Bayesian Inference for the Loss Models via Mixture Priors
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- M. S. Aminzadeh & M. Deng, 2019. "Bayesian predictive modeling for Inverse Gamma-Pareto composite distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(8), pages 1938-1954, April.
- Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.
- Abu Bakar, S.A. & Hamzah, N.A. & Maghsoudi, M. & Nadarajah, S., 2015. "Modeling loss data using composite models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 146-154.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Min Deng & Mostafa Aminzadeh & Min Ji, 2021. "Bayesian Predictive Analysis of Natural Disaster Losses," Risks, MDPI, vol. 9(1), pages 1-23, January.
- Bowen Liu & Malwane M. A. Ananda, 2022. "A Generalized Family of Exponentiated Composite Distributions," Mathematics, MDPI, vol. 10(11), pages 1-18, June.
- Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
- Reynkens, Tom & Verbelen, Roel & Beirlant, Jan & Antonio, Katrien, 2017.
"Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions,"
Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 65-77.
- Tom Reynkens & Roel Verbelen & Jan Beirlant & Katrien Antonio, 2016. "Modeling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Working Papers of Department of Decision Sciences and Information Management, Leuven 549545, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
- Tom Reynkens & Roel Verbelen & Jan Beirlant & Katrien Antonio, 2016. "Modeling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 549545, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.
- Ahmed Z. Afify & Ahmed M. Gemeay & Noor Akma Ibrahim, 2020. "The Heavy-Tailed Exponential Distribution: Risk Measures, Estimation, and Application to Actuarial Data," Mathematics, MDPI, vol. 8(8), pages 1-28, August.
- Bhati, Deepesh & Ravi, Sreenivasan, 2018. "On generalized log-Moyal distribution: A new heavy tailed size distribution," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 247-259.
- Ruben Dewitte & Michel Dumont & Glenn Rayp & Peter Willemé, 2022.
"Unobserved heterogeneity in the productivity distribution and gains from trade,"
Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(3), pages 1566-1597, August.
- Dewitte, Ruben & Dumont, Michel & Rayp, Glenn & Willemé, Peter, 2020. "Unobserved Heterogeneity in the Productivity Distribution and Gains From Trade," MPRA Paper 102711, University Library of Munich, Germany.
- Fung, Tsz Chai, 2022. "Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 180-198.
- Bae, Taehan & Miljkovic, Tatjana, 2024. "Loss modeling with the size-biased lognormal mixture and the entropy regularized EM algorithm," Insurance: Mathematics and Economics, Elsevier, vol. 117(C), pages 182-195.
- Semhar Michael & Tatjana Miljkovic & Volodymyr Melnykov, 2020. "Mixture modeling of data with multiple partial right-censoring levels," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 355-378, June.
- Blostein, Martin & Miljkovic, Tatjana, 2019. "On modeling left-truncated loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 35-46.
- Muhammad Hilmi Abdul Majid & Kamarulzaman Ibrahim & Nurulkamal Masseran, 2023. "Three-Part Composite Pareto Modelling for Income Distribution in Malaysia," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
- Li, Zhengxiao & Wang, Fei & Zhao, Zhengtang, 2024. "A new class of composite GBII regression models with varying threshold for modeling heavy-tailed data," Insurance: Mathematics and Economics, Elsevier, vol. 117(C), pages 45-66.
- Naderi, Mehrdad & Hashemi, Farzane & Bekker, Andriette & Jamalizadeh, Ahad, 2020. "Modeling right-skewed financial data streams: A likelihood inference based on the generalized Birnbaum–Saunders mixture model," Applied Mathematics and Computation, Elsevier, vol. 376(C).
- Carmine Franco & Luc Dumontier, 2024. "Modelling capacity for systematic equity strategies," Journal of Asset Management, Palgrave Macmillan, vol. 25(4), pages 407-416, July.
- Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
- Chen, Zezhun & Dassios, Angelos & Tzougas, George, 2022. "EM estimation for the bivariate mixed exponential regression model," LSE Research Online Documents on Economics 115132, London School of Economics and Political Science, LSE Library.
- Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.
- Jackie Li & Jia Liu, 2023. "Claims Modelling with Three-Component Composite Models," Risks, MDPI, vol. 11(11), pages 1-16, November.
- Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
More about this item
Keywords
Bayesian estimation; composite model; mixture prior distribution; mixture posterior distribution; Bayes factor; marginal likelihood;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:11:y:2023:i:9:p:156-:d:1229854. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.