Parsimonious parameterization of age-period-cohort models by Bayesian Shrinkage

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      <subfield code="a">Age-period-cohort models used in life and general insurance can be overparameterized, and actuaries have used several methods to avoid this, such as cubic splines. Regularization is a statistical approach for avoiding overparameterization, 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. We apply this to the multiple-trend model of Hunt and Blake (2014).</subfield>
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