Displaying similar documents to “Limit theorems for discrete-time metapopulation models.”

On invertibility of a random coefficient moving average model

Tomáš Marek (2005)

Kybernetika

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A linear moving average model with random coefficients (RCMA) is proposed as more general alternative to usual linear MA models. The basic properties of this model are obtained. Although some model properties are similar to linear case the RCMA model class is too general to find general invertibility conditions. The invertibility of some special examples of RCMA(1) model are investigated in this paper.

Autocovariance structure of powers of switching-regime ARMA processes

Christian Francq, Jean-Michel Zakoïan (2002)

ESAIM: Probability and Statistics

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In Francq and Zakoïan [4], we derived stationarity conditions for ARMA ( p , q ) models subject to Markov switching. In this paper, we show that, under appropriate moment conditions, the powers of the stationary solutions admit weak ARMA representations, which we are able to characterize in terms of p , q , the coefficients of the model in each regime, and the transition probabilities of the Markov chain. These representations are potentially useful for statistical applications.

Kermack-McKendrick epidemic model revisited

Josef Štěpán, Daniel Hlubinka (2007)

Kybernetika

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This paper proposes a stochastic diffusion model for the spread of a susceptible-infective-removed Kermack–McKendric epidemic (M1) in a population which size is a martingale N t that solves the Engelbert–Schmidt stochastic differential equation (). The model is given by the stochastic differential equation (M2) or equivalently by the ordinary differential equation (M3) whose coefficients depend on the size N t . Theorems on a unique strong and weak existence of the solution to (M2) are proved...

Markov chain model of phytoplankton dynamics

Radosław Wieczorek (2010)

International Journal of Applied Mathematics and Computer Science

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A discrete-time stochastic spatial model of plankton dynamics is given. We focus on aggregative behaviour of plankton cells. Our aim is to show the convergence of a microscopic, stochastic model to a macroscopic one, given by an evolution equation. Some numerical simulations are also presented.