Imperial College London

ProfessorMajidEzzati

Faculty of MedicineSchool of Public Health

Chair in Global Environmental Health
 
 
 
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Contact

 

+44 (0)20 7594 0767majid.ezzati Website

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Foreman:2016:10.1111/rssc.12157,
author = {Foreman, KJ and Li, G and Best, N and Ezzati, M},
doi = {10.1111/rssc.12157},
journal = {Journal of the Royal Statistical Society: Series C},
pages = {121--139},
title = {Small area forecasts of cause-specific mortality: application of a Bayesian hierarchical model to US vital registration data},
url = {http://dx.doi.org/10.1111/rssc.12157},
volume = {66},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Foreman,KJ
AU - Li,G
AU - Best,N
AU - Ezzati,M
DO - 10.1111/rssc.12157
EP - 139
PY - 2016///
SN - 0035-9254
SP - 121
TI - Small area forecasts of cause-specific mortality: application of a Bayesian hierarchical model to US vital registration data
T2 - Journal of the Royal Statistical Society: Series C
UR - http://dx.doi.org/10.1111/rssc.12157
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000392808300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/49228
VL - 66
ER -