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WEAK CONVERGENCE FOR STATIONARY BOOTSTRAP EMPIRICAL PROCESSES OF ASSOCIATED SEQUENCES

  • Hwang, Eunju (Department of Applied Statistics Gachon University)
  • Received : 2020.02.06
  • Accepted : 2020.07.21
  • Published : 2021.01.01

Abstract

In this work the stationary bootstrap of Politis and Romano [27] is applied to the empirical distribution function of stationary and associated random variables. A weak convergence theorem for the stationary bootstrap empirical processes of associated sequences is established with its limiting to a Gaussian process almost surely, conditionally on the stationary observations. The weak convergence result is proved by means of a random central limit theorem on geometrically distributed random block size of the stationary bootstrap procedure. As its statistical applications, stationary bootstrap quantiles and stationary bootstrap mean residual life process are discussed. Our results extend the existing ones of Peligrad [25] who dealt with the weak convergence of non-random blockwise empirical processes of associated sequences as well as of Shao and Yu [35] who obtained the weak convergence of the mean residual life process in reliability theory as an application of the association.

Keywords

Acknowledgement

This work was supported by National Research Foundation of Korea (NRF-2018R1D1A1B07048745).

References

  1. M. Asadi and A. Berred, Properties and estimation of the mean past lifetime, Statistics 46 (2012), no. 3, 405-417. https://doi.org/10.1080/02331888.2010.540666
  2. P. Billingsley, Convergence of Probability Measures, John Wiley & Sons, Inc., New York, 1968.
  3. P. Buhlmann, Blockwise bootstrapped empirical process for stationary sequences, Ann. Statist. 22 (1994), no. 2, 995-1012. https://doi.org/10.1214/aos/1176325508
  4. P. Buhlmann, The blockwise bootstrap for general empirical processes of stationary sequences, Stochastic Process. Appl. 58 (1995), no. 2, 247-265. https://doi.org/10.1016/0304-4149(95)00019-4
  5. A. Bulinski and A. Shashkin, Limit theorems for associated random fields and related systems, Advanced Series on Statistical Science & Applied Probability, 10, World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ, 2007. https://doi.org/10.1142/9789812709417
  6. P. Doukhan, Stochastic models for time series, Mathematiques & Applications (Berlin), 80, Springer, Cham, 2018. https://doi.org/10.1007/978-3-319-76938-7
  7. P. Doukhan, G. Lang, A. Leucht, and M. H. Neumann, Dependent wild bootstrap for the empirical process, J. Time Series Anal. 36 (2015), no. 3, 290-314. https://doi.org/10.1111/jtsa.12106
  8. P. Doukhan and S. Louhichi, A new weak dependence condition and applications to moment inequalities, Stochastic Process. Appl. 84 (1999), no. 2, 313-342. https://doi.org/10.1016/S0304-4149(99)00055-1
  9. P. Doukhan and D. Surgailis, Functional central limit theorem for the empirical process of short memory linear processes, C. R. Acad. Sci. Paris Ser. I Math. 326 (1998), no. 1, 87-92. https://doi.org/10.1016/S0764-4442(97)82718-8
  10. F. El Ktaibi, B. G. Ivanoff, and N. C. Weber, Bootstrapping the empirical distribution of a linear process, Statist. Probab. Lett. 93 (2014), 134-142. https://doi.org/10.1016/j.spl.2014.06.019
  11. F. El Ktaibi and B. G. Ivanoff, Bootstrapping the empirical distribution of a stationary process with change-point, Electron. J. Stat. 13 (2019), no. 2, 3572-3612. https://doi.org/10.1214/19-ejs1613
  12. J. K. Ghosh, A new proof of the Bahadur representation of quantiles and an application, Ann. Math. Statist. 42 (1971), 1957-1961. https://doi.org/10.1214/aoms/1177693063
  13. E. Hwang and D. W. Shin, Stationary bootstrapping for non-parametric estimator of nonlinear autoregressive model, J. Time Series Anal. 32 (2011), no. 3, 292-303. https://doi.org/10.1111/j.1467-9892.2010.00699.x
  14. E. Hwang and D. W. Shin, Strong consistency of the stationary bootstrap under ψ-weak dependence, Stat. Probab. Lett. 82 (2012), 488-495. https://doi.org/10.1016/j.spl.2011.12.001
  15. E. Hwang and D. W. Shin, Random central limit theorems for linear processes with weakly dependent innovations, J. Korean Stat. Soc. 41 (2012), 313-322. https://doi.org/10.1016/j.jkss.2011.10.004
  16. E. Hwang and D. W. Shin, Stationary bootstrapping realized volatility under market microstructure noise, Electron. J. Stat. 7 (2013), 2032-2053. https://doi.org/10.1214/13-EJS834
  17. E. Hwang and D. W. Shin, Stationary bootstrapping realized volatility, Stat. Probab. Lett. 83 (2013), 2045-2051. https://doi.org/10.1016/j.spl.2013.05.005
  18. E. Hwang and D. W. Shin, Stationary bootstrapping for semiparametric panel unit root tests, Comput. Statist. Data Anal. 83 (2015), 14-25. https://doi.org/10.1016/j.csda.2014.09.004
  19. H. R. Kunsch, The jackknife and the bootstrap for general stationary observations, Ann. Statist. 17 (1989), no. 3, 1217-1241. https://doi.org/10.1214/aos/1176347265
  20. S. Lee, Random central limit theorem for the linear process generated by a strong mixing process, Statist. Probab. Lett. 35 (1997), no. 2, 189-196. https://doi.org/10.1016/S0167-7152(97)00013-8
  21. S. Louhichi, Weak convergence for empirical processes of associated sequences, Ann. Inst. H. Poincare Probab. Statist. 36 (2000), no. 5, 547-567. https://doi.org/10.1016/S0246-0203(00)00140-0
  22. U. V. Naik-Nimbalkar and M. B. Rajarshi, Validity of blockwise bootstrap for empirical processes with stationary observations, Ann. Statist. 22 (1994), no. 2, 980-994. https://doi.org/10.1214/aos/1176325507
  23. M. S. Noughabi and M. Kayid, Bivariate quantile residual life: a characterization theorem and statistical properties, Statist. Papers 60 (2019), no. 6, 2001-2012. https://doi.org/10.1007/s00362-017-0905-9
  24. A. Parvardeh, A note on the asymptotic distribution of the estimation of the mean past lifetime, Statist. Papers 56 (2015), no. 1, 205-215. https://doi.org/10.1007/s00362-013-0575-1
  25. M. Peligrad, On the blockwise bootstrap for empirical processes for stationary sequences, Ann. Probab. 26 (1998), no. 2, 877-901. https://doi.org/10.1214/aop/1022855654
  26. H. Pishro-Nik, Introduction to Probability Statistics and Random Processes, Kappa Research. LLC. www.probabilitycourse.com, 2016.
  27. D. N. Politis and J. P. Romano, The stationary bootstrap, J. Amer. Statist. Assoc. 89 (1994), no. 428, 1303-1313. https://doi.org/10.1080/01621459.1994.10476870
  28. D. N. Politis and J. P. Romano, Limit theorems for weakly dependent Hilbert space valued random variables with applications to the stationary bootstrap, Stat. Sin. 4 (1994), 461-476.
  29. D. Pollard, Convergence of Stochastic Processes, Springer Series in Statistics, Springer-Verlag, New York, 1984. https://doi.org/10.1007/978-1-4612-5254-2
  30. D. Radulovic, The bootstrap for empirical processes based on stationary observations, Stochastic Process. Appl. 65 (1996), no. 2, 259-279. https://doi.org/10.1016/S0304-4149(96)00102-0
  31. D. Radulovic, The bootstrap of empirical processes for α-mixing sequences, in High Dimensional Probability (E. Eberlein, M. Hahn and M. Talagrand, eds.), 315-330, Birkhauser, Basel, 1998.
  32. G. G. Roussas, Kernel estimates under association: strong uniform consistency, Statist. Probab. Lett. 12 (1991), no. 5, 393-403. https://doi.org/10.1016/0167-7152(91)90028-P
  33. X. Shao, The dependent wild bootstrap, J. Amer. Statist. Assoc. 105 (2010), no. 489, 218-235. https://doi.org/10.1198/jasa.2009.tm08744
  34. Q. M. Shao and H. Yu, Bootstrapping the sample means for stationary mixing sequences, Stochastic Process. Appl. 48 (1993), no. 1, 175-190. https://doi.org/10.1016/0304-4149(93)90113-I
  35. Q. M. Shao and H. Yu, Weak convergence for weighted empirical processes of dependent sequences, Ann. Probab. 24 (1996), no. 4, 2098-2127. https://doi.org/10.1214/aop/1041903220
  36. B. Wieczorek, Blockwise bootstrap of the estimated empirical process based on ψ-weakly dependent observations, Stat. Inference Stoch. Process. 19 (2016), no. 1, 111-129. https://doi.org/10.1007/s11203-015-9120-2
  37. H. Yu, A Glivenko-Cantelli lemma and weak convergence for empirical processes of associated sequences, Probab. Theory Related Fields 95 (1993), no. 3, 357-370. https://doi.org/10.1007/BF01192169