Large deviations of a correlogram estimator of the random noise covariance function in a nonlinear regression model

TitleLarge deviations of a correlogram estimator of the random noise covariance function in a nonlinear regression model
Publication TypeJournal Article
Year of Publication2016
AuthorsMoskvychova, KK
Abbreviated Key TitleDopov. Nac. akad. nauk Ukr.
DOI10.15407/dopovidi2016.09.023
Issue9
SectionMathematics
Pagination23-28
Date Published9/2016
LanguageUkrainian
Abstract

A time continuous nonlinear regression model with mean square continuous and almost sure Gaussian stationary random noise with zero mean and positive bounded spectral density is considered. A theorem on probabilities of large deviations of a residual correlogram estimator of the random noise covariance function is proved. The result obtained sharpens previously known facts on the consistency of a correlogram estimator of the covariance function of Gaussian stationary random noise.

Keywordscorrelogram estimator, covariance function, nonlinear regression model, probability of large deviations, pseudometric, stationary Gaussian noise
References: 
  1. Pfanzagl J. Metrika, 1969, 14: 249–272. https://doi.org/10.1007/BF02613654
  2. Ivanov O. V., Moskvichova K. K. Theory Probab. Math. Statist., 2015, 90: 87–101. https://doi.org/10.1090/tpms/951
  3. Ivanov O. V., Moskvichova K. K. Ukr. Math. J., 2014, 66, No 6: 787–805 (in Ukrainian). https://doi.org/10.1007/s11253-014-0979-7
  4. Ivanov O. V., Moskvichova K. K. Naukovi visti NTUU “KPI'', 2015, 4: 57–62 (in Ukrainian).
  5. Ivanov O.V., Moskvichova K.K. Theory Probab. Math. Statist., 2015, 91: 61–70. https://doi.org/10.1090/tpms/966
  6. Ivanov A. V. Theory Probab. Appl., 1976, 21: 557–570. https://doi.org/10.1137/1121067
  7. Prakasa Rao B. L. S. Stat. Probab. Lett., 1984, 2: 139–142. https://doi.org/10.1016/0167-7152(84)90004-X
  8. Sieders A., Dzhaparidze K. Ann. Statist., 1987, 15: 1031–1049. https://doi.org/10.1214/aos/1176350491
  9. Ibragimov I. A., Has'minskii R. Z. Statistical estimation: asymptotic theory, New York: Springer, 1981. https://doi.org/10.1007/978-1-4899-0027-2
  10. Ivanov A. V., Leonenko N. N. Statistical Analysis of Random Fields, Dordecht, Boston, London: Kluwer, 1989. https://doi.org/10.1007/978-94-009-1183-3
  11. Hu Shuhe. Stoch. Proc. Appl., 1993, 47: 345–352. https://doi.org/10.1016/0304-4149(93)90022-V
  12. Ivanov A. V. Asymtotic Theory of Nonlinear Regression, Dordecht, Boston, London: Kluwer, 1997. https://doi.org/10.1007/978-94-015-8877-5
  13. Buldygin V. V., Kozachenko Yu. V. Metric characterization of random variables and random processes, Providence: AMS, 2000.