PARSIMONIOUS PARAMETERIZATION OF AGE-PERIOD-COHORT MODELS BY BAYESIAN SHRINKAGE

@article{Venter2017PARSIMONIOUSPO,
  title={PARSIMONIOUS PARAMETERIZATION OF AGE-PERIOD-COHORT MODELS BY BAYESIAN SHRINKAGE},
  author={Gary Venter and Sule {\"O}nsel Sahin},
  journal={ASTIN Bulletin},
  year={2017},
  volume={48},
  pages={89 - 110}
}
Abstract 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… 

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References

SHOWING 1-10 OF 42 REFERENCES

Bayesian Poisson log-bilinear models for mortality projections with multiple populations

Life insurers, pension funds, health care providers and social security institutions face increasing expenses due to continuing improvements of mortality rates. The actuarial and demographic

A General Procedure for Constructing Mortality Models

Recently a large number of new mortality models have been proposed to analyze historic mortality rates and project them into the future. Many of these suffer from being over-parametrized or have

A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting

This paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models and develops a class of Bayesian state-space models which incorporate a priori beliefs about the mortality model characteristics as well as for more flexible and appropriate assumptions relating to heteroscedasticity that present in observed mortality data.

The Application of Affine Processes in Multi-Cohort Mortality Model

Cohort effects have been identified in many countries. However, some mortality models only consider the modelling and projection of age-period effects. Others, that incorporate cohort effects, do not

Modeling and forecasting U. S. mortality

Abstract Time series methods are used to make long-run forecasts, with confidence intervals, of age-specific mortality in the United States from 1990 to 2065. First, the logs of the age-specific

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

An efficient computation of LOO is introduced using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights, and it is demonstrated that PSIS-LOO is more robust in the finite case with weak priors or influential observations.

A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States

Abstract We compare quantitatively eight stochastic models explaining improvements in mortality rates in England and Wales and in the United States. On the basis of the Bayes Information Criterion

On Measuring and Correcting the Effects of Data Mining and Model Selection

The concept of GDF offers a unified framework under which complex and highly irregular modeling procedures can be analyzed in the same way as classical linear models and many difficult problems can be solved easily.

A cohort-based extension to the Lee-Carter model for mortality reduction factors

Loss Models: From Data to Decisions, 2nd edition

Actuaries focusing on health insurance and casualty insurance are also interested in modeling (and forecasting) deaths, but these actuaries deal even more with other types of losses that have more