Parsimonious Parameterization of Age-Period-Cohort Models by Bayesian Shrinkage
Age-period-cohort models used in life and general insurance can be over-parameterized, and actuaries have used several methods to avoid this, such as cubic splines. Regularization is a statistical approach for avoiding over-parameterization, and it can reduce estimation and predictive variances compared to MLE. In Markov Chain Monte Carlo (MCMC) estimation, regularization is accomplished by the use of mean-zero priors, and the degree of parsimony can be optimized by numerically efficient out-of-sample cross-validation. This provides a consistent framework for comparing a variety of regularized MCMC models, such as those built with cubic splines, linear splines (as ours is), and the limiting case of non-regularized estimation.
- Bayesian Shrinkage in Actuarial Triangle Models.pdf application/pdf 765 KB Download File
More About This Work
- Academic Units
- School of Professional Studies
- Published Here
- June 14, 2017
Preprint submitted to ASTIN Bulletin: The Journal of the International Actuarial Association, June 13, 2017.