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Education / Graduate Education / PhD Programs / Statistics / PhD in Statistics
 

PhD in Statistics

Other Available Graduate Programs
PhD in Statistics (bioinformatics concentration)
Master of Arts in Statistics
Master of Science in Biostatistics

 

Program Overview

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

Course work in statistics is concentrated in three areas - probability, inference, and data analysis. 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.

In general, the PhD program requires a minimum of four years of study, with five years of study being more common (see Timeline for Degree Completion). Prior to completion of the PhD, 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.

Prerequisites

Entering PhD students should have a strong background in mathematics, including three semesters of calculus (through multivariable calculus), a course in linear and/or matrix algebra, and a year of probability and mathematical statistics. A course in real analysis is encouraged; a course in statistical methods is also recommended. While some background in biology may be helpful for pursuing certain avenues of research, it is not required for admission to the program.

Expectations

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, seminars, and supervised research leading to a dissertation. There is no foreign language requirement. Computer expertise is developed in the program.

Students are expected to spend a minimum of 40 months and a maximum of 66 months, not necessarily continuously, engaged in one or more of the following activities that enhances their education and skill sets as statisticians: teaching assistantship, research assistantship, participation on the statistical consulting rotation, and summer internships.

Examinations

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.

Typical Program of Study

Year 1: Fall

  • Probability Theory (4 credits)
  • Statistical Inference I (4 credits)
  • Biostatistical Methods I (4 credits)
  • Introduction to Statistical Computing I (3 credits)
  • Ethics in Research (1 credit)

Year 1: Spring

  • Statistical Inference II (4 credits)
  • Biostatistical Methods II (4 credits)
  • Bayesian Inference (4 credits)
  • Linear Models (4 credits)

Year 2: Fall

  • High Dimensional Data Analysis (4 credits)
  • Generalized Linear Models (4 credits)
  • Advanced Bayesian Inference (4 credits) or Causal Inference (4 credits)
  • Introduction to SAS (1 credit)
  • Seminar in Statistical Literature (1 credit)
  • Supervised Teaching (2 credits)

Year 2: Spring

  • Analysis of Longitudinal and Dependent Data (4 credits) or Survival Analysis (4 credits)
  • Seminar in Statistical Literature (1 credit)
  • Reading Course(s) at the PhD Level
  • Elective(s)

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 who enter the program after 2019 are required to register for at least four 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:
    • Introduction to Spatial Data Analysis
    • Missing Data
    • Functional Data Analysis
    • Statistical Analysis of Cell Mixtures
    • Smoothing Methods
    • ROC Curve Analysis
    • The Bootstrap, the Jackknife, and Resampling Methods
    • Model Selection and Validation
    • Semiparametric Inference