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Improved estimation method for high dimension semimartingale regression models based on discrete data

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  • Evgeny Pchelintsev

    (Tomsk State University)

  • Serguei Pergamenshchikov

    (Université de Rouen Normandie)

  • Maria Leshchinskaya

    (Tomsk State University)

Abstract

In this paper we study a high dimension (Big Data) regression model in continuous time observed in the discrete time moments with dependent noises defined by semimartingale processes. To this end an improved (shrinkage) estimation method is developed and the non-asymptotic comparison between shrinkage and least squares estimates is studied. The improvement effect for the shrinkage estimates showing the significant advantage with respect to the "small" dimension case is established. It turns out that obtained improvement effect holds true uniformly over observation frequency. Then, a model selection method based on these estimates is developed. Non-asymptotic sharp oracle inequalities for the constructed model selection procedure are obtained. Constructive sufficient conditions for the observation frequency providing the robust efficiency property in adaptive setting without using any sparsity assumption are found. A special stochastic calculus tool to guarantee these conditions for non-Gaussian Ornstein–Uhlenbeck processes is developed. Monte-Carlo simulations for the numeric confirmation of the obtained theoretical results are given.

Suggested Citation

  • Evgeny Pchelintsev & Serguei Pergamenshchikov & Maria Leshchinskaya, 2022. "Improved estimation method for high dimension semimartingale regression models based on discrete data," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 537-576, October.
    • Handle: RePEc:spr:sistpr:v:25:y:2022:i:3:d:10.1007_s11203-021-09258-0
    DOI: 10.1007/s11203-021-09258-0
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    References listed on IDEAS

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    1. Fourdrinier, Dominique & Strawderman, William E., 1996. "A Paradox Concerning Shrinkage Estimators: Should a Known Scale Parameter Be Replaced by an Estimated Value in the Shrinkage Factor?," Journal of Multivariate Analysis, Elsevier, vol. 59(2), pages 109-140, November.
    2. Victor Konev & Serguei Pergamenchtchikov, 2010. "General model selection estimation of a periodic regression with a Gaussian noise," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 1083-1111, December.
    3. E. A. Pchelintsev & S. M. Pergamenshchikov, 2018. "Oracle inequalities for the stochastic differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 469-483, July.
    4. Vlad Stefan Barbu & Slim Beltaief & Sergey Pergamenshchikov, 2019. "Robust adaptive efficient estimation for semi-Markov nonparametric regression models," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 187-231, July.
    5. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    6. D. Fourdrinier & S. Pergamenshchikov, 2007. "Improved Model Selection Method for a Regression Function with Dependent Noise," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(3), pages 435-464, September.
    7. Evgeny Pchelintsev, 2013. "Improved estimation in a non-Gaussian parametric regression," Statistical Inference for Stochastic Processes, Springer, vol. 16(1), pages 15-28, April.
    8. De Gregorio, Alessandro & Iacus, Stefano M., 2012. "Adaptive Lasso-Type Estimation For Multivariate Diffusion Processes," Econometric Theory, Cambridge University Press, vol. 28(4), pages 838-860, August.
    9. Reinhard Höpfner & Yury Kutoyants, 2010. "Estimating discontinuous periodic signals in a time inhomogeneous diffusion," Statistical Inference for Stochastic Processes, Springer, vol. 13(3), pages 193-230, October.
    10. Belkacem Berdjane & Serguei Pergamenshchikov, 2013. "Optimal consumption and investment for markets with random coefficients," Finance and Stochastics, Springer, vol. 17(2), pages 419-446, April.
    11. Yuri Kabanov & Serguei Pergamenshchikov, 2020. "Ruin probabilities for a Lévy-driven generalised Ornstein–Uhlenbeck process," Finance and Stochastics, Springer, vol. 24(1), pages 39-69, January.
    12. E. A. Pchelintsev & V. A. Pchelintsev & S. M. Pergamenshchikov, 2019. "Improved robust model selection methods for a Lévy nonparametric regression in continuous time," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(3), pages 612-628, July.
    13. L. Galtchouk & S. Pergamenshchikov, 2009. "Sharp non-asymptotic oracle inequalities for non-parametric heteroscedastic regression models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(1), pages 1-18.
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