Dynamic empirical Bayes models and their applications to longitudinal data
Abstract/Contents
- Abstract
- In the field of insurance rate-making, the framework of standard credibility theory was laid down by Buhlmann in an empirical Bayes setting. However, evolutionary credibility models, in which the individual risk profile that is embedded in a collective evolves over time, are not yet well developed. We develop a new class of dynamic linear empirical Bayes (EB) models as an alternative to linear state-space models for evolutionary credibility. This new dynamic EB modeling approach can be readily extended to a generalized framework, which provides flexible and computationally efficient methods for modeling longitudinal data. Our dynamic EB approach pools the cross-sectional information over individual time series to replace an inherently complicated hidden Markov model by a considerably simpler generalized linear mixed model. We also review the Pepe-Couper (1997) approach to modeling longitudinal data and propose a more general formulation of the approach in terms of "information sets" for prediction. This formulation unifies the marginal and transitional modeling approaches and strikes a balance between the flexibility of the marginal approach and the predictive power of transitional modeling. We further extend our predictive dynamic EB models to resolve the problem of "excess zeros" in longitudinal data. The advantages of using these models are illustrated using examples in insurance, default modeling of corporate loans in finance and predicting baseball batting averages.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2011 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Sun, Kevin Haoyu |
|---|---|
| Associated with | Stanford University, Department of Statistics |
| Primary advisor | Lai, T. L |
| Thesis advisor | Lai, T. L |
| Thesis advisor | Walther, Guenther |
| Thesis advisor | Zhang, Nancy R. (Nancy Ruonan) |
| Advisor | Walther, Guenther |
| Advisor | Zhang, Nancy R. (Nancy Ruonan) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Kevin Haoyu Sun. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Kevin Haoyu Sun
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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