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- W2056200530 abstract "Conditional probability distributions have been commonly used in modeling Markov chains. In this paper we consider an alternative approach based on copulas to investigate Markov-type dependence structures. Based on the realization of a single Markov chain, we estimate the parameters using one- and two-stage estimation procedures. We derive asymptotic properties of the marginal and copula parameter estimators and compare performance of the estimation procedures based on Monte Carlo simulations. At low and moderate dependence structures the two-stage estimation has comparable performance as the maximum likelihood estimation. In addition we propose a parametric pseudo-likelihood ratio test for copula model selection under the two-stage procedure. We apply the proposed methods to an environmental data set." @default.
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- W2056200530 date "2008-04-01" @default.
- W2056200530 modified "2023-10-03" @default.
- W2056200530 title "Modeling statistical dependence of Markov chains via copula models" @default.
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- W2056200530 doi "https://doi.org/10.1016/j.jspi.2007.04.028" @default.
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