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The extremal dependence behavior of t copulas is examined and their extreme value limiting copulas, called the t-EV copulas, are derived explicitly using the tail dependence functions. As two special cases, the Hüsler-Reiss and the Marshall-Olkin distributions emerge as limits of the t-EV copula as the degrees of freedom go to infinity and zero… (More)

- Antonios Analitis, Klea Katsouyanni, +14 authors Juha Pekkanen
- Epidemiology
- 2006

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)

- Aristidis K. Nikoloulopoulos, Dimitris Karlis
- Statistics in medicine
- 2008

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)

- Harry Joe, Haijun Li, Aristidis K. Nikoloulopoulos
- J. Multivariate Analysis
- 2010

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)

- Aristidis K. Nikoloulopoulos, Harry Joe, Haijun Li
- Computational Statistics & Data Analysis
- 2012

It has been shown that vine copulas constructed from bivariate t copulas can provide good fits to multivariate financial asset return data. However, there might be stronger tail dependence of returns in the joint lower tail of assets than the upper tail. To this end, vine copula models with appropriate choices of bivariate reflection asymmetric linking… (More)

- Aristidis K. Nikoloulopoulos, Dimitris Karlis
- Computational Statistics & Data Analysis
- 2008

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)

- Aristidis K. Nikoloulopoulos, Dimitris Karlis
- Communications in Statistics - Simulation and…
- 2010

- Aristidis K. Nikoloulopoulos, Harry Joe
- Psychometrika
- 2015

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)

- Aristidis K. Nikoloulopoulos
- Statistics in medicine
- 2015

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)

- Aristidis K. Nikoloulopoulos
- Statistics in medicine
- 2016

The method of generalized estimating equations (GEE) is popular in the biostatistics literature for analyzing longitudinal binary and count data. It assumes a generalized linear model for the outcome variable, and a working correlation among repeated measurements. In this paper, we introduce a viable competitor: the weighted scores method for generalized… (More)