George Mason University
Graduate Course Approval/Inventory Form
Please complete this form and attach a copy of the syllabus for new courses. Forward it as an email attachment to the Secretary of the Graduate Council. A printed copy of the form with signatures should be brought to the Graduate Council Meeting. Complete the
Coordinator Form on page 2, if changes in this course will affect other units.
____ MODIFY ____ Please indicate : ___X__ NEW
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Local Unit : AES Graduate Council Approval Date:
Course Abbreviation : STAT/IT/CSI Course Number : 972
Full Course Title : Mathematical Statistics I
Abbreviated Course Title (24 characters max.) : Math Stat I
Credit hours : 3 Program of Record : Ph.D. Statistical Science
Repeatable for Credit?
__ D=Yes, not within same term Up to hours total
___ T=Yes, within the same term
_X__ N=Cannot be repeated for credit
Up to hours
Activity Code (please indicate): _X__ Lecture (LEC) ___ Lab (LAB) ___
Recitation (RCT) ___ Studio (STU) ___ Internship (INT) ___ Independent
Study (IND) ____ Seminar (SEM)
Catalog Credit Format 3:3:0 Course Level : GF(500-600) ____ GA(700+)
__X__
Maximum Enrollment : 20 For NEW courses , first term to be offered: Fall 2007
Prerequisites or corequisistes: STAT 652 or equivalent
Catalog Description (35 words or less) Please use catalog format and attach a copy of the syllabus for new courses.
:
Focus on the theory of estimation. Principles of estimation are explored, including the method of moments, least squares, maximum likelihood, and maximum entropy methods.
Details methods of minimum variance unbiased estimation. Topics include sufficiency and completeness of statistics, Fisher information, Cramer-Rao bounds, Bhattacharyya bounds, asymptotic consistency and distributions, statistical decision theory, minimax and Bayesian decision rules, and applications to engineering and scientific problems.

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GEORGE MASON UNIVERSITY
Course Coordination Form
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Syllabus: STAT/IT/CSI 972 Mathematical Statistics I
This course is part of a two-course sequence.
This course is primarily on the theory of estimation. It begins with a brief discussion of probability theory, and then covers fundamentals of statistical inference. The principles of estimation are then explored systematically. Minimum variance unbiased estimation is covered in detail. Topics include sufficiency and completeness of statistics, Fisher information, bounds on variances, consistency and other asymptotic properties. Other topics and approaches in parametric estimation are covered in detail. Topics include the general formulation of statistical decision theory and optimal decision rules.
The text is Jun Shao (2003), Mathematical Statistics, second edition, Springer.
Material through Section 4.3 will be covered in 972 with the remainder in in 973.
Student work in the course (and the relative weighting of this work in the overall grade) will consist of

homework assignments (25)
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a midterm consisting of an in-class component and a take-home component (30)
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a final exam consisting of an in-class component and a take-home component (45)