Estimating method comparison and model diagnosis of Copula
碩士 === 國立臺北大學 === 統計學系 === 99 === Copula is a useful tool to build a joint distribution function. When we use a copula to build a joint distribution function, the estimation of the copula parameters is an essential procedure. Cherubini et al. (2004) systematically introduces some estimation methods...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/94269898278729504059 |
| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 99 === Copula is a useful tool to build a joint distribution function. When we use a copula to build a joint distribution function, the estimation of the copula parameters is an essential procedure. Cherubini et al. (2004) systematically introduces some estimation methods in Chapter 5. Among those estimation methods, Kim et al. (2007) uses the simulation studies to compare Maximum likelihood method (ML) and Inference function for margins (IFM) method with Canonical maximum likelihood method (CML). The main conclusion is that the ML and IFM methods are nonrobust against misspecification of marginal distributions, and that the CML method performs better than the ML and IFM methods overall. The functional relationship between Kendall’s tau and copula parameter is another way to estimate copula. If we restrict our attention to Archimedean copula, then the parameter can be estimated more easily in terms of the functional relationship between Kendall’s tau and copula parameter proposed by Genest and Mackay (1986). This method is called G&M method. The purpose of this paper is to compare CML method with G&M method and employ copula in practical applications.
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