The Ph.D. Program in Statistics

The program of study leading to the degree of Doctor of Philosophy in Statistics seeks to strike a balance between theoretical and applied Statistics. The Ph.D. program prepares you for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods.





Ph.D. Requirements

The Ph.D. requirements are:

• Intermediate Statistics: 36-705
• Regression Analysis: 36-707
• Advanced Probability Theory: 36-752
• Advanced Statistical Theory: 36-755
• Advanced Data Analysis: 36-757 & 36-758
• Statistical Computing: 36-711
• Data analysis exam

The requirements to earn the Ph.D. include two theoretical courses: Advanced Probability Theory, a rigorous treatment of probability and stochastic processes, and Advanced Statistical Theory, a rigorous treatment of statistical inference and decision theory. In the Advanced Data Analysis (ADA) Seminar each student identifies a project on which they work for a year. The ADA project is done in collaboration with an investigator from outside the Department, under the guidance of a faculty committee. It culminates in a publishable report that is presented orally and in writing. Statistical computing covers computational topics that arise in modern statistical practice. In addition, students take the "immigration to statistics" course, in which the students meet with the faculty and learn about their research interests.

In later semesters students continue to participate in the department by attending seminars, and taking further coursework. A variety of minis are offered every term that cover exciting topics in the field. Students are also encouraged to take courses in other departments to deepen their understanding of application areas.

The following is a template showing how a student could achieve the M.S. and Ph.D. degree in four years.

Year	Fall Semester	Spring Semester
1	36-699: Immigration to Statistics 36-707: Regression Analysis 36-705: Intermediate Statistics 10-701: Machine Learning or Two Half-semester "Mini" Courses	36-757: Advanced Data Analysis I 36-752: Advanced Probability 10-702: Statistical Machine Learning or Two Half-semester "Mini" Courses

Year	Summer
1	36-711: Statistical Computing Informal Continuance of ADA

Year	Fall Semester	Spring Semester
2	36-755 : Advanced Statistical Theory 36-758 : Advanced Data Analysis II	Work Towards Proposal

In the Spring of the second year, students will begin "reading and research" in pursuit of a thesis topic.  During this process students are expected to develop a formal relationship with a Ph.D. dissertation advisor in a timely manner. Following an oral dissertation proposal, the work on this research project is carried on intensively during the following two or three years.

M.S. Degree



Many of our Ph.D. students earn a Master of Science (M.S.) in Statistics on the way to achieving their ultimate degree. The M.S. degree is awarded as a milepost after a certain number of courses and hurdles have been achieved. The 2+4 program provides a flexible framework of requirements. It consists of the following mix of courses:

• 2 refers to Intermediate Statistics (36-705) and Applied Regression Analysis (36-707)
• 4 refers to 48 additional graduate credits (i.e. 4 courses) chosen from a variety of options. These classes must contain at least one element from each of the following categories:
  
  • A collaborative research experience such as Advanced Data Analysis (36-757 and 36-758), Statistical Practice (36-726), or an independent research project (provided it includes data analysis).
  • Statistical Methodology
  • Probability or Statistical Theory
• Students in our program seeking an M.S. degree must have a B or better in each of the courses under consideration for their 72 credits, an overall GPA of grater than 3.0, and they must pass the M.S. data analysis exam.
• The M.S. milepost does not have to occur in year 1, but we expect most students to earn this degree within 1.5 years.

 

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