General Description: This is an introduction of mathematical
statistics aimed at graduate students with a background of
univariate and multivariate calculus and elementary knowledge of probability
and statistics. If you have not taken a calculus or a probability class for
some time, you are advised to refresh your knowledge by reading a calculus
book that includes multivariate integration as well as Chapter 1 of
the textbook. The first third of the class consists of a review of some
topics of Probability Theory, that are important for statistical inference.
We use a level of formality that is above that of an upper division
probability class for science and engineering students, but without using
measure theory. The rest of the class will cover topics of classic
statistical inference. We will focus on the methods and principles of point
estimation and will browse through the methods for interval estimation and
hypothesis testing.
Textbook:
Statistical Inference, Second Edition. George Casella and Roger Berger,
Duxbury
Grading: There will be one midterm (30%, Tues., Nov. 06),
two quizes (10%, Thus., Oct. 18 and 10%, Tues., Nov. 20) and a final
exam (35%, Tues., Dec 11, 07:30-10:00 PM). Exams and quizes will be
based on the homework.
Homework: There will be regularly spaced homework, three of them will
be graded (5% each), the rest will not be graded. The solutions will
be discussed in class, some will be posted on the web page. Homework will give
a very close indication of the material that will be covered in exams
and quizes.