Bayesian Nonparametric Methods for Multiple Systems Estimation at University of Birmingham on FindAPhD.com

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Dr Rowland Seymour Applications accepted all year round Competition Funded PhD Project (UK Students Only)

Birmingham United Kingdom Applied Statistics Criminology Data Analysis Mathematical Modelling Statistics

School of Mathematics, University of Birmingham

About the Project

Estimating population size is a common problem in statistics with many well-established methods. However, these methods rely on strict assumptions about the structure of the population. These assumptions are often unrealistic and may result in faulty population estimates. Methodology to estimate population size is commonly applied to epidemic and ecological data sets, but is being increasingly applied to social good applications, estimating the number of individuals facing human rights abuses (Silverman, 2020).

The project will advance methods for estimating population size, for example, by including population dynamics, or by stratifying the population into subgroups. The project will exploit advancements in Bayesian computation and functional data analysis to develop novel and efficient computational algorithms, this will allow for new models to be successfully implemented.

References

Bird, S. M., & King, R. (2018). Multiple Systems Estimation (or capture-recapture estimation) to inform public policy. Annual Review of Statistics and Its Application, 5, 95–118. https://doi.org/10.1146/annurev-statistics-031017-100641
Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical Inference from Capture Data on Closed Animal Populations. Wildlife Monographs, 62, 3–135. http://www.jstor.org/stable/3830650
Silverman, B. W. (2020). Multiple‐systems analysis for the quantification of modern slavery: classical and Bayesian approaches. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 183(3), 691–736. https://doi.org/10.1111/rssa.12505

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