Gaussian proces models for mortality rates and improvement factors
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20180032059 | ||
| 003 | MAP | ||
| 005 | 20220911211517.0 | ||
| 008 | 181119e20180903gbr|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20180014666$aLudkovski, Mike | ||
| 245 | 1 | 0 | $aGaussian proces models for mortality rates and improvement factors$cMike Ludkovski, Jimmy Risk, Howard Zail |
| 520 | $aWe develop a Gaussian process (GP) framework for modeling mortality rates and mortality improvement factors. GP regression is a nonparametric, data driven approach for determining the spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as calendar year and age. The GP model quantifies uncertainty associated with smoothed historical experience and generates full stochastic trajectories for out-of-sample forecasts. | ||
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080592011$aModelos actuariales | |
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 650 | 4 | $0MAPA20080555306$aMortalidad | |
| 650 | $0MAPA20220007825$aData driven | ||
| 700 | 1 | $0MAPA20180014673$aRisk, Jimmy | |
| 700 | 1 | $0MAPA20180014680$aZail, Howard | |
| 773 | 0 | $wMAP20077000420$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association$x0515-0361$g03/09/2018 Volumen 48 Número 3 - septiembre 2018 , p. 1307-1347 |