A Parameterized approach to modeling and forecasting mortality
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20090091634 | ||
| 003 | MAP | ||
| 005 | 20091022125409.0 | ||
| 008 | 091019e20090227esp|| p |0|||b|spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20090035966$aHatzopoulos, P. | ||
| 245 | 1 | 2 | $aA Parameterized approach to modeling and forecasting mortality$cP. Hatzopoulos, S. Haberman |
| 520 | $aA new method is proposed of constructing mortality forecasts. This parameterized approach utilizes Generalized Linear Models (GLMs), based on heteroscedastic Poisson (non-additive) error structures, and using an orthonormal polynomial design matrix. Principal Component (PC) analysis is then applied to the cross-sectional fitted parameters. The produced model can be viewed either as a one-factor parameterized model where the time series are the fitted parameters, or as a principal component model, namely a log-bilinear hierarchical statistical association model of Goodman [Goodman, L.A., 1991. Measures, models, and graphical displays in the analysis of cross-classified data. J. Amer. Statist. Assoc. 86(416), 10851111] or equivalently as a generalized LeeCarter model with p interaction terms. Mortality forecasts are obtained by applying dynamic linear regression models to the PCs. Two applications are presented: Sweden (17512006) and Greece (19572006).Article O | ||
| 650 | 1 | $0MAPA20080555306$aMortalidad | |
| 650 | 1 | $0MAPA20090033023$aEstadística matemática | |
| 650 | 1 | $0MAPA20080549923$aBootstrap | |
| 650 | 1 | $0MAPA20080580377$aEsperanza de vida | |
| 650 | 1 | $0MAPA20080579258$aCálculo actuarial | |
| 700 | 1 | $0MAPA20090035973$aHaberman, S. | |
| 773 | 0 | $wMAP20077100574$tInsurance : mathematics and economics$dOxford : Elsevier, 1990-$x0167-6687$g27/02/2009 Tomo 44 Número 1 - 2009, p. 103-123 |