Inference from survey data and small area estimation

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Sample surveys are widely used to provide reliable statistics (estimates) on a variety of important topics (e.g., monthly unemployment rates and measures of health status of people). Measures of variability associated with these statistics are also needed in assessing their quality. Commonly used measures include standard errors and confidence intervals. Survey data are also extensively used to study relationships among variables and to test the validity of hypotheses. Surveys are increasingly used to provide small area statistics which are needed in formulating policies and programs.  My research program will focus on all the above aspects of sample surveys. In particular, I will study the following problems:(a) Traditional estimates for small areas, based on area specific data, are not reliable due to small sample sizes in small areas.  Indirect estimates that utilize information from related areas are therefore used to increase the reliability. I have done extensive work on this topic, particularly model-based estimates and associated standard errors. I propose to continue this work, focussing on realistic models, measurement errors, missing data and other features.(b) Item non-response often occurs in surveys and imputation is commonly used to handle missing data. Methods of making valid inferences under imputation will be studied.(c) Longitudinal surveys are widely used to study underlying dynamics and to get more efficient estimates. In the past, I have studied methods for cross-sectional surveys that take account of complex survey design features such as clustering and unequal weights. I will extend these methods to longitudinal surveys. These results will be applied to data from longitudinal surveys conducted by Statistics Canada.