STATS-PHD - Statistics (PhD)
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Program Overview
The department looks for students who wish to prepare for either applied or theoretical research careers in statistics or probability. Advanced undergraduate or master’s level work in mathematics and statistics provides a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. The program typically takes five years to complete.
From the student’s arrival until the selection of a research advisor, the department’s Director of Graduate Studies (DGS) monitors the student’s academic progress. Each student should meet with the Doctoral Advisor at least once a quarter to discuss their academic plans and progress toward choosing a dissertation advisor. See Graduate Advising Expectations for more information.
This program is full time, doctoral students in the Statistics Department are expected to maintain a significant physical presence on campus throughout their graduate studies.This ensures that students have access to advisors and mentors, libraries, and other resources that support timely progress towards completion of degree requirements. Additionally, being on campus ensures that students are part of the academic and campus community which supports continued professional development.
Admissions Information
Prospective graduate students should see the Stanford Graduate Admissions website and the PhD Admissions Requirements and Procedures section of the department website for detailed information and application materials. Submission of general Graduate Record Examination (GRE) test scores is required. Submission of GRE Mathematics Subject Test scores is optional.
Minimum Units in the Program
Minimum University Units
course Theory of Statistics I, course Theory of Statistics II, and course Theory of Statistics III systematically survey the ideas of estimation and hypothesis testing for parametric and nonparametric models involving small and large samples.
course Applied Statistics I is concerned with linear regression and the analysis of variance.
course Applied Statistics II: Generalized Linear Models, Survival Analysis, and Exponential Families and course Applied Statistics III survey many modeling techniques related to but going beyond the linear models of course Applied Statistics I.
course Theory of Probability I, course Theory of Probability II, and course Theory of Probability III are measure-theoretic courses in probability theory, beginning with basic concepts of the law of large numbers and martingale theory.
All first-year courses must be taken for a letter grade where offered.
Students with insufficient mathematics background can take STATS 310 A,B,C after their first year but need to have their first-year program approved by the Director of Graduate Studies.
To complete the degree, students must also pass two of three parts of the qualifying examinations (end of the first year), and successfully complete the following: depth and breadth requirements (second, third, and fourth years), the dissertation proposal meeting (winter quarter of the third year); the university oral examination (fourth or fifth year); submission of the dissertation (fourth or fifth year). See details below.
Students must complete a depth requirement consisting of a minimum of three courses (nine units) of advanced topics courses offered by the department. Courses may be taken for a letter grade or credit. Courses for the depth and breadth requirements must equal a combined minimum of 24 units. Recommended advanced topics courses include, but are not limited to, those listed above.
Students must take at least three courses (nine units) outside the department. Courses may be taken for a letter grade or credit. They are advised to choose an area of concentration in a specific scientific field of statistical applications approved by their PhD program advisor. Courses for the depth and breadth requirements must equal a combined minimum of 24 units.
Popular areas include Computational Biology and Statistical Genomics, Machine Learning, Applied Probability, Earth Science Statistics, and Social and Behavioral Sciences.
Teaching is core to the academic and professional training of doctoral students in our program. As such, all students must complete at least four academic quarters of teaching, regardless of financial support, to fulfill degree requirements.
The teaching requirement should be completed by the end of the fourth year in the program. Typically, students complete more than the required number of teaching quarters as part of their financial support and professional training.
Qualifying Examinations
These are intended to test the student’s level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, and all are typically taken during the summer between the student’s first and second years. Students are expected to show acceptable performance in two examinations. A student who does not pass at least two examinations may be dismissed from the program. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students file for PhD candidacy, a university milestone.
Advancement to Candidacy
Advancement to candidacy for the doctoral degree is not an automatic process following the completion of program requirements such as qualifying exams. It is a prospective judgment by the department faculty of the student’s ability to conduct original research and their potential to complete the degree program’s requirements successfully. In the Department of Statistics, students are reviewed for candidacy in June of their second year.
The Application to Candidacy form must be submitted by the final study list deadline in spring quarter of the second year. Candidacy is valid for five years, during which students are expected to complete their degree.