Modeling loss data using composite models

By Shaiful Anuar Abu Bakar, Nor Aishah Hamzah, M. Maghsoudi, Saralees Nadarajah. 2015

124 citations recorded by the source corpus.

Subfields: Demography, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e182d97091c89b975d51aec641531af8ff2f04da

How researchers see this paper

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

In Demography

1. Ahmed M. Gemeay
2. Bettina Grün
3. Tatjana Miljkovic
4. Noor Akma Ibrahim
5. Ahmed Z. Afify

In Management Science and Operations Research

1. Ahmed M. Gemeay
2. Bettina Grün
3. Tatjana Miljkovic
4. Ahmed Z. Afify

In Statistics and Probability

1. Tatjana Miljkovic
2. Ahmed M. Gemeay
3. Bettina Grün
4. Noor Akma Ibrahim
5. Ahmed Z. Afify
6. Haitham M. Yousof
7. G. Hamedani
8. Gauss Moutinho Cordeiro

Citations in the corpus

• Modeling loss data using mixtures of distributions (2016, methodology): “Recent literature on the modeling of heavy tailed insurance loss data tends to focus more on simple models based on single parametric distributions and composite models (Bakar et al., 2015).”
• The Heavy-Tailed Exponential Distribution: Risk Measures, Estimation, and Application to Actuarial Data (2020, background): “[13], Punzo [14], Mazza and Punzo [15], Miljkovic and Grun [16], and Punzo et al.”