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ABSTRACT
Demographic estimation becomes a problem of small area estimation when detailed disaggregation leads to small cell counts. The usual difficulties of small area estimation are compounded when the available data sources contain measurement errors. We present a Bayesian approach to the problem of small area estimation with imperfect data sources. The overall model contains separate submodels for underlying demographic processes and for measurement processes. All unknown quantities in the model, including coverage ratios and demographic rates, are estimated jointly via Markov chain Monte Carlo methods. The approach is illustrated using the example of provincial fertility rates in Cambodia.
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No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Junni L. Zhang
Junni L. Zhang is Associate Professor of Statistics at Peking University, China. Her research interests are Bayesian demography, causal inference, text and data mining. She has a PhD in Statistics from Harvard University.
John Bryant
John Bryant is a demographer and data scientist at Bayesian Demography Limited. He has previously worked at Statistics New Zealand, the New Zealand Treasury, and universities in New Zealand and Thailand. He has a PhD in Demography from the Australian National University.