Doctoral Program in Statistics
- Overview
- Program of Study
- Program Requirements
- Doctoral Students and Their Research
- Statistics Faculty
Overview of the Doctoral Program in Statistics
The world’s financial markets produce an enormous stream of data, and the understanding of the techniques needed to analyze and extract information from this stream has become critical. Doctoral work in statistics combines theory and methodology to deal with the large quantity of statistical data. Here at Stern we use the theoretical and methodological orientation of a traditional statistics with a focus on the applications that are central to the concerns of a business school. The PhD thesis work at Stern is a mathematically sophisticated enterprise that never loses sight of the real and practical problems of business.
Stern’s curriculum in statistics prepares students for academic positions by preparing them to conduct independent research. The statistician must be knowledgeable of the basic issues of the intellectual areas in which his or her work will be applied.
The most popular areas of student interest in the last few years have been mathematical finance, statistical modeling, data mining, stochastic processes, and econometrics.
Students have rigorous course work and participate in special topics seminars. They work closely with the faculty and also present special PhD student seminars.
Clifford Hurvich
Coordinator, Statistics Doctoral Program
Mission
Our mission is the education of scholars who will produce first-rate statistics research and who will succeed as faculty members at first-rate universities.
Admissions and performance
We enroll one or two students each year; these are chosen from approximately 100 highly qualified applicants.
Advising and evaluation
Each student will meet with a committee of faculty members yearly to assess progress through the program.
Research and interaction with faculty
The Stern statistics faculty have a wide range of interests, but there is special emphasis on time series, statistical modeling, stochastic processes, and financial modeling.
PhD students in statistics take courses in statistical inference, stochastic processes, time series, regression analysis, and multivariate analysis.
In addition to course work, doctoral students also participate in research projects in conjunction with faculty members. The students attend seminars, present seminars on their own work, and submit their work for publication.
The program culminates with the creation of the PhD thesis, through the stages of proposal, writing, and defense.
Most students finish in four to five years.
Statistics PhD students take their course work in the first two years of study. These courses are taken within the Statistics Group (both as formal courses and also as independent study), within other departments at the Stern School, at NYU's Courant Institute, and at Columbia University.
In addition to their statistics courses, doctoral students in Statistics often take courses in mathematics, finance, market research, and econometrics. The individual curriculum will be planned with the help of faculty advisers.