Bayesian analysis of big data in insurance predictive modeling using distributed computing
Contenido multimedia no disponible por derechos de autor o por acceso restringido. Contacte con la institución para más información.
| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20170030553 | ||
| 003 | MAP | ||
| 005 | 20220911211739.0 | ||
| 008 | 170920e20170706bel|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20140001262$aZhang, Yanwei | ||
| 245 | 1 | 0 | $aBayesian analysis of big data in insurance predictive modeling using distributed computing$cYanwei Zhang |
| 300 | $a19 p. | ||
| 520 | $aWhile Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian computation using distributed computing on Apache Spark across a cluster of computers. The distributed algorithm dramatically boosts the speed of Bayesian computation and expands the scope of applicability of Bayesian methods in insurance modeling. The empirical analysis applies a Bayesian hierarchical Tweedie model to a big data of 13 million insurance claim records. The distributed algorithm achieves as much as 65 times performance gain over the non-parallel method in this application. The analysis demonstrates that Bayesian methods can be of great value to large-scaled insurance predictive modeling. | ||
| 650 | 4 | $0MAPA20140022717$aBig data | |
| 650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 773 | 0 | $wMAP20077000420$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association$x0515-0361$g01/09/2017 Volumen 47 Número 3 - septiembre 2017 , p. 943-961 |