Titlebook: Analysis of Survival Data with Dependent Censoring; Copula-Based Approac Takeshi Emura,Yi-Hau Chen Book 2018 The Author(s) 2018 Competing R

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Analysis of Survival Data with Dependent Censoring978-981-10-7164-5Series ISSN 2191-544X Series E-ISSN 2191-5458

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https://doi.org/10.1007/978-3-322-89517-2tric likelihood methods, and semi-parametric likelihood methods developed under a variety of copula models. All these approaches employ an ., a copula function that is completely specified including its parameter value to avoid the non-identifiability.

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https://doi.org/10.1007/978-3-322-89517-2er reviewing the concept of copulas, we introduce measures of dependence, including Kendall’s tau and the cross-ratio function. We also introduce the idea of . that explains how dependence between event times arises and how it can be modeled by copulas. Finally, we apply copulas for modeling the eff

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https://doi.org/10.1007/978-3-322-89517-2tric likelihood methods, and semi-parametric likelihood methods developed under a variety of copula models. All these approaches employ an ., a copula function that is completely specified including its parameter value to avoid the non-identifiability.

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Analysis of Survival Data with Dependent CensoringCopula-Based Approac

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