In:
Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 66, No. 1 ( 2017-01-01), p. 121-139
Abstract:
Mortality forecasts are typically limited in that they pertain only to national death rates, predict only all-cause mortality or do not capture and utilize the correlation between diseases. We present a novel Bayesian hierarchical model that jointly forecasts cause-specific death rates for geographic subunits. We examine its effectiveness by applying it to US vital statistics data for 1979–2011 and produce forecasts to 2024. Not only does the model generate coherent forecasts for mutually exclusive causes of death, but also it has lower out-of-sample error than alternative commonly used models for forecasting mortality.
Type of Medium:
Online Resource
ISSN:
0035-9254
,
1467-9876
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2017
detail.hit.zdb_id:
204797-4
detail.hit.zdb_id:
1482300-7
detail.hit.zdb_id:
1476894-X
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