George Mason University
Volgenau School of Engineering
Department of Statistics


STAT 652: Statistical Inference

Section 002

Spring Semester, 2020
Wednesdays from 7:20 to 10:00 PM (starting Jan. 22, with other dates given below)

Location: room 1110 of Nguyen Engineering Building


Instructor: Clifton D. Sutton

Contact Information (phone, fax, e-mail, etc.)
Office Hours: 6:15-7:00 & 10:00-10:30 PM on class nights (more information)


Text:

Statistical Inference, 2nd Ed. , by G. Casella & R. L. Berger (Duxbury, 2002)


Prerequisite:

a graduate level course in probability (STAT 544 or ECE 528) and exposure to basic statistical concepts (STAT 554)
(Note: You don't have to take STAT 554 before this course as long as you've had exposure in some course to statistical concepts such as estimation (including confidence intervals) and hypothesis testing (including null and alternative hypotheses, and p-values). However, the probability prerequisite is extremely important. Without current mastery of the material covered in STAT 544, this course will be extremely difficult.)


Description:

The main goal of this course is to introduce you to some of the basic ideas of statistical inference. A knowledge of probability theory will be assumed, the foundations of parametric statistical inference will be presented, and specific methods for estimation and hypothesis testing will be covered. The material presented in this course will serve to justify and enhance some of the concepts and methods covered in other statistics courses.

More specifically, here are some course goals: The general plan of attack to be used in an attempt to achieve these goals is as follows:

Approximate week-by-week content:

[1] Jan. 22:
exponential families; location and scale families
[Sections 3.4 and 3.5 of text]
(HW #1 posted)
[2] Jan. 29:
random samples; sums of random variables; sampling from normal distributions, sample minimums and maximums
[parts of Sections 5.1, 5.2, 5.3, and 5.4 of text]
[3] Feb. 5:
convergence concepts
[Section 5.5 of text]
[4] Feb. 12:
more on convergence concepts, the sufficiency principle
[Section 5.5 of text, Section 6.2 of text]
(HW #1 due; HW #2 posted)
[5] Feb. 19:
more on the sufficiency principle, the likelihood principle; the equivariance principle
[parts of Sections 6.2, 6.3, and 6.4 of text]
[6] Feb. 26:
point estimation methods
[Section 7.2 of text (subsection 7.2.4 won't be covered)]
(HW #2 due; HW #3 posted)
[7] Mar. 4:
more on point estimation methods; evaluation of point estimators
[Sections 7.2 and 7.3 of text (subsection 7.2.4 won't be covered)]
[**] Mar. 11:
No class due to Spring Break
[**] Mar. 18:
No class due to extended Spring Break
[8] Mar. 25
more on the evaluation of point estimators, UMVUEs
[Section 7.3 of text]
(HW #3 due; HW #4 posted)
[9] Apr. 1:
Pitman estimators and Bayesian estimation
[Subsections 7.2.3 and 7.3.4]
[10] Apr. 8:
hypothesis testing
[Section 8.2 of text (subsection 8.2.2 won't be covered) and Section 8.3 of text]
(HW #4 due; HW #5 posted)
[11] Apr. 15:
likelihood ratio tests
[Subsection 8.2.1]
[12] Apr. 22 :
confidence intervals
[Section 9.2 of text]
(HW #5 due; HW #6 posted)
[13] Apr. 29:
more on confidence intervals; asymptotics pertaining to point estimation
[Section 9.2 of text (and a bit about Sec. 9.3); Section 10.1 of text (subsection 10.1.4 won't be covered)]
(remainder of HW #6 posted)
[14] May 6:
asymptotic methods for hypothesis testing and confidence intervals
[Sections 10.3 and 10.4 of text]
(HW #6 due (no grace period))
[**] May 13:
Take-home Final Exam due at 10:15 PM

Grading:


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