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About the Program

Other Available Graduate Programs
PhD in Statistics (bioinformatics concentration)
Master of Arts in Statistics
MS in Medical Statistics

 

Program for the Degree of PhD in Statistics

The doctoral program in Statistics is administered by the School of Medicine and Dentistry, and hence School of Medicine and Dentistry regulations apply.

The program interprets the term “statistics” very broadly and permits specialization in probability, statistical theory and analysis, biostatistics, and interdisciplinary areas of application.

Entering PhD students should have a strong background in mathematics, including advanced calculus or mathematical analysis, a course in linear and/or matrix algebra, and a year of probability and mathematical statistics. A course in statistical methods is also recommended. Normally, doctoral students are initially considered MA candidates; this non-thesis degree can be completed in three semesters or, in some cases, in one calendar year. PhD studies consist of additional specialized courses and seminars and supervised research leading to a dissertation. There is no foreign language requirement. Computer expertise is developed in the program.

All MA/PhD students take a comprehensive (basic) examination at the beginning of the second year. PhD students take another written (advanced) examination at the beginning of the third year. Both examinations cover material in the areas of probability, inference and data analysis.

After beginning research on a dissertation topic, PhD students take an oral qualifying examination, consisting largely of a presentation of a thesis proposal to a faculty committee, the student's Thesis Committee. Upon completion of the dissertation, doctoral candidates present their work at a public lecture followed by an oral defense of the dissertation before the Thesis Committee.

Prior to completing degrees, most students have some publications underway, including some work related to their dissertation research, possibly other methodological work done in collaboration with other members of the faculty, and often some applied papers with scientific researchers in other fields. In general, the PhD program requires a minimum of four years of study, with five years of study being more common.

Course work in statistics is concentrated in three areas—statistical inference, statistical analysis  (theory and methods), and probability and stochastic processes. Beginning students should expect to spend all of their first year, most of their second year and some of their third year taking formal courses. The balance of time is spent on reading and research. Students entering with advanced training in statistics may transfer credits at the discretion of their advisor and in accordance with University policy. A typical program for an entering student without previous training is as follows:

Year 1: Fall

  • BST 401 Probability Theory (4 credits)
  • BST 411 Statistical Inference (4 credits)
  • BST 464 Applied Linear Regression (4 credits)
  • BST 487 Seminar in Statistical Literature (1 credit)
  • BST 590 Supervised Teaching (2 credits)
  • IND 501 Ethics in Research (1 credit)

Year 1: Spring

  • BST 413 Bayesian Inference (4 credits)
  • BST 426 Linear Models (4 credits)
  • BST 466 Categorical Data Analysis (4 credits)
  • BST 487 Seminar in Statistical Literature (1 credit)
  • BST 590 Supervised Teaching (3 credits)

Year 2: Fall

  • BST 402 Stochastic Processes (4 credits)
  • BST 479 Generalized Linear Models (4 credits)
  • BST 487 Seminar in Statistical Literature (1 credit)
  • BST 550 Topics in Data Analysis (4 credits) or Elective
  • BST 590 Supervised Teaching (3 credits)

Year 2: Spring

  • BST 412 Large-Sample Theory and Methods (4 credits)
  • BST 487 Seminar in Statistical Literature (1 credit)
  • BST 513 Analysis of Longitudinal and Dependent Data (4 credits)
  • BST 531 Nonparametric Inference (4 credits) or Elective
  • BST 591 Reading Course at the PhD Level (3 credits)

Year 3+ Mostly reading and research, with some 400-level and 500-level courses.

Notes:

  • BST 487 Seminar in Statistical Literature (1 credit) is offered every semester. PhD students are required to register for at least six semesters. This course (1) provides students with experience in organizing, preparing, and delivering oral presentations, (2) introduces students to the process of searching the statistical literature, (3) enables students to acquire knowledge of a focused area of statistical research, and (4) introduces students to the research interests of members of the faculty.
  • All PhD students are required to have at least four credits of supervised teaching and/or supervised consulting (BST 590, BST 592).
  • Advanced topics courses listed as BST 511, 512, 550, or 570, for varying numbers of credits, are offered depending on interests of students and instructors. Recent examples include:

     

    • Missing Data
    • Frailty Models in Survival Analysis
    • Causal Inference and Its Applications
    • Functional Data Analysis
    • Time Series
    • Smoothing Methods
    • ROC Methodology Curve Analysis
    • The Bootstrap, the Jackknife, and Resampling Methods
    • Advanced Bayesian Inference with an Emphasis on Computation
    • Model Selection and Validation