Instructor: James Gentle
Appointments for individual consultations can be made by email to the instructor.
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.
Recitations (optional): Monday, 7:30pm, Research Building I, room 92
During the optional recitation periods students and/or the instructor will discuss exercises, especially those listed as "for practice and discussion". The instructor may also discuss some of the class notes.
If you send email to the instructor, please put "CSI 973" in the subject line.
This course is the second part of a two-course sequence. The general description of the two courses is available at mason.gmu.edu/~jgentle/csi9723/
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.
See also the references listed in the
general description.
This course resumes where CSI 972 ended (which is at the end of Section 4.3 in Shao).
The course begins with a brief review of the general theory of statistical estimation, and estimation in parametric models. It then continues with maximum likelihood estimation and asymptotic properties of estimators in parametric models. Next, estimation in nonparametric models is covered. Hypothesis tests and confidence intervals are then covered.
I have put together a set of notes to supplement the material in the text and the lectures. These notes have a subject index that should be useful. (I am continually working on these notes, so they may change from week to week.)
Student work in the course (and the relative weighting of this work in the overall grade) will consist of
Each homework will be graded based on 100 points, and 5 points will be deducted for each day that the homework is late.
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 between when a take-home exam has been given out and when the exam is due, students are free to discuss the homework with each other or anyone else, and are free to use any reference sources. Explicit copying 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). During a period between when a take-home exam has been given out and when the exam is due, students are not to discuss with each other any aspect of the course -- homework, examples, or anything else relating to the course in any way. Any violation of this rule is a violation of the GMU Honor Code.
For in-class exams, one sheet of notes will be allowed.
An approximate schedule is shown below. As the semester progresses, more details will be provided, and there may be some slight adjustments.
EM examples. (Read general notes on optimization.)
The Bayesian approach; Bayesian estimation (review from MathStat_I, pp 77-95)
Reading assignment: Read Shao, Sections 4.1, 6.4.4, and 7.1.3.
Assignment 2, due Mar 3: In Exercises 4.6: 31, 32 (note typo for (b) and (c));
in Exercises 6.6: 106, 107;
in Exercises 7.6: 28, 29.
May turn in as late as March 7.
Comments.