Advances for Complex Surveys with Auxiliary Information and Missing Data

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The objective of this research is to develop a new conceptual framework together with an effective analytical approach for survey sampling. The new framework centers on explicitly linking survey sampling to missing data problems. Conceptually, inference from a survey sample is similar to that in a missing data problem: study variables are observed for subjects in the sample, but are missing for those outside the sample. This researcher will develop efficient estimation, by first constructing efficient estimators, for example, based on nonparametric maximum likelihood for missing data problems with independent and identically distributed data and then extending those estimators to survey sampling.Survey sampling is widely used for information gathering and analysis in various settings, including government agencies, academic institutions, and industries. This research will help to draw more accurate inferences than before from survey data. This will improve the cost-effectiveness of surveys and lead to better informed policy analysis and scientific investigation. Computer programs for implementing the methods will be made publicly available.