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Parameter estimation in mixed fractional stochastic heat equation
Volume 10, Issue 2 (2023), pp. 175–195
Diana Avetisian   Kostiantyn Ralchenko ORCID icon link to view author Kostiantyn Ralchenko details  

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https://doi.org/10.15559/23-VMSTA221
Pub. online: 24 January 2023      Type: Research Article      Open accessOpen Access

Received
8 August 2022
Revised
29 November 2022
Accepted
15 January 2023
Published
24 January 2023

Abstract

The paper is devoted to a stochastic heat equation with a mixed fractional Brownian noise. We investigate the covariance structure, stationarity, upper bounds and asymptotic behavior of the solution. Based on its discrete-time observations, we construct a strongly consistent estimator for the Hurst index H and prove the asymptotic normality for $H < 3/4$. Then assuming the parameter H to be known, we deal with joint estimation of the coefficients at the Wiener process and at the fractional Brownian motion. The quality of estimators is illustrated by simulation experiments.

References

[1] 
Arcones, M.A.: Limit theorems for nonlinear functionals of a stationary Gaussian sequence of vectors. Ann. Probab. 22(4), 2242–2274 (1994). https://doi.org/10.1214/aop/1176988503
[2] 
Avetisian, D., Ralchenko, K.: Ergodic properties of the solution to a fractional stochastic heat equation, with an application to diffusion parameter estimation. Mod. Stoch. Theory Appl. 7(3), 339–356 (2020). https://doi.org/10.15559/20-VMSTA162
[3] 
Avetisian, D.A., Ralchenko, K.V.: Estimation of the Hurst and diffusion parameters in fractional stochastic heat equation. Theory Probab. Math. Stat. 104, 61–76 (2021). https://doi.org/10.1090/tpms/1145
[4] 
Avetisian, D.A., Shevchenko, G.M.: Estimation of diffusion parameter for stochastic heat equation with white noise. Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics & Mathematics 3, 9–16 (2018). https://doi.org/10.17721/1812-5409.2018/3.1
[5] 
Bibinger, M., Trabs, M.: Volatility estimation for stochastic PDEs using high-frequency observations. Stoch. Process. Appl. 130(5), 3005–3052 (2020). MR4080737. https://doi.org/10.1016/j.spa.2019.09.002
[6] 
Cai, C., Chigansky, P., Kleptsyna, M.: Mixed Gaussian processes: A filtering approach. Ann. Probab. 44(4), 3032–3075 (2016). MR3531685. https://doi.org/10.1214/15-AOP1041
[7] 
Cai, C.H., Huang, Y.Z., Sun, L., Xiao, W.L.: Maximum likelihood estimation for mixed fractional Vasicek processes. Fractal Fract. 6(1), 44 (2022). https://doi.org/10.3390/fractalfract6010044
[8] 
Cheridito, P.: Mixed fractional Brownian motion. Bernoulli 7(6), 913–934 (2001). MR1873835. https://doi.org/10.2307/3318626
[9] 
Cialenco, I., Huang, Y.: A note on parameter estimation for discretely sampled SPDEs. Stoch. Dyn. 20(3), 2050016–28 (2020). MR4101083. https://doi.org/10.1142/S0219493720500161
[10] 
Cialenco, I., Kim, H.-J.: Parameter estimation for discretely sampled stochastic heat equation driven by space-only noise. Stoch. Process. Appl. 143, 1–30 (2022). MR4332773. https://doi.org/10.1016/j.spa.2021.09.012
[11] 
Crowder, M.J.: Maximum likelihood estimation for dependent observations. J. R. Stat. Soc. B 38(1), 45–53 (1976). MR0403035
[12] 
Da Silva, J.L., Erraoui, M.: Mixed stochastic differential equations: Existence and uniqueness result. J. Theor. Probab. 31(2), 1119–1141 (2018). MR3803926. https://doi.org/10.1007/s10959-016-0738-9
[13] 
Ditlevsen, S., De Gaetano, A.: Mixed effects in stochastic differential equation models. REVSTAT 3(2), 137–153 (2005). MR2259358
[14] 
Dozzi, M., Mishura, Y., Shevchenko, G.: Asymptotic behavior of mixed power variations and statistical estimation in mixed models. Stat. Inference Stoch. Process. 18(2), 151–175 (2015). MR3348583. https://doi.org/10.1007/s11203-014-9106-5
[15] 
Dufitinema, J., Pynnönen, S., Sottinen, T.: Maximum likelihood estimators from discrete data modeled by mixed fractional Brownian motion with application to the Nordic stock markets. Commun. Stat., Simul. Comput. 51(9), 5264–5287 (2022). MR4491681. https://doi.org/10.1080/03610918.2020.1764581
[16] 
Guerra, J., Nualart, D.: Stochastic differential equations driven by fractional Brownian motion and standard Brownian motion. Stoch. Anal. Appl. 26(5), 1053–1075 (2008). MR2440915. https://doi.org/10.1080/07362990802286483
[17] 
Han, M., Xu, Y., Pei, B.: Mixed stochastic differential equations: averaging principle result. Appl. Math. Lett. 112, 106705–7 (2021). MR4145479. https://doi.org/10.1016/j.aml.2020.106705.
[18] 
Isserlis, L.: On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables. Biometrika 12(1/2), 134–139 (1918). https://doi.org/10.1093/biomet/12.1-2.134
[19] 
Kozachenko, Y., Melnikov, A., Mishura, Y.: On drift parameter estimation in models with fractional Brownian motion. Statistics 49(1), 35–62 (2015). MR3304366. https://doi.org/10.1080/02331888.2014.907294.
[20] 
Kubilius, K., Mishura, Y., Ralchenko, K.: Parameter Estimation in Fractional Diffusion Models. Bocconi & Springer Series, vol. 8, p. 390. Bocconi University Press, Milan Springer, Cham (2017). MR3752152. https://doi.org/10.1007/978-3-319-71030-3
[21] 
Kukush, A., Lohvinenko, S., Mishura, Y., Ralchenko, K.: Two approaches to consistent estimation of parameters of mixed fractional Brownian motion with trend. Stat. Inference Stoch. Process. 25(1), 159–187 (2022). MR4419677. https://doi.org/10.1007/s11203-021-09252-6
[22] 
Markussen, B.: Likelihood inference for a discretely observed stochastic partial differential equation. Bernoulli 9(5), 745–762 (2003). MR2047684. https://doi.org/10.3150/bj/1066418876
[23] 
Mishura, Y., Ralchenko, K., Shklyar, S.: Maximum likelihood drift estimation for gaussian process with stationary increments. Austrian Journal of Statistics 46(3–4), 67–78 (2017). https://doi.org/10.17713/ajs.v46i3-4.672
[24] 
Mishura, Y., Ralchenko, K., Shevchenko, G.: Existence and uniqueness of mild solution to stochastic heat equation with white and fractional noises. Theory Probab. Math. Stat. 98, 149–170 (2019). https://doi.org/10.1090/tpms/1068
[25] 
Mishura, Y.S., Shevchenko, G.M.: Rate of convergence of Euler approximations of solution to mixed stochastic differential equation involving Brownian motion and fractional Brownian motion. Random Oper. Stoch. Equ. 19(4), 387–406 (2011). MR2871847. https://doi.org/10.1515/ROSE.2011.021
[26] 
Mishura, Y.S., Shevchenko, G.M.: Existence and uniqueness of the solution of stochastic differential equation involving Wiener process and fractional Brownian motion with Hurst index $H>1/2$. Commun. Stat., Theory Methods 40(19–20), 3492–3508 (2011). MR2860753. https://doi.org/10.1080/03610926.2011.581174
[27] 
Mishura, Y., Shevchenko, G.: Mixed stochastic differential equations with long-range dependence: Existence, uniqueness and convergence of solutions. Comput. Math. Appl. 64(10), 3217–3227 (2012). MR2989350. https://doi.org/10.1016/j.camwa.2012.03.061
[28] 
Mishura, Y., Zili, M.: Stochastic Analysis of Mixed Fractional Gaussian Processes p. 194. ISTE Press, London Elsevier Ltd, Oxford (2018). MR3793191
[29] 
Patrizio, C.R., Thompson, D.W.: Understanding the role of ocean dynamics in midlatitude sea surface temperature variability using a simple stochastic climate model. J. Climate 35(11), 3313–3333 (2022). https://doi.org/10.1175/JCLI-D-21-0184.1
[30] 
Piterbarg, L., Rozovskii, B.: Maximum likelihood estimators in the equations of physical oceanography. In: Stochastic Modelling in Physical Oceanography. Progr. Probab., vol. 39, pp. 397–421. Birkhäuser Boston, Boston, MA (1996)
[31] 
Prakasa Rao, B.L.S.: Maximum likelihood estimation in the mixed fractional Vasicek model. Journal of the Indian Society for Probability and Statistics 22 (2021). https://doi.org/10.1007/s41096-020-00094-8
[32] 
Schott, J.R.: Matrix Analysis for Statistics, 2nd edn. Wiley Series in Probability and Statistics. Wiley-Interscience. John Wiley & Sons, Hoboken, NJ (2005)
[33] 
Shevchenko, G.: Mixed stochastic delay differential equations. Theory Probab. Math. Stat. 89, 181–195 (2014). MR3235184. https://doi.org/10.1090/S0094-9000-2015-00944-3
[34] 
Vergara, R.C.: Development of geostatistical models using stochastic partial differential equations. PhD thesis, MINES, Paris Tech (2018). http://cg.ensmp.fr/bibliotheque/public/CARRIZO_These_02513.pdf
[35] 
Wiqvist, S., Golightly, A., McLean, A.T., Picchini, U.: Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. Comput. Stat. Data Anal. 157, 107151–26 (2021). MR4192029. https://doi.org/10.1016/j.csda.2020.107151
[36] 
Zili, M.: On the mixed fractional Brownian motion. J. Appl. Math. Stoch. Anal., 32435–9 (2006). MR2253522. https://doi.org/10.1155/JAMSA/2006/32435

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© 2023 The Author(s). Published by VTeX
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Keywords
Stochastic partial differential equation mixed fractional Brownian motion Hurst index estimation strong consistency asymptotic normality

MSC2010
60G22 60H15 62F10 62F12

Funding
KR was supported by the Sydney Mathematical Research Institute under Ukrainian Visitors Program.

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