ST794-001
Advanced Statistical Inference - II
Spring Session, 2008
(a pdf version of syllabus)


Course:

ST794: Advanced Statistical Inference - II

Time:

TH from 10:15 to 11:30 a.m.

Place:

208 Patterson Hall

. .

Instructor:

Sujit Ghosh

Email:

ghosh@stat.ncsu.edu

Telephone

515-1950

Office:

220C Patterson Hall

Office hours:

Wednesday 3:00 - 5:00 p.m. or by appointment

. .

TA:

Mihye Ahn

Email:

mahn@ncsu.edu

Telephone:

513-2956

Office:

Statistics Tutorial Center, Bureau of Mines 110

Office hours:

Monday 3:00 - 5:00 p.m. or by appointment


Class links: Lectures & Assignments| Ask a question (use Message board)

Course prerequisite: ST793 and corequisite: ST778

Optional text: Jun Shao (2003). Mathematical Statistics, 2nd Edition. Springer Verlag. (ISBN:0387953825) Lecture Notes will be provided.

Homework: Homework will normally be assigned (as indicated on the homework page) at the end of class on Thursdays. Unexcused late homework will not be accepted. The final homework average will be computed after dropping the two lowest grades.

Examinations: Examinations will be closed book and closed notes. However students will be permitted to bring one 8.5 by 11 inch sheet of notes to the midterm exam and two to the final exam. The final exam will be cumulative, but weighted toward the materials covered after the midterm.

Exam schedule:
Midterm exam
Tuesday, Mar 11
4:30-6:30 p.m.
In-class
Syllabus: LN-I & LN-II
Final exam
Tuesday, May 1
8:00-11:00 a.m.
In-class
Syllabus: LN-I-IV

Asking questions: If you have questions about lectures, homework assignments, exams, procedures or any other aspect of the course please log onto http://courses.ncsu.edu/st794/, and click on "Message Board". Then click on "Post New Topic", enter your question in the Message box, and click on "Submit Message". You will receive a response from me or another student. Everyone in the class will be able see your question and the response.

Anonymous mail: If you wish to send me an anonymous suggestion or reminder, send email to st794-001-sup@wolfware.ncsu.edu. The system will remove mail headers, but you must remember to remove your signature and other identifying information.

Grading System: Final grade will be based on:

Final Semester Score = (4xHW + CP + 7xME + 8xFE)/20

where HW is the homework average (out of 100) after dropping the two lowest scores, CP is based on class participation and ME and FE are the scores (out of 100) on the midterm and the final exams, respectively. Grades will be assigned on the +/- scale.

Auditing: Auditors are expected to attend class regularly and submit homework on the same schedule as the other students. The final grade for auditors (AU or NR) will be based on their final homework average after dropping the two lowest scores. A homework score of 75 or better is required for an AU.

Policy on Academic Integrity: The University policy on academic integrity is spelled out in Appendix L of the NCSU Code of Student Conduct. For a more though elaboration see the NCSU Office of Student Conduct website. For this course group work on homework is encouraged. However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own.

Students with Disabilities: Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students (DSS), 1900 Student Health Center, CB# 7509, 515-7653.

Reference material (Have requested these be on reserve at DH Hill Library):


Bickel, P. J. and Doksum, K. A. (2001). Mathematical Statistics vol. I, 2nd Edition. Prentice Hall

Lehmann, E. L. (1997). Testing Statistical Hypotheses, 2nd Edition. Springer Verlag

Lehmann, E. L. and Casella, G. (2001). Theory of Point Estimation, 2nd Edition. Springer Verlag


Course objectives:

This course is the second semester of a two-course sequence, ST793-4, covering advanced statistical inference. The objective of this course, ST794, is to develop an advanced-level understanding and working knowledge of statistical inference.
The course provides an introduction to the rudiments of statistical inference for population parameters based on a general decision theoretic framework covering estimation and test of hypothesis. Some nonparametric methods will also be introduced (time permitting). Concepts, methods and theory are emphasized, rather than applications. Successful completion of this course will provide you with a foundation for understanding probability-based statistical inference material presented in other courses.

Students taking the course will have completed both ST793, and ST778.

Syllabus: In ST794 we shall complete the following concepts.
  • Statistical inferential framework: The decision theoretic set-up; views all models, parametric, semi-parametric, and non-parametric from a "coordinate free" point of view.
  • Performance criteria: Frequentist and Bayesian inference (developed side by side) based Admissibile, Minimax and Bayes procedures.
  • Sufficiency and estimation: convex loss functions, minimal sufficiency and Lehmann-Scheffe property, information inequalities, exponential families, Bayes estimation and Stein's paradox (optional)
  • Consistency and efficiency: consistency of approximate M-estimators, consistency of posteriors, efficiency of estimators, likelihood ratio tests (Wilk's theorem).
  • Nonparametric Inference: Empirical likelihood, density estimation (time permitting)

Last updated on: Jan 15, 2008