Efficient imputation and resampling methods for analyzing complex survey data

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Large scale complex surveys are routinely conducted by organizations such as Statistics Canada to gather critical information about the target population. Statistical analysis of survey data provides grounds for informed decisions on management strategies, development plans and policies that affect every aspects of our society. Survey data are also widely used by researchers to address issues in business, education, health, behaviour and other psychosocial and economical areas. Among the challenging issues related to analysis of complex survey data, missing data problems and variance estimation techniques have been two important areas for methodological research. This proposal extends methods that are used in current practice and develops new and efficient imputation methods for handling missing data and efficient resampling procedures for variance estimation. Our proposed methods build on the strength of currently used procedures, provide solutions to some of the problems and will likely change the way that public-use survey data sets are produced and how these data sets are analyzed. The proposed research topics also provide ample opportunities for graduate students to get involved and to develop ideas into their thesis research.