Course Announcement 553.730: Statistical Theory Fall 2017 The fundamentals of mathematical statistics will be covered. Topics include: distribution theory for statistics of normal samples, exponential statistical models, the sufficiency principle, least squares estimation, maximum likelihood estimation, uniform minimum variance unbiased estimation, hypothesis testing, the Neyman-Pearson lemma, likelihood ratio procedures, the general linear model, the Gauss-Markov theorem, simultaneous inference, decision theory, Bayes and minimax procedures, chi-square methods, goodness-of-fit tests, and nonparametric and robust methods. Prerequisites: Probability Time: TTh 3-4:15 Section: F 3:00 Text: Bickel and Doksum, Mathematical Statistics, Vol I, 2/e, Updated Printing, 2007 * Be sure to get the 2007 "Updated Printing" version of the textbook! Instructor: Carey E. Priebe Clark 301D cep@jhu.edu ----- A natural follow-on course to 553.730 is ... 553.735: Topics in Statistical Pattern Recognition "Statistical pattern recognition" is my version of an explicitly prob/stat-centric "machine learning" or "data mining" course, following naturally from 553.730. Both theory and practice are stressed. -----