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- W2068156653 abstract "Consider the problem of estimating parameter(s) of a copula which provides joint distribution for X1, X2, ⋅⋅⋅, Xp. This article employs concept of the generalized linear model (glm) to estimate parameter(s) of a given copula. More precisely, it considers marginal cumulative distributions as covariate information about Then, it estimates copula’s parameter(s) by minimizing mean-squared distance between and conditional expectation Several properties of this new approach, say GLM-method, have been explored. A simulation study has been conducted to make a comparison among GLM-method, Kendal’s tau, Spearman’s rho, the pml, and Copula-quantile regression. Based upon such simulation study, one may conjecture that for the multivariate elliptical distributions (including normal, t-student, etc.) the GLM-method provides an appropriate result, in the sense of Cramer-von Mises distance, compared to other nonparametric estimation methods." @default.
- W2068156653 created "2016-06-24" @default.
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- W2068156653 date "2014-12-10" @default.
- W2068156653 modified "2023-09-26" @default.
- W2068156653 title "A GLM Approach to Estimating Copula Models" @default.
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- W2068156653 doi "https://doi.org/10.1080/03610918.2013.824588" @default.
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