Statistics, PhD

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

  • analysis of observational studies;
  • Bayesian inference, bioinformatics;
  • decision theory;
  • game theory;
  • high dimensional inference;
  • information theory;
  • machine learning;
  • model selection;
  • nonparametric function estimation; and
  • time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

View the University’s Academic Requirements for PhD Degrees.

The total course units required for graduation is 13.

Core Requirements
STAT 927Bayesian Statistical Theory and Methods1
STAT 930Probability1
STAT 931Stochastic Processes1
STAT 961Statistical Methodology1
STAT 970Mathematical Statistics1
STAT 971Introduction to Linear Statistical Models1
STAT 972Advanced Topics in Mathematical Statistics1
Electives 1
Select six course units6
Total Course Units13
1

Electives must include suitable courses numbered 900 and above, when offered.


The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2018 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Sample Plan of Study

First Year
Fall
Probability
Statistical Methodology
Mathematical Statistics
Spring
Bayesian Statistical Theory and Methods
Stochastic Processes
Introduction to Linear Statistical Models
Summer
Qualifying Examination and First Year Paper
Second Year
Fall
Advanced Topics in Mathematical Statistics
Two Electives
Spring
Three Electives
Summer
Second-Year Paper
Third Year
Fall
Directed Study Course
Two Electives
Oral Exam/Thesis Proposal
Spring
Electives or Directed Study Units
Fourth Year and Beyond
Directed Study and Dissertation Research