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Valentin Konakov

Publications and source records attributed to Valentin Konakov.

15 recordsLinked to original sources
arXiv2025

Random walks on rank one symmetric spaces of noncompact type

We establish a central limit theorem, a local limit theorem, and a law of large numbers for a natural random walk on a symmetric space $M$ of non-compact type and rank one. This class of spaces, which includes the complex and quaternionic hyperbolic spaces and the Cayley hyperbolic plane, generalizes the real hyperbolic space $\mathbb{H}^{n}$. Our approach introduces a unified algebraic framework that generalizes the M\"obius addition, previously used for the constant curvature case, to define the random walk via a non-Euclidean summation of variables. We demonstrate that the renormalized walk converges to the heat kernel associated with the Laplace-Beltrami operator on $M$, which plays the role of the limiting normal law. The proofs leverage the harmonic analysis of spherical functions on symmetric spaces. To the best of our knowledge, these results are new in the context of rank one symmetric spaces.

math.PR↗
arXiv2023

Local Limit Theorems and Strong Approximations for Robbins-Monro Procedures

The Robbins-Monro algorithm is a recursive, simulation-based stochastic procedure to approximate the zeros of a function that can be written as an expectation. It is known that under some technical assumptions, Gaussian limit distributions approximate the stochastic performance of the algorithm. Here, we are interested in strong approximations for Robbins-Monro procedures. The main tool for getting them are local limit theorems, that is, studying the convergence of the density of the algorithm. The analysis relies on a version of parametrix techniques for Markov chains converging to diffusions. The main difficulty that arises here is the fact that the drift is unbounded.

math.PR↗
arXiv2021

$L_1$ and $L_{\infty}$ stability of transition densities of perturbed diffusions

In this paper, we derive a stability result for $L_1$ and $L_{\infty}$ perturbations of diffusions under weak regularity conditions on the coefficients. In particular, the drift terms we consider can be unbounded with at most linear growth, and we do not require uniform convergence of perturbed diffusions. Instead, we require a weaker convergence condition in a special metric introduced in this paper, related to the Holder norm of the diffusion matrix differences. Our approach is based on a special version of the McKean-Singer parametrix expansion.

math.PR↗
arXiv2020

Extremes of Gaussian non-stationary processes and maximal deviation of projection density estimates

In this paper, we consider the distribution of the supremum of non-stationary Gaussian processes, and present a new theoretical result on the asymptotic behaviour of this distribution. Unlike previously known facts in this field, our main theorem yields the asymptotic representation of the corresponding distribution function with exponentially decaying remainder term. This result can be efficiently used for studying the projection density estimates, based, for instance, on Legendre polynomials. More precisely, we construct the sequence of accompanying laws, which approximates the distribution of maximal deviation of the considered estimates with polynomial rate. Moreover, we construct the confidence bands for densities, which are honest at polynomial rate to a broad class of densities.

math.PR↗
arXiv2019

Well-posedness of some non-linear stable driven SDEs

We prove the well-posedness of some non-linear stochastic differential equations in the sense of McKean-Vlasov driven by non-degenerate symmetric $\alpha$-stable L\'evy processes with values in $R^d$ under some mild H{\"o}lder regularity assumptions on the drift and diffusion coefficients with respect to both space and measure variables. The methodology developed here allows to consider unbounded drift terms even in the so-called super-critical case, i.e. when the stability index $\alpha \in (0, 1)$. New strong well-posedness results are also derived from the previous analysis.

math.AP↗
arXiv2019

Convergence of some classes of random flights in Wasserstein distance

In this paper we consider a random walk of a particle in $\mathbb{R}^d$. Convergence of different transformations of trajectories of random flights with Poisson switching moments has been obtained by Davydov and Konakov, as well as diffusion approximation of the process has been built. The goal of this paper is to prove stronger convergence in terms of the Wasserstein distance. Three types of transformations are considered: cases of exponential and super-exponential growth of a switching moment transformation function are quite simple, and the result follows from the fact that the limit processes belong to the unit ball. In the case of the power growth the estimation is more complicated and follows from combinatorial reasoning and properties of the Wasserstein metric.

math.PR↗
arXiv2016

Random walks in non homogeneous Poissonian environment

We consider the moving particle process in Rd which is defined in the following way. There are two independent sequences (Tk) and (dk) of random variables. The variables Tk are non negative and form an increasing sequence, while variables dk form an i.i.d sequence with common distribution concentrated on the unit sphere. The values dk are interpreted as the directions, and Tk as the moments of change of directions. A particle starts from zero and moves in the direction d1 up to the moment T1 . It then changes direction to d2 and moves on within the time interval T2 minus T1 , etc. The speed is constant at all sites. The position of the particle at time t is denoted by X(t). We suppose that the points (Tk) form a non homogeneous Poisson point process and we are interested in the global behavior of the process (X(t)), namely, we are looking for conditions under which the processes (Y(T,t), T is non negative), Y(T,t) is X(tT) normalized by B(T), t in (0, 1), weakly converges in C(0, 1) to some process Y when T tends to infinity. In the second part of the paper the process X(t) is considered as a Markov chain. We construct diffusion approximations for this process and investigate their accuracy. The main tool in this part is the paramertix method.

math.PR↗
arXiv2015

Limits of Kalman Filter application in heavy tailed problems

In this paper we consider the behavior of Kalman Filter state estimates in the case of distribution with heavy tails .The simulated linear state space models with Gaussian measurement noises were used. Gaussian noises in state equation are replaced by components with alpha-stable distribution with di erent parameters alpha and beta. We consider the case when "all parameters are known" and two methods of parameters estimation are compared: the maximum likelihood estimator (MLE) and the expectation- maximization algorithm (EM). It was shown that in cases of large deviation from Gaussian distribution the total error of states estimation rises dramatically. We conjecture that it can be explained by underestimation of the state equation noises covariance matrix that can be taken into account through the EM parameters estimation and ignored in the case of ML estimation.

math.ST↗
arXiv2014

Convergence rates of maximal deviation distribution for projection estimates of L\'evy densities

In this paper, we consider projection estimates for L\'evy densities in high-frequency setup. We give a unified treatment for different sets of basis functions and focus on the asymptotic properties of the maximal deviation distribution for these estimates. Our results are based on the idea to reformulate the problems in terms of Gaussian processes of some special type and to further analyze these Gaussian processes. In particular, we construct a sequence of excursion sets, which guarantees the convergence of the deviation distribution to the Gumbel distribution. We show that the rates of convergence presented in previous articles on this topic are logarithmic and construct the sequences of accompanying laws, which approximate the deviation distribution with polynomial rate.

math.PR↗
arXiv2014

Statistical convergence of Markov experiments to diffusion limits

Assume that one observes the $k$th, $2k$th$,\ldots,nk$th value of a Markov chain $X_{1,h},\ldots,X_{nk,h}$. That means we assume that a high frequency Markov chain runs in the background on a very fine time grid but that it is only observed on a coarser grid. This asymptotics reflects a set up occurring in the high frequency statistical analysis for financial data where diffusion approximations are used only for coarser time scales. In this paper, we show that under appropriate conditions the L$_1$-distance between the joint distribution of the Markov chain and the distribution of the discretized diffusion limit converges to zero. The result implies that the LeCam deficiency distance between the statistical Markov experiment and its diffusion limit converges to zero. This result can be applied to Euler approximations for the joint distribution of diffusions observed at points $Δ,2Δ,\ldots,nΔ$. The joint distribution can be approximated by generating Euler approximations at the points $Δk^{-1},2Δk^{-1},\ldots,nΔ$. Our result implies that under our regularity conditions the Euler approximation is consistent for $n\to\infty$ if $nk^{-2}\to0$.

math.ST↗
arXiv2010

Weak Error for stable driven SDEs: expansion of the densities

Consider a multidimensional SDE of the form $X_t = x+\int_{0}^{t} b(X_{s-})ds+\int{0}^{t} f(X_{s-})dZ_s$ where $(Z_s)_{s\ge 0}$ is a symmetric stable process. Under suitable assumptions on the coefficients the unique strong solution of the above equation admits a density w.r.t. the Lebesgue measure and so does its Euler scheme. Using a parametrix approach, we derive an error expansion at order 1 w.r.t. the time step for the difference of these densities.

math.PR↗
arXiv2009

Explicit parametrix and local limit theorems for some degenerate diffusion processes

For a class of degenerate diffusion processes of rank 2, i.e. when only Poisson brackets of order one are needed to span the whole space, we obtain a parametrix representation of the density from which we derive some explicit Gaussian controls that characterize the additional singularity induced by the degeneracy. We then give a local limit theorem with the usual convergence rate for an associated Markov chain approximation. The key point is that the "weak" degeneracy allows to exploit the techniques first introduced by Konakov and Molchanov and then developed by Konakov and Mammen that rely on Gaussian approximations.

math.PR↗
arXiv2007

Small time Edgeworth-type expansions for weakly convergent nonhomogeneous Markov chains

We consider triangular arrays of Markov chains that converge weakly to a diffusion process. Second order Edgeworth type expansions for transition densities are proved. The paper differs from recent results in two respects. We allow nonhomogeneous diffusion limits and we treat transition densities with time lag converging to zero. Small time asymptotics are motivated by statistical applications and by resulting approximations for the joint density of diffusion values at an increasing grid of points.

math.ST↗
arXiv2006

Accuracy of Diffusion Approximations for High Frequency Markov Data

We consider triangular arrays of Markov chains that converge weakly to a diffusion process. Edgeworth type expansions of third order for transition densities are proved. This is done for time horizons that converge to 0. For this purpose we represent the transition density as a functional of densities of sums of i.i.d. variables. This will be done by application of the parametrix method. Then we apply Edgeworth expansions to the densities. The resulting series gives our Edgeworth-type expansion for the transition density of Markov chains. The research is motivated by applications to high frequency data that are available on a very fine grid but are approximated by a diffusion model on a more rough grid.

math.PR↗