, we examine the estimation for trend functions in a time series model with Gaussian dependent residues with the aid of wavelet techniques. Using the simulations on the five different test functions and the   process and taking into account the desired function, the factors affecting the error in our estimation have been discussed. The results show that the error rate of the wavelet method depends on the long-term dependence length. Finally, according to our simulations, the wavelet estimator method is compared with the so called classical methods of Kernel estimation and the results revealed that Wavelet estimations are more efficient.

Keywords


 [1] علم شاهی، نجمه (1395). برآورد بهینه مجانبی موجک توابع روند تحت وابستگی دراز مدت، دانشگاه پیام نور مرکز مشهد.
 
[2] Bardet, J.M., Lang, G., Moulines, E. and Soulier, P. (2000).Wavelet estimator of long- range dependent processes. Stat. Inference Stoch. Process. 3 85–99. MR1819288.
[3] Beran, J. (1986). Estimation, testing and prediction for self-similar and related processes. Doctoral thesis, ETH, Zurich.
[4] Beran, J. (1994). Statistics for Long-Memory Processes. London: Chapman and Hall. MR1304490.
[5] Beran, J. and Feng, Y. (2002). SEMIFAR models – a semiparametric framework for modeling trends, long-range dependence and nonstationarity. Comput. Statist. Data Anal. 40 393– 419. MR1924017.
[6] Beran, J. and Shumeyko, Y. (2012). On asymptotically optimal wavelet estimation of trend functions under long-range dependence. Bernoulli, 2012, Vol. 18, No. 1, 137–176.
[7] Box, G.E.P. and Jenkins, G.M. (1970) Time series analysis: forecasting and control. Holden Day, San Francisco.
[8] Cox, D.R. (1984). Long-range dependence: a review. In H.A. David and H.T. David (eds), Statistics: An Appraisal. Proceedings of a Conference Marking the 50th Anniversary of the Statistical Laboratory, Iowa State University, pp. 55±74. Ames: Iowa State University Press.
[9] Craigmile, Peter F. and Percival, Donald B. Wavelet-Based Trend Detection and Estimation. WA 98195–4322. WA 98195–5640. WA 98109–3044.
[10] Donoho, D.L. and Johnstone, I.M. (1992). Minimax estimation via wavelet shrinkage. Technical Report No.402, Department of Stastistics, Stanford University, to appear in Ann Statist. 1997.
[11] Granger, C.W.J. and Joyeux, R. (1980). An introduction to long-range time series models and fractional differencing. J. Time Ser. Anal., 1, 15±30.
[12] Hall, P. and Hart, J.D. (1990a). Nonparametric regression with long-range dependence. Stochastic Process. Appl., 36, 339±351.
[13] Hosking, J.R.M. (1981). Fractional differencing. Biometrika, 68, 165±176.
[14] Hurst, H. (1951): Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers 116:770–808.
[15] Hurst, H. (1955). Methods of using long-term storage in reservoirs. Proceedings of the Institution of Civil Engineers, Part I: 519–577.
[16] Johnstone, I.M. and Silverman, B.W. (1997) Wavelet threshold estimators for data with correlated noise. J. Roy. Statist. Soc. Ser. B, 59, 319±351.
[17] KuÈnsch, H., Beran, J. and Hampel, F. (1993) Contrasts under long-range correlations. Ann. Statist., 21, 943±964.
[18] Mandelbrot, B. (1965). “Une classe de processus stochastiques homothetiques a soi; application a loi climatologique de H. E. Hurst,” Comptes Rendus Academic Sciences Paris, vol. 240, pp. 3274–3277.
[19] Mandelbrot, B. and Van Ness, J. “Fractional Brownian motions, fractional noises and applications,” SIAM Review, vol. 10, pp. 422–437, 1968.
[20] Mandelbrot, B. and Wallis, J. (1968). “Noah, Joseph and operational hydrology,” Water Resources Research, vol. 4, pp. 909–918.
[21] Mandelbrot, B. and Taqqu, M. (1979). “Robust R/S analysis of long-run serial correlation,” in Proceedings of the 42nd Session of the International Statistical Institute, pp. 69–104, Manila: Bulletin of the I.S.I.
[22] Mandelbrot, B. (1983). The Fractal Geometry of Nature. San Francisco: W. H. Freeman and Co. [22] Priestley, M. B. (1981). Spectral Analysis and Time Series. (Vol. 1): Univariate Series. London: Academic Press.
[23] Vidakovic, B. (1999). Statistical Modeling by Wavelets. New York: Wiley. MR1681904.
[24] Wang, Y. (1996). Function estimation via wavelet shrinkage for long-memory data. Ann. Statist. 24 466–484. MR1394972.
[25] Yajima, Y. (1991). Asymptotic properties of LSE in a regression model with long-memory stationary errors. Ann. Statist., 19, 158±177.