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Andrey Pilipenko

Publications and source records attributed to Andrey Pilipenko.

At least 37 records · Page 2Linked to original sources

On a skew stable Lévy process

The skew Brownian motion is a strong Markov process which behaves like a Brownian motion until hitting zero and exhibits an asymmetry at zero. We address the following question: what is a natural counterpart of the skew Brownian motion in the situation that the noise is a stable Lévy process with finite mean and infinite variance. We define a skew stable Lévy process $X$ as the limit of a sequence of stable Lévy processes which are perturbed at zero. We point out a formula for the resolvent of $X$ and show that $X$ is a solution to a stochastic differential equation with a local time. Also, we provide a representation of $X$ in terms of Itô`s excursion theory.

math.PR↗

On a limit behaviour of a random walk penalised in the lower half-plane

We consider a random walk $\tilde S$ which has different increment distributions in positive and negative half-planes. In the upper half-plane the increments are mean-zero i.i.d. with finite variance. In the lower half-plane we consider two cases: increments are positive i.i.d. random variables with either a slowly varying tail or with a finite expectation. For the distributions with a slowly varying tails, we show that $\{\frac{1}{\sqrt n} \tilde S(nt)\}$ has no weak limit in $\De$; alternatively, the weak limit is a reflected Brownian motion.

math.PR↗

On regularization by a small noise of multidimensional ODEs with non-Lipschitz coefficients

In this paper we solve a selection problem for multidimensional SDE $d X^\varepsilon(t)=a(X^\varepsilon(t)) d t+\varepsilon σ(X^\varepsilon(t))\, d W(t)$, where the drift and diffusion are locally Lipschitz continuous outside of a fixed hyperplane $H$. It is assumed that $X^\varepsilon(0)=x^0\in H$, the drift $a(x)$ has a Hoelder asymptotics as $x$ approaches $H$, and the limit ODE $d X(t)=a(X(t))\, d t$ does not have a unique solution. We show that if the drift pushes the solution away of $H$, then the limit process with certain probabilities selects some extreme solutions to the limit ODE. If the drift attracts the solution to $H$, then the limit process satisfies an ODE with some averaged coefficients. To prove the last result we formulate an averaging principle, which is quite general and new.

math.PR↗

Generalized selection problem with Lévy noise

Let $A_\pm>0$, $β\in(0,1)$, and let $Z^{(α)}$ be a strictly $α$-stable Lévy process with the jump measure $ν(\mathrm{d} z)=(C_+\mathbb{I}_{(0,\infty)}(z)+ C_-\mathbb{I}_{(-\infty,0)}(z))|z|^{-1-α}\,\mathrm{d} z$, $α\in (1,2)$, $C_\pm\geq 0$, $C_++C_->0$. The selection problem for the model stochastic differential equation $\mathrm{d} \bar X^\varepsilon=(A_+\mathbb{I}_{[0,\infty)}(\bar X^\varepsilon) - A_-\mathbb{I}_{(-\infty,0)}(\bar X^\varepsilon))|\bar X^\varepsilon|^β\,\mathrm{d} t +\varepsilon \mathrm{d} Z^{(α)}$ states that in the small noise limit $\varepsilon\to 0$, solutions $\bar X^\varepsilon$ converge weakly to the maximal or minimal solutions of the limiting non-Lipschitzian ordinary differential equation $\mathrm{d} \bar x=(A_+\mathbb{I}_{[0,\infty)}(\bar x)- A_-\mathbb{I}_{(\infty,0)}(\bar x))|\bar x|^β\,\mathrm{d} t$ with probabilities $\bar p_\pm=\bar p_\pm(α,C_+/C_-,β, A_+/A_-)$, see [Pilipenko and Proske, Stat. Probab. Lett., 132:62-73, 2018]. In this paper we solve the generalized selection problem for the stochastic differential equation $\mathrm{d} X^\varepsilon=a(X^\varepsilon)\,\mathrm{d} t+\varepsilon b(X^\varepsilon)\,\mathrm{d} Z$ whose dynamics in the vicinity of the origin in certain sense reminds of dynamics of the model equation. In particular we show that solutions $X^\varepsilon$ also converge to the maximal or minimal solutions of the limiting irregular ordinary differential equation $\mathrm{d} x=a(x) \,\mathrm{d} t$ with the same model selection probabilities $\bar p_\pm$. This means that for a large class of irregular stochastic differential equations, the selection dynamics is completely determined by four local parameters of the drift and the jump measure.

math.PR↗

On exponential decay of a distance between solutions of an SDE with non-regular drift

We consider a multidimensional stochastic differential equation with a Gaussian noise and a drift vector having a jump discontinuity along a hyperplane. The large time behavior of the distance between two solutions starting from different points is studied.We consider a multidimensional stochastic differential equation with a Gaussian noise and a drift vector having a jump discontinuity along a hyperplane. The large time behavior of the distance between two solutions starting from different points is studied.

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Strong solutions of SDE's with generalized drift and multidimensional fractional Brownian initial noise

In this paper we prove the existence of strong solutions to a SDE with a generalized drift driven by a multidimensional fractional Brownian motion for small Hurst parameters H<1/2. Here the generalized drift is given as the local time of the unknown solution process, which can be considered an extension of the concept of a skew Brownian motion to the case of fractional Brownian motion. Our approach for the construction of strong solutions is new and relies on techniques from Malliavin calculus combined with a "local time variational calculus" argument.

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The quasi-optimality criterion in the linear functional strategy

The linear functional strategy for the regularization of inverse problems is considered. For selecting the regularization parameter therein, we propose the heuristic quasi-optimality principle and some modifications including the smoothness of the linear functionals. We prove convergence rates for the linear functional strategy with these heuristic rules taking into account the smoothness of the solution and the functionals and imposing a structural condition on the noise. Furthermore, we study these noise conditions in both a deterministic and stochastic setup and verify that for mildly-ill-posed problems and Gaussian noise, these conditions are satisfied almost surely, where on the contrary, in the severely-ill-posed case and in a similar setup, the corresponding noise condition fails to hold. Moreover, we propose an aggregation method for adaptively optimizing the parameter choice rule by making use of improved rates for linear functionals. Numerical results indicate that this method yields better results than the standard heuristic rule.

math.NA↗

On perturbations of an ODE with non-Lipschitz coefficients by a small self-similar noise

We study the limit behavior of differential equations with non-Lipschitz coefficients that are perturbed by a small self-similar noise. It is proved that the limiting process is equal to the maximal solution or minimal solution with certain probabilities $p_+$ and $p_-=1-p_+$, respectively. We propose a space-time transformation that reduces the investigation of the original problem to the study of the exact growth rate of a solution to a certain SDE with self-similar noise. This problem is interesting in itself. Moreover, the probabilities $p_+$ and $p_-$ coincide with probabilities that the solution of the transformed equation converges to $+\infty$ or $-\infty$ as $t\to\infty,$ respectively.

math.PR↗

On a Brownian motion with a hard membrane

Local perturbations of a Brownian motion are considered. As a limit we obtain a non-Markov process that behaves as a reflected Brownian motion on the positive half line until its local time at zero reaches some exponential level, then changes a sign and behaves as a reflected Brownian motion on the negative half line until some stopping time, etc.

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A limit theorem for singular stochastic differential equations

We study the weak limits of solutions to SDEs \[dX_n(t)=a_n\bigl(X_n(t)\bigr)\,dt+dW(t),\] where the sequence $\{a_n\}$ converges in some sense to $(c_- 1\mkern-4.5mu\mathrm{l}_{x<0}+c_+ 1\mkern-4.5mu\mathrm{l}_{x>0})/x+γδ_0$. Here $δ_0$ is the Dirac delta function concentrated at zero. A limit of $\{X_n\}$ may be a Bessel process, a skew Bessel process, or a mixture of Bessel processes.

math.PR↗

A functional limit theorem for for excited random walks

We consider the limit behavior of an excited random walk (ERW), i.e., a random walk whose transition probabilities depend on the number of times the walk has visited to the current state. We prove that an ERW being naturally scaled converges in distribution to an excited Brownian motion that satisfies an SDE, where the drift of the unknown process depends on its local time. Similar result was obtained by Raimond and Schapira, their proof was based on the Ray-Knight type theorems. We propose a new method of investigations based on a study of the Radon-Nikodym density of the ERW distribution with respect to the distribution of a symmetric random walk.

math.PR↗

On a limit behavior of a random walk with modifications at zero

We consider the limit behavior of a one-dimensional random walk with unit jumps whose transition probabilities are modified every time the walk hits zero. The invariance principle is proved in the scheme of series where the size of modifications depends on the number of series. For the natural scaling of time and space arguments the limit process is (i) a Brownian motion if modifications are "small", (ii) a linear motion with a random slope if modifications are "large", and (iii) the limit process satisfies an SDE with a local time of unknown process in a drift if modifications are "moderate".

math.PR↗

Functional limit theorems for the maxima of perturbed random walks and divergent perpetuities in the $M_1$-topology

Let $(ξ_1,η_1)$, $(ξ_2,η_2),\ldots$ be a sequence of i.i.d. two-dimensional random vectors. In the earlier article Iksanov and Pilipenko (2014) weak convergence in the $J_1$-topology on the Skorokhod space of $n^{-1/2}\underset{0\leq k\leq \cdot}{\max}\,(ξ_1+\ldots+ξ_k+η_{k+1})$ was proved under the assumption that contributions of $\underset{0\leq k\leq n}{\max}\,(ξ_1+\ldots+ξ_k)$ and $\underset{1\leq k\leq n}{\max}\,η_k$ to the limit are comparable and that $n^{-1/2}(ξ_1+\ldots+ξ_{[n\cdot]})$ is attracted to a Brownian motion. In the present paper, we continue this line of research and investigate a more complicated situation when $ξ_1+\ldots+ξ_{[n\cdot]}$, properly normalized without centering, is attracted to a centered stable Lévy process, a process with jumps. As a consequence, weak convergence normally holds in the $M_1$-topology. We also provide sufficient conditions for the $J_1$-convergence. For completeness, less interesting situations are discussed when one of the sequences $\underset{0\leq k\leq n}{\max}\,(ξ_1+\ldots+ξ_k)$ and $\underset{1\leq k\leq n}{\max}\,η_k$ dominates the other. An application of our main results to divergent perpetuities with positive entries is given.

math.PR↗

A representation for the derivative with respect to the initial data of the solution of an SDE with a non-regular drift and a Gaussian noise

We consider a multidimensional SDE with a Gaussian noise and a drift vector being a vector function of bounded variation. We prove the existence of generalized derivative of the solution with respect to the initial conditions and represent the derivative as a solution of a linear SDE with coefficients depending on the initial process. The representation obtained is a natural generalization of the expression for the derivative in the smooth case. The theory of continuous additive functionals is used.

math.PR↗

On a Selection Problem for Small Noise Perturbation in Multidimensional Case

The problem on identification of a limit of an ordinary differential equation with discontinuous drift that perturbed by a zero-noise is considered in multidimensional case. This problem is a classical subject of stochastic analysis. However the multidimensional case was poorly investigated. We assume that the drift coefficient has a jump discontinuity along a hyperplane and is Lipschitz continuous in the upper and lower half-spaces. It appears that the behavior of the limit process depends on signs of the normal component of the drift at the upper and lower half-spaces in a neighborhood of the hyperplane, all cases are considered.

math.PR↗

On a limit behavior of a sequence of Markov processes perturbed in a neighborhood of a singular point

We study a limit behavior of a sequence of Markov processes (or Markov chains) such that their distributions outside of any neighborhood of a "singular" point attract to some probability law. In any neighborhood of this point the behavior may be irregular. As an example of the general result we consider a symmetric random walk with the unit jump that is perturbed in a neighborhood of 0. The invariance principle is obtained under standard scaling of time and space. The limit process turns out to be a skew Brownian motion.

math.PR↗

A functional limit theorem for locally perturbed random walks

A particle moves randomly over the integer points of the real line. Jumps of the particle outside the membrane (a fixed "locally perturbating set") are i.i.d., have zero mean and finite variance, whereas jumps of the particle from the membrane have other distributions with finite means which may be different for different points of the membrane; furthermore, these jumps are mutually independent and independent of the jumps outside the membrane. Assuming that the particle cannot jump over the membrane we prove that the weak scaling limit of the particle position is a skew Brownian motion with parameter $γ\in [-1,1]$. The path of a skew Brownian motion is obtained by taking each excursion of a reflected Brownian motion, independently of the others, positive with probability $2^{-1}(1+γ)$ and negative with probability $2^{-1}(1-γ)$. To prove the weak convergence result we offer a new approach which is based on the martingale characterization of a skew Brownian motion. Among others, this enables us to provide the explicit formula for the parameter $γ$. In the previous articles the explicit formulae for the parameter have only been obtained under the assumption that outside the membrane the particle performs unit jumps.

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