Cover Image

Nonlinear mixed-effect compartmental model in loss reserving

The Bayesian hierarchical compartmental loss reserving model is an one of a kind model under the loss reserving context in the actuarial society. In this research, the model will be reformulated with some improvements on the Bayesian assumptions to increase its practical usage in the industry. First...

Full description

Saved in:
Bibliographic Details
Main Author: Ng, Wei Siang
Format: Thesis
Published: 2021
Subjects:
QA Mathematics
QC Physics
Online Access:http://eprints.sunway.edu.my/2394/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.sunway.eprints.2394
record_format eprints
spelling my.sunway.eprints.23942023-09-27T06:29:48Z http://eprints.sunway.edu.my/2394/ Nonlinear mixed-effect compartmental model in loss reserving Ng, Wei Siang QA Mathematics QC Physics The Bayesian hierarchical compartmental loss reserving model is an one of a kind model under the loss reserving context in the actuarial society. In this research, the model will be reformulated with some improvements on the Bayesian assumptions to increase its practical usage in the industry. First of all, the Markov chain Monte Carlo methods are used to approximate the posterior distribution of the parameters. In particular, the No-U-Turn sampling (NUTS) is replacing the Gibbs sampling method to reduce the computation time and resources. Furthermore, several convergence diagnostics are performed, supported with descriptive statistics and visualisations after sampling is done. These checkings can avoid questionable decision making based on the sampling chains as it makes the posterior inference scientifically and statistically defensible. Besides, multiple loss triangle datasets contain only a single entity with different lines of business are fitted to the models. In conclusion, the application of NUTS gives better results and the analysis of multiple datasets with the model further prove the nonlinear mixed-effect compartmental loss reserving model can fit dataset with a different line of business. In addition, the Bayesian reformulation and the convergence diagnostics give a better understanding on the application and formulation of the Bayesian loss reserving model. 2021-05 Thesis NonPeerReviewed Ng, Wei Siang (2021) Nonlinear mixed-effect compartmental model in loss reserving. Masters thesis, Sunway University.
institution Sunway University
building Sunway Campus Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Sunway University
content_source Sunway Institutional Repository
url_provider http://eprints.sunway.edu.my/
topic QA Mathematics
QC Physics
spellingShingle QA Mathematics
QC Physics
Ng, Wei Siang
Nonlinear mixed-effect compartmental model in loss reserving
description The Bayesian hierarchical compartmental loss reserving model is an one of a kind model under the loss reserving context in the actuarial society. In this research, the model will be reformulated with some improvements on the Bayesian assumptions to increase its practical usage in the industry. First of all, the Markov chain Monte Carlo methods are used to approximate the posterior distribution of the parameters. In particular, the No-U-Turn sampling (NUTS) is replacing the Gibbs sampling method to reduce the computation time and resources. Furthermore, several convergence diagnostics are performed, supported with descriptive statistics and visualisations after sampling is done. These checkings can avoid questionable decision making based on the sampling chains as it makes the posterior inference scientifically and statistically defensible. Besides, multiple loss triangle datasets contain only a single entity with different lines of business are fitted to the models. In conclusion, the application of NUTS gives better results and the analysis of multiple datasets with the model further prove the nonlinear mixed-effect compartmental loss reserving model can fit dataset with a different line of business. In addition, the Bayesian reformulation and the convergence diagnostics give a better understanding on the application and formulation of the Bayesian loss reserving model.
format Thesis
author Ng, Wei Siang
author_facet Ng, Wei Siang
author_sort Ng, Wei Siang
title Nonlinear mixed-effect compartmental model in loss reserving
title_short Nonlinear mixed-effect compartmental model in loss reserving
title_full Nonlinear mixed-effect compartmental model in loss reserving
title_fullStr Nonlinear mixed-effect compartmental model in loss reserving
title_full_unstemmed Nonlinear mixed-effect compartmental model in loss reserving
title_sort nonlinear mixed-effect compartmental model in loss reserving
publishDate 2021
url http://eprints.sunway.edu.my/2394/
_version_ 1779442530148941824
score 13.211869

Similar Items