PAA 2019 one-day workshop on Bayesian small-area estimation using complex survey data. Jon Wakefield, Richard Li, and me @jonwakeblue, @z_richard_li
— Sam Clark (@sinafala) November 20, 2019
When: Wed. April 22: 8:00 AM – 5:30 PM
Where: room "Wilson C"
Limited to 40 participants
More at: https://t.co/05JlOlb2RU
Wednesday, November 20, 2019
Bayesian Estimation
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Course Description:
Small area estimation (SAE) is an important endeavor in global health, epidemiology, and increasingly,
in demography. SAE is often based on data obtained from complex surveys, and one
must acknowledge the survey design when statistical analysis is performed so that measures of
uncertainty incorporate sampling variability. Often data in particular areas are sparse (perhaps
non-existent) and so spatial smoothing is advantageous to ‘borrow strength’ from neighboring
areas.
We will begin with introductions to complex survey data, SAE, space-time modeling, and Bayesian
statistics and then bring these topics together to show how reliable SAE estimation can be
performed. The course will end with a complex application: space-time smoothing of under-5
infant mortality using demographic and health survey (DHS) data. This application is part of an
on-going collaboration that the instructors have with UNICEF. In this context, the use of both full
and summary birth history data will be described. Throughout, hands-on experience will be gained
through the use of the instructors’ SUMMER R package that carries out space-time smoothing of
area-level complex survey data, based on methodology that has been developed by the instructors
[Li et al., 2019, Wakefield et al., 2019, Wakefield, 2020]. More details on the package, and more
materials are available at http://faculty.washington.edu/jonno/space-station.html.
There are no prerequisites, although familiarity with R, Bayesian methods, and mortality measurement
would be helpful. Participants should bring their own laptop with R and RStudio installed
so that they can follow along and experiment with the data and code.
When you leave the workshop you will:
Have an overview of small area estimation (SAE).
Understand the need for special methods for the analysis of survey data.
Understand the need for special methods for the analysis of spatial data.
Have a grasp of the basics of Bayesian inference.
Presenters:
Zehang Richard Li https://zehangli.com
Jon Wakefield http://faculty.washington.edu/jonno/index.html
Sam Clark http://www.samclark.net
References
Zehang R Li, Yuan Hsiao, Jessica Godwin, Bryan D Martin, Jon Wakefield, and Samuel J Clark.
Changes in the spatial distribution of the under five mortality rate: small-area analysis of 122
DHS surveys in 262 subregions of 35 countries in Africa. PLoS One, 2019. Published January
22, 2019.
J. Wakefield, G.-A. Fuglstad, A. Riebler, J. Godwin, K. Wilson, and S.J. Clark. Estimating under
five mortality in space and time in a developing world context. Statistical Methods in Medical
Research, 28:2614–2634, 2019.
Jon Wakefield. Prevalence mapping. In Wiley StatsRef: Statistics Reference Online. 2020.
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