Limit theorems for discrete-time metapopulation models
By F. Buckley, P. Pollett. 2010
48 citations recorded by the source corpus.
We describe a class of one-dimensional chain binomial models of use in studying metapopulations (population networks). Limit theorems are established for time-inhomogeneous Markov chains that share the salient features of these models. We prove a law of large numbers, which can be used to identify an approximating deterministic trajectory, and a central limit theorem, which establishes that the scaled fluctuations about this trajectory have an approximating autoregressive structure.
Subfields: Mathematical Physics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/17bdb2f78ec4845349910282d884a765bed63336
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In Mathematical Physics
In Statistical and Nonlinear Physics
In Statistics and Probability
Citations in the corpus
- BINOMIAL AUTOREGRESSIVE PROCESSES WITH DENSITY‐DEPENDENT THINNING (2014, unknown): “Since 0 XNt 1, we may apply Theorem 1 of Buckley and Pollett (2010a) to obtain the following law of large numbers.”
- Binomial AR(1) processes: moments, cumulants, and estimation (2013, methodology): “Further fields of application are described, for example, by Buckley and Pollett 12 , where the binomial AR(1) model is used for describing the dynamics of certain types of metapopulation models, and by Cui and Lund 8 , where superpositioned renewal processes are considered.”