«THE BULLETIN OF IRKUTSK STATE UNIVERSITY». SERIES «MATHEMATICS»
«IZVESTIYA IRKUTSKOGO GOSUDARSTVENNOGO UNIVERSITETA». SERIYA «MATEMATIKA»
ISSN 1997-7670 (Print)
ISSN 2541-8785 (Online)

List of issues > Series «Mathematics». 2020. Vol. 31

Fractional Smoothness of Distributions of Trigonometric Polynomials on a Space with a Gaussian Measure

Author(s)
G. I. Zelenov
Abstract

In this paper we study properties of images of a gaussian measure under trigonometric polynomials of a fixed degree, defined on finite-dimensional space with fixed number of dimensions. We prove that the images of the n-dimensional Gaussian measure under trigonometric polynomials have densities from the Nikolskii – Besov class of fractional parameter. This property of images of a gaussian measure is used for estimating the total variation distance between such images via the Fortet – Mourier distance. We also generalize these results to the case of k-dimensional mappings whose components are trigonometric polynomials.

About the Authors

Georgii Zelenov, PhD (Phys.–Math.), Junior Research Fellow, Moscow State University, 1, Leninskie Gory, Moscow, 119991, Russian Federation; Assoc. Prof., National Research University Higher School of Economics, 20, Myasnitskaya ulitsa, Moscow, 101000, Russian Federation, e-mail: zelenovyur@gmail.com

For citation

Zelenov G.I. Fractional Smoothness of Distributions of Trigonometric Polynomials on a Space with a Gaussian Measure. The Bulletin of Irkutsk State University. Series Mathematics, 2020, vol. 31, pp. 78-95. (in Russian) https://doi.org/10.26516/1997-7670.2020.31.78

Keywords
Nikolskii – Besov class, Gaussian measure, distribution of a trigonometric polynomial
UDC
519.2
MSC
60E05, 60E015; 28C20, 60F99
DOI
https://doi.org/10.26516/1997-7670.2020.31.78
References

1. Besov O.V., Il’in V.P., Nikol’skiı S.M. Integral representations of functions and imbedding theorems. V. I, II, Winston & Sons, Washington; Halsted Press, New York, Toronto, London, 1978, 1979, 480 p.

2. Bogachev V.I. Distributions of polynomials on multidimensional and infinite- dimensional spaces with measures. Russian Math. Surveys, 2016, vol. 71, no. 4, pp. 703-749.

3. Bogachev V.I., Zelenov G.I., Kosov E.D. Membership of distributions of polyno- mials in the Nikolskii – Besov class, Dokl. Math., 2016, vol. 94, no. 2, pp. 453-457. https://doi.org/10.1134/S1064562416040293

4. Bogachev V.I., Kosov E.D., Popova S.N. Characterization of Nikolskii–Besov classes in terms of integration by parts. Dokl. Math., 2017, vol. 96, no 2, pp. 449–453. https://doi.org/10.1134/S106456241705012X

5. Bogachev V.I., Kosov E.D., Popova S.N. On Gaussian Nikolskii–Besov classes. Dokl. Math., 2017, vol. 96, no. 2, pp. 498-502. https://doi.org/10.1134/S1064562417050295

6. Davydov Y.A., Martynova G.V. Limit behavior of multiple stochastic integral. Statistics and Control of Random Processes. Nauka, Preila, Moscow, 1987, pp. 55-57 (in Russian).

7. Kosov E.D. Besov classes on a space with a Gaussian measure. Dokl. Math., 2018, vol. 97, no. 1, pp. 20-22. https://doi.org/10.1134/s1064562418010076

8. Kosov E.D. Besov classes on finite- and infinite-dimensional spaces. Sb. Math. 2019, vol. 210, no. 5, pp. 663-692. https://doi.org/10.1070/SM9058

9. Martynova G.V. Limit theorems for the functionals of random procceses, Cand. sci. dis. Leningrad, LGU Publ., 1987 (in Russian).

10. Nazarov F.L. Local estimates for exponential polynomials and their applications to inequalities of the uncertainty principle type. St. Petersburg Math. J., 1994, vol. 5, no. 4, pp. 663-717.

11. Nazarov F., Sodin M., Volberg A. The geometric Kannan – Lovasz – Simonovits lemma, dimension-free estimates for the distribution of the values of polynomials, and the distribution of the zeros of random analytic functions. St. Petersburg Math. J., 2003, vol. 14, no. 2, pp. 351-366.

12. Nikol’skiı S.M. Approximation of functions of several variables and imbedding theorems. Springer-Verlag, New York, Heidelberg, 1975, 456 p.

13. Adams R.A., Fournier J.J. Sobolev spaces. New York, Academic Press, 2003, 310 p.

14. Bogachev V.I. Differentiable measures and the Malliavin calculus. Amer. Math. Soc., Rhode Island, Providence, 2010, 510 p.

15. Bogachev V.I. Gaussian measures. Amer. Math. Soc., Providence, Rhode Island, 1998, 450 p.

16. Bogachev V.I. Measure theory. Vol. 1,2. New York, Springer, 2007, 1170 p. https://doi.org/10.1007/978-3-540-34514-5

17. Bogachev V.I., Kosov E.D., Popova S.N. A new approach to Nikolskii– Besov classes. Moscow Math. J., 2019, vol. 19, no. 4, pp. 619-654. https://doi.org/10.17323/1609-4514-2019-19-4-619-654

18. Bogachev V., Kosov E., Zelenov G. Fractional smoothness of distributions of polynomials and a fractional analog of the Hardy–Landau–Littlewood inequality. Trans. Amer. Math. Soc., 2018, vol. 370, no. 6., pp. 4401–4432. https://doi.org/10.1090/tran/7181

19. Carbery A., Wright J. Distributional and Lq norm inequalities for polynomials over convex bodies in Rn. Math. Research Lett., 2001, vol. 8, no. 3. pp. 233-248. https://doi.org/10.4310/MRL.2001.v8.n3.a1

20. Davydov Y.A. On distance in total variation between image measures. Statistics & Probability Letters, 2017, vol. 129, pp. 393-400. https://doi.org/10.1016/j.spl.2017.06.022

21. Fortet R., Mourier E. Convergence de la repartition empirique vers la repartition theorique, Ann. Sci. Ecole Norm. Sup., 1953, vol. 70, no. 3, pp. 267-285. https://doi.org/10.24033/asens.1013

22. Kosov E.D. Fractional smoothness of images of logarithmically concave measures under polynomials. J. Math. Anal. Appl., 2018, vol. 462, no. 2, pp. 390-406. https://doi.org/10.1016/j.jmaa.2018.02.016

23. Nourdin I., Nualart D., Poly G. Absolute continuity and convergence of densities for random vectors on Wiener chaos. Electron. J. Probab., 2013, vol. 18, no. 22, pp. 1-19. https://doi.org/10.1214/ejp.v18-2181

24. Nourdin I., Poly G. Convergence in total variation on Wiener chaos. Stochastic Process. Appl., 2013, vol. 123, no. 2, pp. 651-674. https://doi.org/10.1016/j.spa.2012.10.004

25. Stein E. Singular integrals and differentiability properties of functions. Princeton: Princeton University Press, 1970, 287 p.

26. Zelenov G.I. On distances between distributions of polynomials. Theory Stoch. Processes, 2017, vol. 22, no 2, pp. 79-85.


Full text (russian)