Aristidis K. Nikoloulopoulos

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BACKGROUND Particulate air pollution is associated with increased mortality. There is a need for European results from multicountry databases concerning cause-specific mortality to obtain more accurate effect estimates. METHODS We report the estimated effects of ambient particle concentrations (black smoke and particulate matter less than 10 mum [PM10])(More)
Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its(More)
In Aas et al. (2009) and Aas and Berg (2009), it is shown that vine copulas constructed from bivariate t-copulas can provide better fits to multivariate financial asset return data. Several published articles indicate that for several assets there might be stronger tail dependence of returns in the joint lower tail than upper tail. We use vine copula models(More)
Copulas are used to model multivariate data as they account for the dependence structure and provide a flexible representation of the multivariate distribution. A great number of copulas has been proposed with various dependence aspects. One important issue is the choice of an appropriate copula from a large set of candidate families to model the data at(More)
Applications of copulas for multivariate continuous data abound but there are only a few that treat multivariate binary data. In the present paper, we model multivariate binary data based on copulas using mixtures of max-infinitely divisible copulas, introduced by Joe and Hu (J. Multivar. Anal. 1996; 57(2): 240-265). When applying copulas to binary data the(More)
Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized(More)
There are copula-based statistical models in the literature for regression with dependent data such as clustered and longitudinal overdispersed counts, for which parameter estimation and inference are straightforward. For situations where the main interest is in the regression and other univariate parameters and not the dependence, we propose a "weighted(More)
Diagnostic test accuracy studies typically report the number of true positives, false positives, true negatives and false negatives. There usually exists a negative association between the number of true positives and true negatives, because studies that adopt less stringent criterion for declaring a test positive invoke higher sensitivities and lower(More)