A long-term care multi-state Markov model revisited : a Markov chain Monte Carlo approach
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20220019859 | ||
| 003 | MAP | ||
| 005 | 20220701140847.0 | ||
| 008 | 220701e20220606che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | 1 | $0MAPA20220006682$aFleischmann, Anselm | |
| 245 | 1 | 0 | $aA long-term care multi-state Markov model revisited$b: a Markov chain Monte Carlo approach$cAnselm Fleischmann, Jonas Hirz, Daniel Sirianni |
| 520 | $aA multi-state Markov model is calibrated to Austrian data on recipients of long-term care payments the amount of which depends on defined frailty state levels. In contrast to a predecessor paper by one of the authors (see Fleischmann in Eur Actuar J 5(2):327354, 2015), we are able to allow for different mortality intensities for different frailty states. A correction term is introduced in the mortality intensities' functional representation to deal with observed mortality humps around the retirement age for certain frailty levels. Parameter calibration is done using MCMC methods (adaptive MetropolisHastings-within-Gibbs). The results reveal a considerably better fit of refined to raw prevalence units than the original model of Fleischmann (Eur Actuar J 5(2):327354, 2015). Finally, the results are used to estimate the remaining healthy lifetime for certain ages, indicating slight but significant increases over the last 4 years. | ||
| 650 | 4 | $0MAPA20080608606$aSimulación Monte Carlo | |
| 650 | 4 | $0MAPA20080576783$aModelo de Markov | |
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 700 | 1 | $0MAPA20220006699$aHirz, Jonas | |
| 773 | 0 | $wMAP20220007085$g06/06/2022 Volúmen 12 - Número 1 - junio 2022 , p. 215-247$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |