A Journal "Theory of Probability and Mathematical Statistics"
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970


Archive

About   Editorial Board   Contacts   Template   Publication Ethics   Peer Review Process   Special Issues   History  

Theory of Probability and Mathematical Statistics



Estimation of the Hurst and diffusion parameters in fractional stochastic heat equation

D.A. Avetisian, K.V. Palchenko

Link

Abstract: The paper deals with a one-dimensional stochastic heat equation driven by a space-only fractional Brownian noise. We construct strongly consistent estimators of two unknown parameters, namely, the diffusion parameter $\sigma$ and the Hurst parameter $H\in(0,1)$, based on the discrete-time observations of a solution. We also prove joint asymptotic normality of the estimators in the case $H\in\left (0,\frac34\right )$.

Keywords: Stochastic partial differential equation, fractional Brownian motion, strong consistency, asymptotic normality

Bibliography:
1. S. I. Aihara and A. Bagchi, Stochastic hyperbolic dynamics for infinite-dimensional forward rates and option pricing, Math. Finance 15 (2005), no. 1, 27–47.
2. S. I. Aihara and A. Bagchi, Parameter estimation of parabolic type factor model and empirical study of US treasury bonds, System modeling and optimization, IFIP Int. Fed. Inf. Process., vol. 199, Springer, New York, 2006, pp. 207–217.
3. M. A. Arcones, Limit theorems for nonlinear functionals of a stationary Gaussian sequence of vectors, Ann. Probab. 22 (1994), no. 4, 2242–2274.
4. D. Avetisian and K. Ralchenko, Ergodic properties of the solution to a fractional stochastic heat equation, with an application to diffusion parameter estimation, Mod. Stoch. Theory Appl. 7 (2020), no. 3, 339–356.
5. D. A. Avetisian and G. M. Shevchenko, Estimation of diffusion parameter for stochastic heat equation with white noise, Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics & Mathematics (2018), no. 3, 9–16.
6. M. Bibinger and M. Trabs, On central limit theorems for power variations of the solution to the stochastic heat equation, Stochastic models, statistics and their applications, Springer Proc. Math. Stat., vol. 294, Springer, Cham, 2019, pp. 69–84.
7. M. Bibinger and M. Trabs, Volatility estimation for stochastic PDEs using high-frequency observations, Stochastic Process. Appl. 130 (2020), no. 5, 3005–3052.
8. P. Breuer and P. Major, Central limit theorems for nonlinear functionals of Gaussian fields, J. Multivariate Anal. 13 (1983), no. 3, 425–441.
9. B. Chen and J. Duan, Stochastic quantification of missing mechanisms in dynamical systems, Recent development in stochastic dynamics and stochastic analysis, Interdiscip. Math. Sci., vol. 8, World Sci. Publ., Hackensack, NJ, 2010, pp. 67–76.
10. C. Chong, High-frequency analysis of parabolic stochastic PDEs, Ann. Statist. 48 (2020), no. 2, 1143–1167.
11. P.-L. Chow, Stochastic partial differential equations, Chapman & Hall/CRC Applied Mathematics and Nonlinear Science Series, Chapman & Hall/CRC, Boca Raton, FL, 2007.
12. I. Cialenco, F. Delgado-Vences, and H.-J. Kim, Drift estimation for discretely sampled SPDEs, Stoch. Partial Differ. Equ. Anal. Comput. 8 (2020), no. 4, 895–920.
13. I. Cialenco and Y. Huang, A note on parameter estimation for discretely sampled SPDEs, Stoch. Dyn. 20 (2020), no. 3, 2050016, 28.
14. I. Cialenco and H.-J. Kim, Parameter estimation for discretely sampled stochastic heat equation driven by space-only noise revised, 2020.
15. I. Cialenco, H.-J. Kim, and S. V. Lototsky, Statistical analysis of some evolution equations driven by space-only noise, Stat. Inference Stoch. Process. 23 (2020), no. 1, 83–103.
16. R. Cont, Modeling term structure dynamics: an infinite dimensional approach, Int. J. Theor. Appl. Finance 8 (2005), no. 3, 357–380.
17. G. Da Prato and J. Zabczyk, Stochastic equations in infinite dimensions, second ed., Encyclopedia of Mathematics and its Applications, vol. 152, Cambridge University Press, Cambridge, 2014.
18. D. A. Dawson, Qualitative behavior of geostochastic systems, Stochastic Process. Appl. 10 (1980), no. 1, 1–31.
19. S. S. De, Stochastic model of population growth and spread, Bull. Math. Biol. 49 (1987), no. 1, 1–11.
20. R. L. Dobrushin and P. Major, Non-central limit theorems for nonlinear functionals of Gaussian fields, Z. Wahrsch. Verw. Gebiete 50 (1979), no. 1, 27–52.
21. M. Gubinelli, A. Lejay, and S. Tindel, Young integrals and SPDEs, Potential Anal. 25 (2006), no. 4, 307–326.
22. Y. Hu and D. Nualart, Stochastic heat equation driven by fractional noise and local time, Probab. Theory Related Fields 143 (2009), no. 1-2, 285–328.
23. L. Isserlis, On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables, Biometrika 12 (1918), no. 1/2, 134–139.
24. Y. Kaino and M. Uchida, Parametric estimation for a parabolic linear SPDE model based on discrete observations, J. Statist. Plann. Inference 211 (2021), 190–220.
25. S. V. Lototsky and B. L. Rozovskii, Stochastic partial differential equations driven by purely spatial noise, SIAM J. Math. Anal. 41 (2009), no. 4, 1295–1322.
26. Z. Mahdi Khalil and C. Tudor, Estimation of the drift parameter for the fractional stochastic heat equation via power variation, Mod. Stoch. Theory Appl. 6 (2019), no. 4, 397–417.
27. P. Major, Non-central limit theorem for non-linear functionals of vector valued gaussian stationary random fields, 2019.
28. B. Maslowski and D. Nualart, Evolution equations driven by a fractional Brownian motion, J. Funct. Anal. 202 (2003), no. 1, 277–305.
29. Yu. Mishura, K. Ralchenko, and G. Shevchenko, Existence and uniqueness of mild solution to stochastic heat equation with white and fractional noises, Theory Probab. Math. Statist. 98 (2019), 149–170
30. L. Piterbarg and B. Rozovskii, Maximum likelihood estimators in the equations of physical oceanography, Stochastic modelling in physical oceanography, Progr. Probab., vol. 39, Birkh\"auser Boston, Boston, MA, 1996, pp. 397–421.
31. K. Ralchenko and G. Shevchenko, Existence and uniqueness of mild solution to fractional stochastic heat equation, Mod. Stoch. Theory Appl. 6 (2019), no. 1, 57–79.
32. B. L. Rozovsky and S. V. Lototsky, Stochastic evolution systems, Probability Theory and Stochastic Modelling, vol. 89, Springer, Cham, 2018, Linear theory and applications to non- linear filtering, Second edition of [ MR1135324].
33. M. S. Taqqu, Convergence of integrated processes of arbitrary Hermite rank, Z. Wahrsch. Verw. Gebiete 50 (1979), no. 1, 53–83.
34. S. Tindel, C. A. Tudor, and F. Viens, Stochastic evolution equations with fractional Brownian motion, Probab. Theory Related Fields 127 (2003), no. 2, 186–204.
35. J. B. Walsh, An introduction to stochastic partial differential equations, E'cole d'et'e de probabilit'es de Saint-Flour, XIV—1984, Lecture Notes in Math., vol. 1180, Springer, Berlin, 1986, pp. 265–439.