Finite normal mixture copulas for multivariate discrete data modeling

By Aristidis K. Nikoloulopoulos, Dimitris Karlis. 2009

48 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/dfc8bd542802baf8cb8c4edb94652f4865297a19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xanthi Pedeli
2. Aristidis K. Nikoloulopoulos
3. Lluís Bermúdez
4. Claudia Czado
5. Dimitris Karlis
6. Harry Joe
7. Anastasios Panagiotelis
8. Eike C. Brechmann

In Finance

1. Aristidis K. Nikoloulopoulos
2. Claudia Czado
3. Harry Joe
4. Dimitris Karlis
5. Xanthi Pedeli
6. Eike C. Brechmann
7. Anastasios Panagiotelis
8. Kjersti Aas

In Management Science and Operations Research

1. Xanthi Pedeli
2. Dimitris Karlis
3. Aristidis K. Nikoloulopoulos
4. Lluís Bermúdez
5. Harry Joe
6. Anastasios Panagiotelis
7. Claudia Czado
8. Albert Maydeu-Olivares

Citations in the corpus

• Bayesian multivariate Poisson models for insurance ratemaking (2011, unknown): “There are some other models that allow for negative correlation (see van Ophem 1999, Chib and Winkelmann 2001, Berkhout and Plug 2004, Karlis and Melogkotsidou (2007), Nikoloulopoulos and Karlis, 2009), but they are much more complicated and require a special effort for parameter estimation.”
• Pair Copula Constructions for Multivariate Discrete Data (2012, background): “As a result, many applications of copula modeling to discrete data have been low-dimensional and used copulas that are fast to compute (see Nikoloulopoulos and Karlis ( 2008 )), Nikoloulopoulos and Karlis ( 2009 ) and Li and Wong ( 2011 ).”
• Modelling Dependence in a Ratemaking Procedure with Multivariate Poisson Regression Models (2010, background): “There are some other models that allow for negative correlation (see van Ophem 1999, Chib and Winkelmann 2001, Berkhout and Plug 2004, Karlis and Melogkotsidou (2007), Nikoloulopoulos and Karlis, 2009), but they are much more complicated and require a special effort for parameter estimation.”
• Flexible Bivariate INAR(1) Processes Using Copulas (2013, methodology): “For the case of discrete data, copulabased modeling is less developed (see Genest and Nešlehová, 2007; Nikoloulopoulos and Karlis, 2009).”
• A bivariate INAR(1) process with application (2011, methodology): “…literature, as, e.g., the bivariate Poisson lognormal model of Aitchinson and Ho (1989) (see also Chib and Winkelmann, 2001), the finite mixture model developed by Karlis and Meligkotsidou (2007) and models based on copulas see, e.g., Nikoloulopoulos and Karlis (2009) and the references therein.”
• Copula-Based Models for Multivariate Discrete Response Data (2013, methodology): “Nikoloulopoulos and Karlis [45] modeled multivariate count data proposing a copula generated by a mixture of two independent MVN distributions.”