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1,094 results on '"Borisov I"'

2. Universal kernel-type estimation of random fields

Author

Linke, Yu. Yu., Borisov, I. S., and Ruzankin, P. S.

Subjects
Mathematics - Statistics Theory, 62G08, G.3
Abstract

Consistent weighted least square estimators are proposed for a wide class of nonparametric regression models with random regression function, where this real-valued random function of $k$ arguments is assumed to be continuous with probability 1. We obtain explicit upper bounds for the rate of uniform convergence in probability of the new estimators to the unobservable random regression function for both fixed or random designs. In contrast to the predecessors' results, the bounds for the convergence are insensitive to the correlation structure of the $k$-variate design points. As an application, we study the problem of estimating the mean and covariance functions of random fields with additive noise under dense data conditions. The theoretical results of the study are illustrated by simulation examples which show that the new estimators are more accurate in some cases than the Nadaraya--Watson ones. An example of processing real data on earthquakes in Japan in 2012--2021 is included., Comment: To appear in Statistics

Published
2023
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15. Construction Materials with Low Power Intensity Based on Cement

Author

Borisov, I. N., Stronin, A. A., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Klyuev, Sergey Vasil'yevich, editor, Klyuev, Alexander Vasil'yevich, editor, and Vatin, Nikolay Ivanovich, editor

Published
2021
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37. Influence of the Chemical Structure of Terminal Groups on the Properties of Poly(phenylene sulfone) Ultrafiltration Membranes

Author

Matveev, D. N., Raeva, A. Yu., Zhansitov, A. A., Shakhmurzova, K. T., Kurdanova, Zh. I., Anokhina, T. S., Khashirova, S. Yu., Volkov, V. V., and Borisov, I. L.

Abstract

Abstract: For the first time, poly(phenylene sulfones) (PPSFs) with chlorine and hydroxyl terminal groups are synthesized and tested for casting high-performance flat-sheet ultrafiltration membranes. The synthesis of PPSFs is carried out in dimethylacetamide at various ratios of 4,4'-dihydroxydiphenyl and 4,4-dichlorodiphenyl sulfone monomers. Two samples with the predominant content of hydroxyl (PPSF-ОН) and chlorine (PPSF-Cl) terminal groups are studied by NMR spectroscopy, GPC, and DSC methods. The coagulation values of polymer solutions in N-methyl-2-pyrrolidone (NMP) and the mechanical properties and hydrophilicity of polymer materials are determined. Both PPSF samples exhibit high tensile strength values at a level of 16 MPa. Using the method of precipitation of PPSF solutions in NMP with PEG-400 additives into water flat-sheet porous asymmetric membranes with a mesoporous (a pore diameter of about 7 nm) thin outer layer and fingerlike macropores in the substrate layer are obtained. An increase in the proportion of hydroxyl terminal groups enhances the hydrophilicity of the polymer. This, in turn, allows for the preparation of flat-sheet membranes from PPSF-ОН with a water permeability of 66 L/(m2h bar), which is 1.5 times higher than the water permeability of the PPSF-Cl membrane. Meanwhile, both membranes demonstrate a Blue Dextran (Mw= 70 000 g mol–1) rejection of 99.9%.

Published
2024
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38. Exponential inequalities for the distribution tails of canonical U- and V-statistics of $\rho$-mixing observations

Author

Borisov, I. S. and Volodko, N.

Subjects
Mathematics - Probability
Abstract

The Hoeffding-type-inequalities are obtained for the distribution tails of canonical (degenerate) U- and V-statistics of an arbitrary order based on samples from a stationary sequence of observations satisfying $\rho$-mixing., Comment: This paper has been withdrawn by the author due to an inaccuracy in the proof

Published
2010

39. Orthogonal series and limit theorems for canonical U- and V-statistics of stationary connected observations

Author

Borisov, I. S. and Volodko, N.

Subjects
Mathematics - Probability
Abstract

The limit behavior is studied for the distributions of normalized U- and V-statistics of an arbitrary order with canonical (degenerate) kernels, based on samples of increasing sizes from a stationary sequence of observations satisfying classical mixing conditions. The corresponding limit distributions are represented as infinite multilinear forms of a centered Gaussian sequence with a known covariance matrix.

Published
2009

40. A note on exponential inequalities for the distribution tails of canonical von Mises' statistics of dependent observations

Author

Borisov, I. S. and Volodko, N.

Subjects
Mathematics - Probability
Abstract

Hoeffding-type exponential inequalities are obtained for the distribution tails of canonical von Mises' statistics of arbitrary order based on samples from a stationary sequence of random variables satisfying the {\varphi}-mixing condition. The present paper weakens the restrictions on the coefficient {\varphi}() which are contained in the paper "Exponential inequalities for the distributions of canonical U- and V-statistics of dependent observations" by Borisov and Volodko (2009). At the same time, we correct the corresponding proof in this paper.

Published
2009

41. UNIVERSAL NONPARAMETRIC KERNEL-TYPE ESTIMATORS FOR THE MEAN AND COVARIANCE FUNCTIONS OF A STOCHASTIC PROCESS.

Author

LINKE, YU. YU. and BORISOV, I. S.

Subjects
*STOCHASTIC processes, *KERNEL functions, *NONPARAMETRIC estimation, *CONTINUOUS processing, *DEPENDENT variables
Abstract

Let f1(t), ..., fn(t) be independent copies of some a.s. continuous stochastic process f(t), t ∈ [0, 1], which are observed with noise. We consider the problem of nonparametric estimation of the mean function µ(t) = Ef(t) and of the covariance function ψ(t, s) = Cov{f(t), f(s)} if the noise values of each of the copies fi(t), i = 1, ..., n, are observed in some collection of generally random (in general) time points (regressors). Under wide assumptions on the time points, we construct uniformly consistent kernel estimators for the mean and covariance functions both in the case of sparse data (where the number of observations for each copy of the stochastic process is uniformly bounded) and in the case of dense data (where the number of observations at each of n series is increasing as n → ∞). In contrast to the previous studies, our kernel estimators are universal with respect to the structure of time points, which can be either fixed rather than necessarily regular, or random rather than necessarily formed of independent or weakly dependent random variables. [ABSTRACT FROM AUTHOR]

Published
2024
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