Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
148 Amphitheater WayCharlottesville, Virginia 22904
Ph.D., University of North Carolina at Chapel Hill
Neutral-data Comparisons for Massive Multiple Testing in the Social Sciences
Source: U.S. NSF - Directorate Soc., Behav. & Eco. Science
May 15, 2013 – October 31, 2015
Main designs & estimation techniques used in sample surveys; including simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation; non-response problems, measurement errors. Properties of sample surveys are developed through simulation procedures. Uses SUDAAN software package for analyzing sample surveys.
This course includes an overview of parametric vs. nonparametric methods including one-sample, two-sample, and k-sample methods; pair comparison and block designs; tests for trends and association; multivariate tests; analysis of censored data; bootstrap methods; multifactor experiments; and smoothing methods. Prerequisite: STAT 1120 or STAT 2120
Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and direct the program.
Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 3120
This course provides the opportunity to offer a new topic in the subject area of statistics.
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R. Prerequisite: A previous statistics course, a previous linear algebra course, and permission of instructor.
Course provides an introduction to Bayesian methods with an emphasis on modeling and applications. Topics include the elicitation of prior distributions, deriving posterior and predictive distributions and their moments, Bayesian linear and generalized linear regression, and Bayesian hierarchical models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 6120, STAT 6190, and graduate standing in Statistics.
This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would be suitable for publication. The paper reviews literature relevant to a specialized research area, and possibly suggests an original research problem. Topics will vary from term to term.
This course introduces fundamental concepts in the classical theory of statistical inference. Topics include sufficiency and related statistical principles, elementary decision theory, point estimation, hypothesis testing, likelihood-ratio tests, interval estimation, large-sample analysis, and elementary modeling applications. Prerequisite: STAT 6190 and graduate standing in Statistics
For doctoral research, taken under the supervision of a dissertation director.