This course will present both theoretical and practical issues in statistics at an advanced mathematical level. It will attempt to integrate data analysis on the one hand with theoretical results from mathematical statistics on the other. The interrelationships among probability theory, mathematical statistics and data analysis will be highlighted throughout the course. Various approaches to estimation will be presented, including maximum likelihood theory and bayesian approaches. Specific statistical techniques covered will include generalized linear and additive modeling, hypothesis testing and modern computationally intensive techniques. Computer data analysis and Monte Carlo simulation of theoretical results on the iris workstations will be an integral part of the course. A background in probability theory and/or a previous course in statistics is strongly recommended. Evaluation will be primarily based on homework and projects. Prerequisites: Mathematics 243 or 341; and 105 and 211; or permission of instructor.
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