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
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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.
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