# Welcome to CSI 972 / STAT 972

## Fall, 2009

Instructor: James Gentle

Lectures: Tuesday, 4:30-7:10pm, Engineering Building, room 1103

Some of the lectures will be based on the instructor's notes posted on this website. Some lectures will be accompanied only by notes written on the board.

If you send email to the instructor, please put "CSI 972" or "STAT 972" in the subject line.

This course is part of a two-course sequence. The general description of the two courses is available at mason.gmu.edu/~jgentle/csi9723/

The prerequisites for the first course include a course in mathematical statistics at the advanced calculus level, for example, at George Mason, CSI 672 / STAT 652, "Statistical Inference", and a measure-theory-based course in probability, for example, at George Mason, CSI 971 / STAT 971, "Probability Theory".

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, beginning with a general formulation of statistical decision theory and optimal decision rules. 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.

The text is Jun Shao (2003), Mathematical Statistics, second edition, Springer.
Be sure to get the corrections at the author's website
A useful supplement is Jun Shao (2005), Mathematical Statistics: Exercises and Solutions, Springer. My assigned "exercises for practice and discussion" are all solved (or at least partially solved) in this book.
I will also use my Companion notes.
I plan to cover the material through Chapter 4 in the Companion during the fall semester for 972.
I plan to cover the remainder in 973.

One learns mathematical theory primarily by individual work; that is, by supplying the successive steps in solving a problem or proving a theorem. Some mathematical theory is learned and reinforced by passive activities such as reading or listening to lectures and discussions, and the assigned readings and weekly lectures are meant to serve this purpose. The reading assignments listed in the schedule below should be carried out with a pencil and paper in hand. The readings should be iterated as necessary to achieve a complete understanding of the material.

Student work in the course (and the relative weighting of this work in the overall grade) will consist of

• homework assignments (25)
• a midterm consisting of an in-class component and, possibly, a take-home component (30)
• a final exam consisting of an in-class component and, possibly, a take-home component (45)

Each homework will be graded based on 100 points, and 5 points will be deducted for each day that the homework is late. The homework assignments are long, so they should be begun long before they are due. Start each problem on a new sheet of paper and label it clearly. The problems do not need to be worked sequentially (some are much harder than others); when you are stuck on one problem, go on to the next one.

Each student enrolled in this course must assume the responsibilities of an active participant in GMU's scholarly community in which everyone's academic work and behavior are held to the highest standards of honesty. The GMU policy on academic conduct will be followed in this course.

Except during a period in which a take-home exam is being worked on, students are free to discuss homework problems or other topics with each other or anyone else, and are free to use any reference sources. Group work and discussion outside of class is encouraged, but of course explicit copying of homework solutions should not be done.

Students are not to communicate concerning exams with each other or with any person other than the instructor. On take-home exams, any passive reference is permissible (that is, the student cannot ask someone for information, but the student may use any existing information from whatever source).

For in-class exams, one sheet of notes will be allowed. The preparation of that sheet is one of the most important learning activities.

An approximate schedule is shown below. As the semester progresses, more details may be provided, and there may be some slight adjustments.
Notes are posted in a password-protected directory.
Students are expected to read the relevant material in the text prior to each class (after the first one).
Students are strongly encouraged to solve the "exercises for practice and discussion", especially those marked with an asterisk.

#### Week 1, September 1

Course overview; notation; etc.
How to learn mathematical statistics (working problems and remembering the big picture); "easy pieces".
Fundamentals of measure theory; sigma-fields, measures, integration and differentiation.
Fundamentals of probability theory: random variables and probability distributions, and expectation; important inequalities.
Reading assignments: Companion notes, Appendices A.1, A.2, B, and C, and Chapter 1, and Shao, Chapter 1.
Exercises for practice and discussion: In Shao Exercises 1.6: problems 12, 14, 30, 31, 36, 38, 51, 53, 55, 60, 70, 85, 91, 97, 128, 161
Assignment 1, due September 8: In Shao Exercises 1.6: problems 4, 5, 8, 18, 23, 43, 58, 63.

#### Week 2, September 8

Conditional expectation, joint distributions, and independence
Asymptotic properties
Limit theorems
Assignment 2, due September 15: In Shao Exercises 1.6: problems 78, 90, 101, 102, 103, 127, 158.

#### Week 3, September 15

Fundamentals of statistics.
Reading assignments: Companion notes, Chapter 2, and Shao, Chapter 2.
Exercises for practice and discussion: In Shao Exercises 2.6: problems 9, 13, 19, 23, 25, 30, 44, 56, 66, 74, 84, 93, 101, 115, 121
Assignment 3, due September 22: In Shao Exercises 2.6: problems 3, 4, 8, 20, 28.

#### Week 4, September 22

Decision theory, confidence sets, and hypothesis testing.
Assignment 4, due September 29: In Shao Exercises 2.6: problems 33, 63, 81, 116, 123.

#### Week 5, September 29

Asymptotic inference

#### Week 6, October 6

Midterm exam. Closed book and closed notes except for one sheet (front and back) of prewritten notes.
Sample from a previous year. (The coverage may be slightly different.)
Hand out midterm takehome. Due October 20
Between now and the end of class on October 20, students are not to discuss homework or other aspects of the course (including the takehome of course!) with anyone other than the instructor.

#### October 13

Class does not meet this week

#### Week 7, October 20

Review midterm.
Bayesian inference.
Reading assignments: Companion notes, Chapter 3.
Exercises for practice and discussion: In Shao: problems 4.2(a)(b), 4.13, 4.14, 4.15, 4.19(b), 4.27, 4.30, 6.105, 7.29
Assignment 5, due November 3: In Shao: problems 4.1(a)(b), 4.17, 4.18, 4.31, 4.32(a), 4.38(a)(b).

#### Week 8, October 27

Bayesian inference.

#### Week 9, November 3

Bayesian testing and credible regions.
Assignment 6, due November 10: In Shao: problems 6.106, 6.107, 7.28, 7.40.

#### Week 10, November 10

UMVUE, U statistics
Reading assignments: Companion notes, Chapter 4.
Exercises for practice and discussion: In Shao: problems 3.6, 3.19, 3.33, 3.34, 3.60, 3.70, 3.106, 3.107, 3.111
Assignment 7, due November 17: In Shao: problems 3.3, 3.16, 3.32(a)(b)(c), 3.35(a)(b)(c).

#### Week 11, November 17

Unbiased estimation
Assignment 8, due December 1: In Shao: problems 3.44, 3.52, 3.91, 3.109, 3.114.

#### Week 12, November 24

Unbiased estimation.
Maximum likelihood estimation.
Reading assignments: Companion notes, Chapter 5.
Exercises for practice and discussion: In Shao: problems 4.96(a)(g)(h), 4.107, 4.151, 5.20, 5.21
Assignment 9, due December 8: In Shao: problems 4.94, 4.95, 4.97, 4.109, 4.120, 4.152, 5.90

#### Week 13, December 1

Maximum likelihood.

#### Week 14, December 8

Maximum likelihood.

#### December 15

4:30pm - 7:15pm Final Exam.
Closed book and closed notes except for one sheet of prewritten notes.