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PhD Studentship (ESRC South Coast DTP) - Bayesian methods in Official Statistics

Social Statistics & Demography

Location:  Highfield Campus
Salary:   £14553
Annual stipend of £14553 plus payment of programme fees.
Closing Date:   Wednesday 28 June 2017
Reference:  878917CC

Bayesian methods in Official Statistics

A fully funded studentship awarded by the Economic and Social Research Council (ESRC) South Coast Doctoral Training Partnership (SCDTP)

Supervisory Team:

Dr Jakub Bijak (Division of Social Statistics and Demography) ( and colleagues from S3RI and theOffice for National Statistics (ONS)

The aim of this research is explore the potential for Bayesian methods to be applied in official statistics. The research will take place in two parts: the first part of the PhD will focus on current and potential applications within National Statistical Institutes and identify possible applications to be the subject of the research in years two and three (second part). Particular attention will be given to how the uncertainty can be communicated to users. Possible topics which will be explored as part of the PhD include: Bayesian estimation in survey estimation – application to non-probability-based surveys; Bayesian methods a means of combining data from various sources (e.g. ‘big’ data, administrative data); and in small area estimation.

Research questions:

1) What Bayesian methods have been already applied, and which could potentially be applied in official statistics?
2) What is the scope for incorporating such methods within National Statistical Institutions, including the Office of National Statistics, in the future?
3) How can the concept of uncertainty in Bayesian methods most effectively be communicated to different audiences?

The standard framework for estimation within Official Statistics in the UK is based on a design-based approach. Estimates are derived from probability surveys with samples large enough to give high quality estimates. A model-based approach is adopted for some outputs for small areas (e.g. income and unemployment), where sample sizes are small. Bayesian methods, which are mainly model-based, are a potential methodology for the integration of data from various sources. Such methods have been adopted in New Zealand for making population estimates, by the UN for population projections, and in research carried out in the Australian Bureau of Statistics as a way of incorporating big data into official statistics. The ONS do not currently use any Bayesian methods in the production of official statistics – but have current research projects with the University of Southampton exploring Bayesian methods in population projections. As part of this project, the ONS would like to explore further the potential for Bayesian methods within official statistics.

Skills required of the PhD student:

• Essential: Background in mathematics and statistics.
• Essential: A good understanding of various inferential frameworks and interest in the applications of these advanced statistical methods.


South Coast DTP Funding provides an annual maintenance grant (tax free) of £14553, plus payment of all programme fees.  Other funding available for SCDTP funded students can be found on the SCDTP website (

Funding is provided for 3 years full-time PhD study (pro-rata for part-time students).  Applications for 1+3 funding for students completing a Master's year prior to the commencement of PhD study are also welcome (details available at

Application Procedure

The closing date and time for applications is noon on 28th June 2017.  The full application procedure, the funding application form, and more information on the South Coast Doctoral Training Partnership can be found at:

For further information about this project, please contact the lead supervisor detailsed above.  For questions relating to the application procedure, or for more information about the SCDTP, please visit the SCDTP website or contact us at


The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position



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