papers

Publications (26)

math.PR2018

Populations with interaction and environmental dependence: from few, (almost) independent, members into deterministic evolution of high densities

P. Chigansky, P. Jagers, F. C. Klebaner

Many populations, e.g. of cells, bacteria, viruses, or replicating DNA molecules, start small, from a few individuals, and grow large into a noticeable fraction of the environmenta…

math.PR2008

Distribution of the Brownian motion on its way to hitting zero

P. Chigansky, F. C. Klebaner

For the one-dimensional Brownian motion , started at , and the first hitting time , we find the probability density of for…

math.PR2024

An approximation of populations on a habitat with large carrying capacity

N. Bauman, P. Chigansky, F. Klebaner

We consider stochastic dynamics of a population which starts from a small colony on a habitat with large but limited carrying capacity. A common heuristics suggests that such popul…

math.PR2002

On exponential stability of Wonham filter

P. Chigansky, R. Liptser

We give elementary proof of a stability result concerning an exponential asymptotic () for filtering estimates generated by wrongly initialized Wonham filter. This proo…

math.PR2020

On the establishment of a mutant

J. Baker, P. Chigansky, P. Jagers +1

How long does it take for an initially advantageous mutant to establish itself in a resident population, and what does the population composition look like then? We approach these…

math.PR2006

On a role of predictor in the filtering stability

P. Chigansky, R. Liptser

When is a nonlinear filter stable with respect to its initial condition? In spite of the recent progress, this question still lacks a complete answer in general. Currently availabl…

math.PR2006

Stability of the nonlinear filter for slowly switching Markov chains

P. Chigansky

Exponential stability of the nonlinear filtering equation is revisited, when the signal is a finite state Markov chain. An asymptotic upper bound for the filtering error due to inc…

math.ST2013

Estimation in threshold autoregressive models with correlated innovations

P. Chigansky, Y. Kutoyants

Large sample statistical analysis of threshold autoregressive (TAR) models is usually based on the assumption that the underlying driving noise is uncorrelated. In this paper, we c…

math.ST2026

Asymptotic analysis of the finite predictor for fractional Gaussian noise

P. Chigansky, M. Kleptsyna

This paper proposes a new approach to the asymptotic analysis of the finite predictor for stationary sequences. Our method yields the exact asymptotics of both the relative predict…

math.PR2016

On the emergence of random initial conditions in fluid limits

A. D. Barbour, P. Chigansky, F. C. Klebaner

The paper presents a phenomenon occurring in population processes that start near zero and have large carrying capacity. By the classical result of Kurtz~(1970), such processes, no…

math.PR2021

Sharp asymptotics in a fractional Sturm-Liouville problem

P. Chigansky, M. Kleptsyna

The current research of fractional Sturm-Liouville boundary value problems focuses on the qualitative theory and numerical methods, and much progress has been recently achieved in…

math.PR2019

Mixed fractional Brownian motion: a spectral take

P. Chigansky, M. Kleptsyna, D. Marushkevych

This paper provides yet another look at the mixed fractional Brownian motion (fBm), this time, from the spectral perspective. We derive an approximation for the eigenvalues of its…

math.PR2018

Exact spectral asymptotics of fractional processes

P. Chigansky, M. Kleptsyna, D. Marushkevych

Eigenproblems frequently arise in theory and applications of stochastic processes, but only a few have explicit solutions. Those which do, are usually solved by reduction to the ge…

math.PR2022

Linear filtering with fractional noises: large time and small noise asymptotics

D. Afterman, P. Chigansky, M. Kleptsyna +1

The classical state-space approach to optimal estimation of stochastic processes is efficient when the driving noises are generated by martingales. In particular, the weight functi…

math.PR2006

On filtering of Markov chains in strong noise

P. Chigansky

The filtering problem for finite state Markov chains is revisited, when the intensity of the observation noise increases. We give a description of conditional measure concentration…

math.PR2019

Persistence of Small Noise and Random initial conditions

J. Baker, P. Chigansky, K. Hamza +1

The effect of small noise in a smooth dynamical system is negligible on any finite time interval. Here we study situations when it persists on intervals increasing to infinity. Suc…

math.ST2022

Estimation of the Hurst parameter from continuous noisy data

P. Chigansky, M. Kleptsyna

This paper addresses the problem of estimating the Hurst exponent of the fractional Brownian motion from continuous time noisy sample. Consistent estimation in the setup under cons…

math.PR2006

The Freidlin-Wentzell LDP with rapidly growing coefficients

P. Chigansky, R. Liptser

The Large Deviations Principle (LDP) is verified for a homogeneous diffusion process with respect to a Brownian motion , $$ X^\eps_t=x_0+\int_0^tb(X^\eps_s)ds+ \eps\int_0^tσ(…

math.ST2022

Asymptotic accuracy in estimation of a fractional signal in a small white noise

M. Kleptsyna, D. Marushkevych, P. Chigansky

This paper revisits the problem of estimating the fractional Ornstein - Uhlenbeck process observed in a linear channel with white noise of small intensity. We drive the exact asymp…

math.PR2012

Compound Poisson approximation for triangular arrays with application to threshold estimation

P. Chigansky, F. C. Klebaner

We prove weak convergence of triangular arrays to the compound Poisson limit using Tikhomirov's method. The result is applied to statistical estimation of the threshold parameter i…

math.PR2007

Large deviations for a scalar diffusion in random environment

P. Chigansky, R. Liptser

Let , be an ergodic stationary Markov chain, taking a finite number of values , and , where is a bounded and measurable fun…

math.PR2026

Multitype PCR branching processes

P. Chigansky, F. Klebaner, M. Mrksa +1

To model amplification Polymerase Chain Reaction (PCR) techniques targeting DNA sequences of several types, we introduce a multitype PCR branching process as a generalized version…

math.PR2020

On the eigenproblem for Gaussian bridges

P. Chigansky, M. Kleptsyna, D. Marushkevych

Spectral decomposition of the covariance operator is one of the main building blocks in the theory and applications of Gaussian processes. Unfortunately it is notoriously hard to d…

math.PR2006

An ergodic theorem for filtering with applications to stability

P. Chigansky

Ergodic properties of the signal-filtering pair are studied for continuous time finite Markov chains, observed in white noise. The obtained law of large numbers is applied to the s…

math.PR2005

Asymptotic stability of the Wonham filter for ergodic and nonergodic signals

P. Baxendale, P. Chigansky, R. Liptser

Stability problem of the Wonham filter with respect to initial conditions is addressed. The case of ergodic signals is revisited in view of a gap in the classic work of H. Kunita (…

math.PR2024

Asymptotic analysis in problems with fractional processes

P. Chigansky, M. Kleptsyna

Some problems in the theory and applications of stochastic processes can be reduced to solving integral equations. While explicit solutions for these equations are often elusive, v…