Risk modelling with the mixed Erlang distribution
By Gordon E. Willmot, X. Lin. 2011
77 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e7278bad05bf48bf72e950df2f0f458a10a83bc5
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
In Management Science and Operations Research
- Hélène Cossette
- É. Marceau
- Roel Verbelen
- Sheldon Lin
- Lan Gong
- Katrien Antonio
- Andrei L. Badescu
- Mélina Mailhot
In Statistics and Probability
- Roel Verbelen
- Sheldon Lin
- Lan Gong
- Hélène Cossette
- Katrien Antonio
- Andrei L. Badescu
- Mélina Mailhot
- É. Marceau
Citations in the corpus
- Multivariate distribution defined with Farlie–Gumbel–Morgenstern copula and mixed Erlang marginals: Aggregation and capital allocation (2013, unknown): “The class of mixed Erlang distributions has many interesting features which are discussed in detail notably in Willmot and Lin (2010) (see also references therein).”
- FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM (2015, background): “Klugman et al. (2013), Willmot and Lin (2011) and Willmot and Woo (2007) give an overview of these analytical and computational properties of mixtures of Erlangs.”
- TVaR-based capital allocation for multivariate compound distributions with positive continuous claim amounts (2012, background): “Willmot and Woo (2007), Lee and Lin (2010), and Willmot and Lin (2011) show the usefulness of this distribution.”