Stat 562, Spring 2014
Instructor: Bing Li; Telephone: 865 1952; Office: Thomas Building 410 Thomas Building.
Time & Place: MWF 11:15 pm – 12:05 pm at 105 Business Bldg.
Office hours: MWF 4:00 pm – 5:00 pm.
References:
1. Theory of Point Estimation, Erich Lehmann, Springer, 1997.
2. Approximation Theorems of Mathematical Statistics, Robert Serfling, John Wiley
& Sons, 1980.
3. A Course in Large Sample Theory, Thomas S. Ferguson, Chapman and Hall,
1996.
4. Quasi-likelihood and its application, Christopher Heyde.
5. Asymptotic Statistics, van der Vaart, Cambridge University Press, 1998.
6. Efficient and Adaptive Estimation for Semiparametric Models, Peter J. Bickel,
Chris A. J. Klaassen, Ya’acov Ritov, Jon A. Wellner. The Johns Hopkins University Press. 1993.
7. Tensor Methods in Statistics, Peter McCullagh. Chapman and Hall, 1987.
Examinations: There will be a midterm and a final exam. They will be closed book and in
class. You may bring a page of prepared notes for each exam.
Assignments: Homework will be assigned and graded every two weeks.
Evaluation: Midterm 30 %. Final 45%. Homework 25%.
Course coverage:
Preliminaries. Asymptotic Theory for Maximum Likelihood Estimation. Asymptotic Theory
for Estimating Equations. Asymptotic chi-square tests. Quasilikelihood, GLM, GEE. Local
Asymptotic Analysis of regular estimators. Local Asymptotic Analysis of regular tests. von
Mises expansion of statistical functionals.
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All Penn State and Eberly College of Science policies regarding academic integrity apply to
this course. See http://www.science.psu.edu/academic/Integrity concerning academic integrity for details.