Copula is a convenient method that manufactures multivariate distributions with specified desired marginaldistributions. One can hence utilize copula for likelihood inference. However, one rarely pays attention to theproperty of robustness of copula. That is, how sensitive is the validity of inference derived from copulamodels when, in fact, data distributions do not conform to the model assumption? Can copula be made robustwhen model fails?There are several univariate distributions, including gamma, Poisson, normal, negative binomial andbinomials that can be robustified under model misspecification. The objectives of this research projectincludes1. Which, if any, copula models are more robust?2. Copula models with gamma, Poisson, normal, negative binomial and binomials as marginals,can be robustified as the marginals?Or3. The nice property of being robustifiable for these univariate distributions is destroyed bycopula?Meanwhile, the copula technique falls short on the analysis of data of mixing types, such as correlated(count, continuous), (nominal, continuous), (count, nominal) data. This is certainly due to the theoreticaldifficulty for copula to incorporate correlated mixing data. We would also like to develop robustlikelihood methods and make contrasts between copula models and several robust likelihood approaches interms of validity and precision.
|Effective start/end date||1/08/18 → 31/07/19|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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