Surveys usually are designed to produce reliable estimates of various characteristics of interest for large geographic areas or socio-economic domains. However, for effective planning of health, social, and other services and for apportioning government funds, there has been a growing demand to produce similar estimates for small geographic areas and subpopulations, commonly referred to as small areas. This research project aims at developing a new method of small area estimation that potentially will lead to a dramatic improvement in accuracy over the traditional methods in practical situations. Model-based small area estimation utilizes statistical models, such as mixed effects models, to "borrow strength." In particular, the empirical best linear unbiased prediction (EBLUP) is a well-known model-based method that has had dominant influence in small area estimation. From a practical point of view, however, any proposed model is subject to model misspecification. When the proposed statistical model is incorrect, EBLUP is no longer efficient or even effective. In such cases, a new method, known as observed best prediction (OBP), may be superior. This project involves several important research topics on OBP, including theoretical developments, assessment of uncertainties under weak model assumptions, and implementation of the OBP via user-friendly software. The research largely will expand the results of our earlier studies, and contribute to making the OBP method more effective, practical, and easy to use.

The research introduces a completely new idea and method to model-based statistical methods in survey sampling. It is expected to impact other scientific areas where statistical methods have been used for prediction problems. The project will develop and freely disseminate R code to implement the OBP method. The education component of the project will introduce the OBP method into courses at the investigators' institutes. These courses are expected to draw students and researchers from statistics, biostatistics, genetic epidemiology, animal and plant sciences, educational research, social sciences, and government agencies. The project is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
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Cheryl L. Eavey
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Oregon Health and Science University
United States
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