Borrowing Strength – a collaborative software development for Small Area Estimation

ESRC National Centre for Research Methods funded project

  • Duration: 01/03/2018 - 31/07/2019



Small Area Estimation (SAE) is the name given to the process of calculating statistics of interest for each of a set of small areas within a larger population, for example neighbourhood average house prices. Typically, standard surveys provide insufficient data on each small area to give accurate estimates and so instead these techniques 'borrow strength' by incorporating information from other areas, measured variables, and datasets.

In this project we will develop a user-friendly statistical software toolkit that will assist with all aspects of the process of constructing small area estimates.

We will focus on a computationally intensive estimation approach known as Markov Chain Monte Carlo (MCMC) methods and will exploit the possibilities of modern computing to develop methods that use parallel processing to make MCMC a viable approach for the big datasets that are often used in SAE. MCMC methods are particularly good at accurately capturing all sources of uncertainty in the modelling and so produce better calibrated estimates. They can also be easily extended to include additional features of the data within the modelling process.

There already exist many competing approaches to SAE and so we will not only develop new approaches but also provide access to existing methods developed in the emdi software package by members of the team.

We will develop through the Stat-JR software package a common interface to both the new MCMC methods and those developed in emdi so that users can try out and compare alternative approaches through one convenient software interface. We will do this by using our statistical analysis assistant features in Stat-JR which will allow a single interface to the two alternative approaches and will enhance the statistical analysis by including details of the different steps involved in the analysis.


The grant produced several templates for StatJR along with an eBook that can be used as a Statistical Analysis Assistant for SAE modelling. These can be downloaded from here.

The templates also interoperate with the emdi package in the R software which can be downloaded from CRAN. This also includes further details on the package and provides a vignette.


Small Area Estimation using R and Stat-JR

For more details of this workshop see CMM Workshops:

Journal articles


Kreutzmann, A. K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (Accepted/In press) The R package emdi for the estimation and mapping of regional disaggregated indicators.  Journal of Statistical Software. (to appear early 2020)


Tzavidis, N., Zhang, L.-C., Luna Hernandez, A., Schmid, T., and Rojas-Perilla, N. (2019) From start to finish: A framework for the production of small area official statistics. Journal of the Royal Statistical Society: Series A, 2018, 181 (4), 927-79
Rojas-Perilla, N., Pannier, S., Schmid, T., & Tzavidis, N. (2019) Data-driven transformations in small area estimation. Journal of the Royal Statistical Society A.

Conference presentations


Würz, N., Schmid, T. and Tzavidis, N. (2019) Data-driven Transformations for the Estimation of Small Area Means. In: Deutsche Arbeitsgemeinschaft Statistik (DAGSTAT) Conference 18-22nd March 2019, LMU Munich, Germany
Browne. W.J. (2019) Developing a statistical analysis assistant for Small Area Estimation in StatJR. In: 12th International Multilevel Conference, 9-10th April 2019, Utrecht, The Netherlands
Würz, N., Schmid, T. and Tzavidis, N. (2019) Data-driven Transformations for the Estimation of Small Area Means. In: 6th ITAlian COnference on Survey Methodology (ITACOSM) Conference, 5th-7th June 2019, University of Florence, Italy
Tzavidis, N., Luna, A. Steele, J. and Nilsen, K. (2019) Small area estimation with mobile-data and remote-sensing covariate data. In: Invited presentation, International Statistical Institute (ISI) Conference, 18-23rd August 2019, Kuala Lumpar, Malaysia.


Tzavidis, N., Zhang, L.-C., Luna Hernandez, A., Schmid, T., and Rojas-Perilla, N. (2018) From start to finish: a framework for the production of small area official statistics. In: The Royal Statistical Society at a meeting organized by the Official Statistics Section, 9th May 2018

Other presentations


Browne. W.J. (2019) Borrowing Strength - a collaborative software development for Small Area Estimation. Talk to the ESRC NCRM showcase, University of Manchester, UK, 10th July 2019


As part of the project we created 3 videos related to the work of the centre on the StatJR software. These are firstly an introduction to StatJR, second a talk on eBooks and Statistical Analysis Assistants in StatJR and finally a talk on the more recent StatJR functionality to create bespoke training materials in SPSS. These three videos can be reached at the weblink

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