Predictive modeling in long-term care insurance
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20160022155 | ||
| 003 | MAP | ||
| 005 | 20160817142057.0 | ||
| 008 | 160712e20160601usa|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20160008869$aLally, Nathan R. | ||
| 245 | 1 | 0 | $aPredictive modeling in long-term care insurance$cNathan R. Lally, Brian M. Hartman |
| 520 | $aThe accurate prediction of long-term care insurance (LTCI) mortality, lapse, and claim rates is essential when making informed pricing and risk management decisions. Unfortunately, academic literature on the subject is sparse and industry practice is limited by software and time constraints. In this article, it is reviewed current LTCI industry modeling methodology, which is typically Poisson regression with covariate banding/modification and stepwise variable selection. It is tested the claim that covariate banding improves predictive accuracy, examine the potential downfalls of stepwise selection, and contend that the assumptions required for Poisson regression are not appropriate for LTCI data. | ||
| 650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20100014189$aLong term care insurance | |
| 650 | 4 | $0MAPA20080555306$aMortalidad | |
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 650 | 4 | $0MAPA20090041721$aDistribución Poisson-Beta | |
| 650 | 4 | $0MAPA20080591182$aGerencia de riesgos | |
| 650 | 4 | $0MAPA20080592578$aPolítica de precios | |
| 650 | 4 | $0MAPA20130012056$aGastos médicos | |
| 700 | 1 | $0MAPA20130016856$aHartman, Brian M. | |
| 773 | 0 | $wMAP20077000239$tNorth American actuarial journal$dSchaumburg : Society of Actuaries, 1997-$x1092-0277$g01/06/2016 Tomo 20 Número 2 - 2016 , p. 160-183 |