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Instructor Class Description

Time Schedule:

Jon A Wellner
STAT 581
Seattle Campus

Advanced Theory of Statistical Inference

Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability: (bootstrap and jackknife); robustness; estimation for dependent data, nonparametric estimation and testing. Prerequisite: STAT 513; either MATH 426 or MATH 576. Offered: A.

Class description

Use of limit theory in statistics: central limit theorems, modes of convergence, continuous mapping theorems, asymptotic linearity of statistics. Asymptotic normality of sample quantiles and some basic large sample theory for empirical distribution functions. Cramer-Rao efficiency bounds in the presence of nuisance parameters, and the geometry of efficienty estimation. Large sample theory of maximum likelihood estimtors and the related test statistics: Wald, Rao (or score), and likelihood ratio statistics.

Student learning goals

General method of instruction

Lecture with weekly homework sets.

Recommended preparation

Statistics 512-513 or equivalent. Math 424-425-426.

Class assignments and grading


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by Jon A Wellner
Date: 09/24/2002