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**Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data.**
*(English)*
Zbl 1284.91238

Insur. Math. Econ. 52, No. 2, 320-337 (2013); corrigendum ibid. 53, No. 3, 919 (2013).

Summary: A new common mortality modeling structure is presented for analyzing mortality dynamics for a pool of countries, under the framework of generalized linear models (GLM). The countries are first classified by fuzzy c-means cluster analysis in order to construct the common sparse age-period model structure for the mortality experience. Next, we propose a method to create the common sex difference age-period model structure and then use this to produce the residual age-period model structure for each country and sex. The time related principal components are extrapolated using dynamic linear regression (DLR) models and coherent mortality forecasts are investigated. We make use of mortality data from the “Human Mortality Database”.

### MSC:

91B30 | Risk theory, insurance (MSC2010) |

91D20 | Mathematical geography and demography |

62P05 | Applications of statistics to actuarial sciences and financial mathematics |

62J12 | Generalized linear models (logistic models) |

62H30 | Classification and discrimination; cluster analysis (statistical aspects) |

### Keywords:

fuzzy c-means cluster; generalized linear models; sparse principal component analysis; dynamic linear regression; mortality forecasting; residuals; coherent
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\textit{P. Hatzopoulos} and \textit{S. Haberman}, Insur. Math. Econ. 52, No. 2, 320--337 (2013; Zbl 1284.91238)

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