The doctoral program in statistics is designed to provide students with comprehensive training in statistical theory and methodology and in the application of statistical methods to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. The PhD program prepares students for careers as research statisticians in government or industry or as university teachers and researchers.
In addition to satisfying all the requirements of the MS program, students in the PhD program must take either Probability 1,2,3 (Math 450-1,2,3) or Probability I (Math 450-1) and Stochastic Models 1,2 (IE/MS 460- 1,2). A doctorate requires passing of a preliminary examination covering basic topics in statistics; this is normally taken in the second year of residence. Students must also pass a qualifying examination, normally taken in the third year of residence, on a topic of their choice. They then begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination is given based on the dissertation. In addition to these requirements, students are expected to participate in other research activities and seminars in the department.
Some Recent PhD Dissertations
The following gives the dissertation topics and current employer of recent PhD graduates.
- Contributions to multiple endpoints and dose finding
Brent Logan, 2001
Department of Biostatistics, Medical College of Wisconsin - Model selection for unbiased estimating equations
Xiangyang Liu, 2000
Johnson and Johnson - Automatic Bandwidth Selection and Data-Driven Estimators in a Semiparametric Regression Model
Shengyan Hong, 1998
Abbott Laboratories - Bayesian Model Comparison: Computation of the Marginal Likelihood
Joan Z. Yu, 1998
Risk Management, Household Credit Services - Inference for Exponential Order Statistics Models
Jason A. Osborne, 1997
Department of Statistics, North Carolina State University - Measures of Association and Regression Models for Ordinal Variables
Yi-Lin Chiu, 1997
Clinical Statistics, Abbott Laboratories - Estimation of Spectral Moments and the Classification of Spatial-Temporal Electrical Patterns during Ventricular Fibrillation
Jill R. Glassman, 1995
Intersystems, Inc., and Columbia University - Nonlinear Partial Least Squares
Edward C. Malthouse, 1995
Integrated Marketing Communications Department, Medill School of Journalism, Northwestern University - Extended Generalized Estimating Equations for Longitudinal Data
Daniel B. Hall, 1994
Department of Statistics, University of Georgia - Entropy-Based Prediction of Categorical Response Variables in Scanner Panel Data
Eric D. Nordmoe, 1993
Department of Mathematics, Kalamazoo College - Minimum Mean Square Error Estimation in Stratified Sampling
Jiahe Qian, 1993
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