Basic and Applied Statistical Inference

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Over the last decade statistical data inference have played an important role in establishing the basic facts used for public policy, e.g. estimating the prevalence of HIV infection, determining the risk of cancer from occupational or environmental exposures, and in the legal setting, e.g. cases concerning employment discrimination. The purpose of this project is to study statistical problems arising in these contexts and to develop appropriate statistical methodologies for their solution. Research on procedures for estimating the prevalence of rare diseases which preserve anonymity by combining individual samples into batches prior to testing is a major goal. These methods should enable health specialists to obtain the cooperation of the public as infected individuals and non-infected persons who are misclassified (false positive) will not be identified. Robust methods which led to statistically valid inferences when several scientifically plausible models may underlie the data or when a few observations may be erroneous will continue to emphasized. In addition, statistical methods continue to play a role in the analysis of pay, promotion and hiring data in employment discrimination cases. In particular, alternative regression approaches will be analyzed that were used by parties in the Watson v. Fort Worth Bank case.