Collaborative Research: Computation-driven small area inference with applications

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).There is a growing demand to assess various measures of human well-being (including socio-economic and health characteristics) at national and sub-national levels. However, data availability at the sub-national level is often limited owing to constraints such as costs of data collection, natural limitations, confidentiality and other ethical issues. This research aims to extract relevant information for predictive purposes from several data sources and provide sound predictions under limited-data conditions. Since small area statistics are routinely used by a diverse group of stakeholders, there is an urgent need to develop different reliable risk measures - this is the prime focus of this project. More specifically, a unified computational method will be developed to estimate different risk measures across a broad spectrum of statistical models, methods of estimation of model parameters, types of data, and symmetric or asymmetric loss functions. The proposed research will replace complicated analytical formulae, often hard or impossible to obtain, by simple efficient techniques that use the efficiencies of high power computing. Extensive evaluation of the proposed techniques will be carried out using computer-generated and complex survey datasets. This research will extend the scope of small area statistics to several applications of public interest and will strengthen related educational programs.