Predictive modeling of threshold life tables
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20200018148 | ||
| 003 | MAP | ||
| 005 | 20200602122544.0 | ||
| 008 | 200528e20200501usa|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20110030926$aJi, Min | ||
| 245 | 1 | 0 | $aPredictive modeling of threshold life tables$cMin Ji, Mostafa Aminzadeh, Min Deng |
| 520 | $aAccurate modeling of mortality at advanced ages is crucial to the valuation of pension plans and life annuities, especially longevity annuities for which annuity payments start very late in life, say age 85. Parametric regression models extrapolate lifetime distribution to the advanced ages but do not guarantee goodness fit. Threshold life tables offer an alternative solution to this problem using piecewise distribution via the peaks-over-threshold approach in the extreme value family. However, parameter estimation of this model is challenging, especially the determination of the threshold. Regular estimation methods do not guarantee a smooth life table. In this research, we impose parameter constraints to achieve a smooth threshold life table and propose a Bayesian approach to obtain Bayes estimates of the parameters and approximate predictive density via simulation, which can be used to compute life expectancy and other measures of interest in a Bayesian framework. | ||
| 650 | 4 | $0MAPA20080599300$aTablas de mortalidad | |
| 650 | 4 | $0MAPA20080602642$aModelos de simulación | |
| 650 | 4 | $0MAPA20080592455$aPlanes de pensiones | |
| 650 | 4 | $0MAPA20080573614$aRenta vitalicia | |
| 650 | 4 | $0MAPA20080625894$aMétodos de estimación objetiva | |
| 650 | 4 | $0MAPA20100065242$aTeorema de Bayes | |
| 650 | 4 | $0MAPA20080580377$aEsperanza de vida | |
| 700 | 1 | $0MAPA20200012719$aAminzadeh, Mostafa | |
| 700 | 1 | $0MAPA20200012726$aDeng, Min | |
| 773 | 0 | $wMAP20077000239$tNorth American actuarial journal$dSchaumburg : Society of Actuaries, 1997-$x1092-0277$g01/06/2020 Tomo 24 Número 2 - 2020 , p. 316-332 |