MATH-503 Mathematical Statistics
Spring for 2016-2017
This is a first course in the mathematical theory of statistical inference. The emphasis is on classical methods, with appropriate attention also to Bayesian methods. Topics include principles of data reduction (sufficiency and sufficient statistics, likelihood, invariance); point estimation (method of moments, maximum likelihood, Bayes estimators) and associated criteria (mean squared error, unbiasedness, consistency); some asymptotic properties of point estimators; construction of and criteria for hypothesis tests (error probabilities and power, most powerful tests, bias); asymptotics of some large sample tests; construction of and criteria for interval estimate; and elements of decision theory and applications to statistical inference (Bayes rules, minimax).
Text: Statistical Inference, by G Casella and R Berger, Cengage Learning; 2nd edition (June 18, 2001).
Must be enrolled in one of the following Levels:
MN or MC Graduate
Must be enrolled in one of the following Majors:
Mathematics and Statistics
Prerequisites: MATH-501 or equivalent or consent of the instructor
The following syllabi may help you learn more about this course (login required):
Spring '17: Tadesse M (file download)
Additional syllabi may be available in prior academic years.
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