PhD in Statistics

The PhD in Statistics prepares students professional leadership in statistical research, teaching and collaboration as faculty at colleges and universities and as researchers at government institutions or in the private sector. For further information and degree requirements please see the links below.

Course Requirements
Course Sequences
PhD Qualifying Examinations
Thesis Advisor
Preliminary Examination
Preliminary Examination Committee
Teaching Requirements
Doctoral Thesis and Defense Examination
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Course Requirements


  • MATH 447 Real Variables (*Waived if a course at an equivalent level has been taken at another institution and a grade of B or above is achieved)
  • MS Equivalent Core (16 credits)

  • STAT 424 Analysis of Variance
  • STAT 425 Applied Regression and Design
  • STAT 426 Sampling and Categorical Data
  • STAT 510 Mathematical Statistics I
  • Theory Core Courses (12 credits)

  • STAT 511 Mathematical Statistics II
  • STAT 553 Probability and Measure I
  • STAT 575 Large Sample Theory
  • Select one Practicum Course: (4 credits)

  • STAT 427 Statistical Consulting
  • STAT 593 STAT Internship
  • STAT 595 Preparing Future Faculty
  • Select one Computational Theory and Methods Course: (4 credits)

  • STAT 428 Statistical Computing
  • STAT 525 Computational Statistics
  • STAT 530 Bioinformatics
  • STAT 542 Statistical Learning
  • Select one of the Stochastic Processes and Time Series Courses: (4 credits)

  • STAT 429 Time Series Analysis
  • STAT 433 Stochastic Processes
  • STAT 554 Probability and Measure II
  • STAT 555 Applied Stochastic Processes
  • Select at least 3 elective courses not used above, from the list of electives below. At least two courses must be at the 500-level. (12 credits)

    Statistics Courses:

  • STAT 427 - Statistical Consulting
  • STAT 428 - Statistical Computing
  • STAT 429 - Time Series Analysis
  • STAT 430 - Topics in Applied Statistics
  • STAT 431 - Applied Bayesian Analysis
  • STAT 432 - Basics of Statistical Learning
  • STAT 433 - Stochastic processes (pending approval)
  • STAT 434 - Survival Analysis 1
  • STAT 440 - Data Management
  • STAT 448 - Advanced Data Analysis
  • STAT 458 - Math Modeling in Life Sciences
  • STAT 466 - Image and Neuroimage Analysis
  • STAT 525 - Computational Statistics
  • STAT 530 - Bioinformatics
  • STAT 534 - Advanced Survival Analysis
  • STAT 538 - Clinical Trials Methodology
  • STAT 542 - Statistical Learning
  • STAT 545 - Spatial Statistics
  • STAT 551/Math561 - Theory of Probability I
  • STAT 552/Math562 - Theory of Probability II
  • STAT 554 - Probability and Measure II
  • STAT 555 - Applied Stochastic Processes
  • STAT 571 - Multivariate Analysis
  • STAT 578 - Topics in Statistics (if the topic is different, it can be taken multiple times and counted as a different course)
  • STAT 587 - Hierarchical Linear Models
  • STAT 588 - Covariance Structures and Factor Models
  • STAT 593 - Internship
  • STAT 595 - Preparing Future Faculty
  • Approved elective courses offered by other departments (other courses subject to approval by the Ph.D. committee)

  • CS 512 - Data Mining Principles
  • CS 543 - Computer Vision
  • CS 546 - Machine Learning in NLP
  • CS 573 - Algorithms
  • CS 583 - Approximation Algorithms
  • ECE 547 - Topics in Image Processing
  • ECE 561 - Detection and Estimation Theory
  • ECE 563 - Information Theory
  • ECE 580 - Optimization by Vector Space Methods
  • ECON 536 - Applied Econometrics
  • ECON 574 - Econometrics I
  • ECON 575 - Econometrics II
  • ECON 576 - Time Series
  • ECON 590 - Applied Macroeconometrics
  • ECON 590 - Applied Financial Econometrics
  • IE 510 - Applied Nonlinear Programming
  • IE 521 - Convex Optimization
  • IE 528 - Computing for Data Analytics
  • IE 529 - Stats of Big Data & Clustering
  • MATH 540 - Real Analysis
  • MATH 580 - Combinatorial Mathematics
  • MATH 585 - Probabilistic Combinatorics
  • MATH 588 - Optimization in Networks
  • MATH 589 - Conjugate Duality and Optimization
  • Thesis and Individual Study Courses (0-32 credits)

  • STAT 590 Individual Study and Research (0 to 8 credits)
  • STAT 599 Thesis Research (0 min applied toward degree)
  • Total Hours 64

    Other Requirements

    Other requirements may overlap

    Masters Degree Required for Admission to PhD? No, but Masters level requirements must be met (32 additional hours min)

    Qualifying Exam Required? Yes

    Preliminary Exam Required? Yes

    Final Exam/Dissertation Defense Required? Yes

    Dissertation Deposit Required? Yes

    Minimum GPA: 2.75

    Course Sequences

    Students admitted without deficiencies (i.e., who are not on "Limited Status") take Statistics 425 and Statistics 510 in their first semester of study, and Statistics 424, Statistics 426, and Statistics 511 in the second semester. The student will be ready to take the Ph.D. qualifying exam after the first two semesters. The typical Ph.D. course sequence is as follows:

    First Year



    Statistics 425

    Statistics 424

    Statistics 510

    Statistics 426

    Math 447

    Statistics 511

    Second Year



    Statistics 429

    Statistics 427

    Statistics 525 or 571

    Statistics 428

    Statistics 553 or 578

    Statistics 554 or 575

    Third Year



    Statistics 553 or 578

    Statistics 554 or 575

    Additional courses

    Additional courses

    Students who have taken real analysis previously may waive Mathematics 447 with approval from the PhD program director.

    International students are expected to pass the SPEAK/TSE exam on campus during their first year as Ph.D. students (see Teaching Requirements). Because teaching is fundamental to both financial support and career development, international students who do not pass the SPEAK exam by January of the second year are subject to a reduction in financial aid.

    Qualifying Examinations

    A Ph.D. qualifying exam is offered once each year, at the beginning of the Fall semester. The exam covers material in STAT 424, 425, 426, 510, and 511. It consists of two four-hour exams, given on two different days. There are approximately two problems per course, for a total of about ten problems. The exams are interchangeable, i.e. there could be questions relating to any course on either or both exams.

    Every eligible Ph.D. student is required to take the Qualifying exam in the Fall semester after the first full year. A student receiving a passing score on the exam becomes a Ph.D. candidate and maintains regular progress towards the Ph.D. degree. A students who does not achieve a passing score will have one of two possible outcomes: (1) near passing, allowed to make a second attempt the following year, or (2) terminal non-passing score.

    Reading List for Qualifying Examinations

    * Primary text(s)

    Thesis Advisor

    Soon after passing the qualifying examination, the student should find a faculty member who will agree to be the student's thesis advisor. The student and advisor will then plan a course of study, including course work, outside reading, and original work, leading to the preliminary examination. Emphasis can be on, for example, mathematical statistics, computational statistics, applied or theoretical probability, methodology, or statistical applications in another discipline.

    Preliminary Examination

    During the first two years of graduate study, the student should be thinking seriously about what area of statistics to concentrate in, so that upon completing the qualifying examinations, work can begin toward the preliminary examination. The preliminary examination is frequently an oral presentation of the proposed thesis topic.

    Preliminary Examination Committee

    The Preliminary Examination Committee consists of at least four faculty members, not all of whom need to be in the Department of Statistics. The committee must be approved by the Graduate Advisor of the Department of Statistics, ad well as the Graduate College. The student prepares a written report to be presented to the members of the Committee at least two weeks before the Preliminary Exam. The Preliminary Exam itself consists of a short presentation by the student followed by questions from the members of the Committee. The Committee then has three choices: pass the student, fail the student, or postpone their decision with an indication to the student of what further work must be accomplished to satisfy the Committee. Since failure means that the Committee believes that the chances for success are very slim, only under extraordinary circumstances will a failed student be allowed to retake the Preliminary Exam. A pass means the student is eligible to begin thesis work.

    Teaching Requirements

    Each student in the Ph.D. program is required to teach a statistics or mathematics course for at least one semester. Before serving as a teaching assistant (TA), a graduate student whose native language is not English must attain a minimum score of 50 on the SPEAK or TSE and attend the International and All Campus Teaching Assistants Orientation Program. All Ph.D. students are required to attend the All Campus Teaching Assistants Orientation and participate in two post-orientation workshops held during the semester. Students who do not complete this requirement within one year of their admission will see a reduction in the percentage of their assistantship.

    Doctoral Thesis and Defense Examination

    The thesis is written under the supervision of the student's faculty advisor. It must consist of original work, presumably an outgrowth of the preliminary work. A thesis examination committee consisting of at least four faculty members, appointed by the Graduate College at the request of the Department of Statistics, reads the thesis. The student is examined orally by this committee during the defense examination. The committee members should be given sufficient time to study the thesis prior to the examination.

    After the defense examination had been passed, copies of the thesis, whose format and physical appearance have been approved by the Department of Statistics and the Graduate College, are to be submitted to the Thesis Office of the Graduate College for final approval.

    Additional PhD Program Policies

    Annual Review of Ph.D. Candidates

    The Graduate Committee of the Department of Statistics conducts an annual review of each candidate's progress toward completion of the Ph.D. degree. Any candidate whose progress is not satisfactory is subject to dismissal from the program. The following guidelines, in addition to the course requirements, will be used in measuring a candidate's satisfactory progress. The student must:

    Departmental Seminars

    The Department of Statistics sponsors seminars where researchers from academia or industry discuss their recent research. Each student enrolled in the Ph.D. program is expected to attend the seminars. Participation in the seminar series is one aspect given consideration by the Graduate Committee in its annual review of the student's performance.

    Graduate Course GPA Requirement

    In order to earn an M.S. or Ph.D. degree in Statistics, the candidate must maintain an overall minimum grade point average of 3.0 (A=4.0) in the course work completed.

    Changing from Master's to Ph.D. Program

    Students in the Master's degree program who wish to enter the Ph.D. program can apply through the regular admission process or through the MS to Ph.D. Transfer program.

    Applications submitted through the regular admission process will be reviewed during the traditional admission periods of the Ph.D. program. Applications to the Ph.D. program are due December 15 of each year for the following fall term.

    For the MS to Ph.D. Transfer program, current MS students who meet the eligibility requirements outlined below may apply for the transfer during the review period, which will be a 2–3 week period each May–June. Details will be emailed out to students shortly after the end of the spring semester and final grades have been reported. The Ph.D. Admission Committee will review all applicants with decisions made in mid-June. Further details will be emailed out each year.

    Eligibility Requirements:

    1. a) Overall accumulative GPA of 3.85 or higher for the entirety of your UIUC MS study, including the prior semester of applying.
    2. b) Successfully completed a minimum of 3 STAT500 level courses with a final grade of an 'A' or higher.
    3. c) Must be a current MS student at the time of applying for the transfer program. MS students who graduate in May prior to the transfer application period are ineligible and should apply to the Ph.D. program through the traditional application system.

    There is no application fee for the MS to Ph.D. Transfer program.

    Application materials submitted with your MS application will be used to evaluate your request to transfer to the Ph.D. program. You will be permitted to supply additional and updated materials at the time of applying for the transfer program.