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Course information:

Instructor:  Ana-Maria Staicu [last name pronounced sty:ku]

Office: 5220 SAS Hall
Phone: 919 515-0644
Email: astaicu [at]  ncsu [dot] edu

Meeting information: Tu/Th 10:15-11:30AM, 1108 SAS Hall

This course is for students who have had a first year graduate level mathematical statistics course. It prepares them to handle statistical inference in a wide range of problems at an advanced level. (Prerequisites: ST 702). Required Texts: Essential Statistical Inference: Theory and Methods, 2013, Boos, D.D. and Stefanski, L.A.
The following chapters will be covered: Ch 2-3, Ch 5-11 and possibly Ch 12 (coverage is subject to change).

Software used in this course: R is freely available at http://www.r-project.org/. Download and install R. Go to http://cran.r-project.org/ and follow the instructions at the top of the page. You may also want to download R studio.

 

Schedule of lectures/homework assignments

First day of classes: Tuesday January 8

Week1: Ch 1. Introduction.  Ch2. Likelihood based methods. Illustration in R Correction/Clarification re the likelihood for the TypeI Left censoring
Week2: Ch 2. Likelihood fn (cont’d). Exponential Fam and canonical link fn. Likel principle. Pseudo likelihood
Week3: Ch 2. Pseudo Likel (cont’d). Fisher Information matrix. Methods to find MLE.
Week4: Ch 2.  EM logarithm. In-class notes
Week5: Ch 3. Hypothesis testing. Notes 02/07. Likelihood Testing in Linear Regression (material not covered in class)
Week6: Ch 3. Notes 02/12 (+extra). Starting Ch 5. Class example  correction: a.s. implies conv in probab
Week7: Ch 5. Markov, Chebyshev. WLLN. Continuity mapping thm. Scalar/multivariate Sutsky CLT AN. AN and conv in prob for vectors
Week8: Ch 5. Cramer Wold. Delta method. Example: asy distn of the sample correlation coeff

Past midterms: Midterm 2018 with solutions || Midterm 2017 with solutions

Problem session February 27 @ 5-7PM 5270 SAS Hall

Midterm 1 (February 28) covers Ch 2,3,5. Exam checklist Midterm solution Exam1 class performance. Scores: HW+midterm

Week9: Ch 5. Approx by avg. Avg of fns with estimated para Ch 6. Large sample: MLE and likel-based tests

Spring Break: March 11-15

Week10 Ch 6. Final part of score test+LR test asy distn
Week11 Ch 7. M estimation. Theoretical properties. Examples
Week12 Ch 8 Hypothesis testing under model misspecification (Wald, Score, LRT). Generalized Wald and Score tests.
Week13 Ch 9 Monte Carlo Study: notes
Week14 Ch 11. Bootstrap: notes

Problem session April 17 @ 5-7PM 5270 SAS Hall

Past finals: Final 2018 with solutions  || Final 2017 with solutions

Midterm 2 (April 18) covers: part of Ch 5, Ch 6-8, Ch 9 Exam checklist

Problem session April 17 @ 5-7PM 5270 SAS Hall

Last day of the semester Friday April 26

Grades (temporarily) here