In this study, we introduce a mixed copula-based vector autoregressive (VAR) model for investigating the relationship between random variables. The one-step maximum likelihood estimation is used to obtain point estimates of parameters and copula parameters. More specifically, combine likelihoods marginal construct full function. simulation study confirm accuracy as well reliability proposed mod...

Copulas enable to specifymultivariate distributions with givenmarginals.Various parametric proposals weremade in the literature for these quantities, mainly in the bivariate case. They can be systematically derived from multivariate distributions with known marginals, yielding e.g. the normal and the Student copulas. Alternatively, one can restrict his/her interest to a sub-family of copulas na...

A new Archimedean copula family is presented that was derived from the SAHARA utility function introduced in economic literature 2011. Its properties are discussed, and its flexibility versatility demonstrated. It left tail decreasing or right increasing, but unlike mainstream families, not necessarily stochastically increasing at same time. shown fits very well to a dataset of previously studi...

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We review the main “omnibus procedures” for goodness-of-fit testing for copulas: tests based on the empirical copula process, on probability integral transformations, on Kendall’s dependence function, etc, and some corresponding reductions of dimension techniques. The problems of finding asymptotic distribution-free test statistics and the calculation of reliable p-values are discussed. Some pa...

Rivest & Wells (2001) proposed estimators of the marginal survival functions in a right-censored model that assumes an Archimedean copula between the survival time and the censoring time. We study the extension of these estimators to the context of rightcensored semi-competing risks data with an independent second level censoring time. We intensively use martingale techniques to derive their la...

In this paper, we investigate the problem of linear joint probabilistic constraints. We assume that the rows of the constraint matrix are dependent and the dependence is driven by a convenient Archimedean copula. Further we assume the distribution of the constraint rows to be elliptically distributed, covering normal, t, or Laplace distributions. Under these and some additional conditions, we p...