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- W2017104501 abstract "To estimate and measure risks, two key classes of dependence relationship must be identified: temporal dependence and contemporaneous dependence. In this paper, we propose a parametric estimation model that uses a three-stage pseudo maximum likelihood estimation (3SPMLE), and we investigate the consistency and asymptotic normality of parametric estimators. The proposed model combines the concept of a copula and the methods of parametric estimators of two-stage pseudo maximum likelihood estimation (2SPMLE). The selection of a copula model that best captures the dependence structure is a critical problem. To solve this problem, we propose a model selection method that is based on the parametric pseudo-likelihood ratio under the 3SPMLE for stationary Markov vector-type models." @default.
- W2017104501 created "2016-06-24" @default.
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- W2017104501 date "2010-06-01" @default.
- W2017104501 modified "2023-10-12" @default.
- W2017104501 title "Statistical properties of parametric estimators for Markov chain vectors based on copula models" @default.
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- W2017104501 doi "https://doi.org/10.1016/j.jspi.2009.12.002" @default.
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