About the Program Other Available Graduate Programs PhD in Statistics (bioinformatics concentration) MA in Statistics MS in Medical Statistics Program for the Degree of PhD in Statistics The doctoral program in Statistics is administered by the School of Medicine and Dentistry, and hence School of Medicine and Dentistry regulations apply. Entering PhD students need undergraduate preparation in mathematics, including mathematical analysis (advanced calculus) and linear algebra, and a year of probability and statistics. Normally, doctoral students are initially considered MA candidates; this non-thesis degree can be completed in three or four semesters or, in some cases, in one calendar year. PhD studies consist of additional specialized courses and seminars and supervised research leading to a dissertation. There is no foreign language requirement. Computer expertise is developed in the program. All MA/PhD students take a comprehensive examination at the beginning of the second year. PhD students take another written examination at the beginning of the third year. Both examinations cover material in the areas of probability, inference and data analysis. After beginning research on a dissertation topic, PhD students take an oral qualifying examination, consisting largely of a presentation of a thesis proposal to a faculty committee, the student's Thesis Committee. Upon completion of the dissertation, doctoral candidates present their work at a public lecture followed by an oral defense of the dissertation before the Thesis Committee. Prior to completing degrees, most students have some publications underway, including some work related to their dissertation research, possibly other methodological work done in collaboration with other members of the faculty, and often some applied papers with scientific researchers in other fields. In general, the PhD program requires a minimum of four years of study, with five years of study being more common. Course work in statistics is concentrated in three areas—probability, inference and data analysis. Beginning students should expect to spend all of their first year, most of their second year and some of their third year taking formal courses. The balance of time is spent on reading and research. Students entering with advanced training in statistics may transfer credits at the discretion of their advisor. A typical program for an entering student without previous training is as follows: Year 1: Fall BST 401 Probability Theory (4 credits) BST 411 Statistical Inference (4 credits) BST 464 Applied Linear Regression (4 credits) BST 487 Seminar in Statistical Literature (1 credit) BST 590 Supervised Teaching (2 credits) IND 501 Ethics in Research (1 credit) Year 1: Spring BST 426 Linear Models (4 credits) BST 466 Categorical Data Analysis (4 credits) BST 487 Seminar in Statistical Literature (1 credit) BST 520 Current Topics in Bioinformatics (4 credits) BST 590 Supervised Teaching (3 credits) Year 1: Summer BST 477 Introduction to Statistical Software I (0 credits) BST 478 Introduction to Statistical Software II (0 credits) Year 2: Fall BST 402 Stochastic Processes (4 credits) BST 479 Generalized Linear Models (4 credits) BST 450 Data Analysis (4 credits) BST 487 Seminar in Statistical Literature (1 credit) BST 590 Supervised Teaching (3 credits) Year 2: Spring BST 412 Large-Sample Theory and Methods (4 credits) BST 513 Analysis of Longitudinal and Dependent Data (4 credits) BST 531 Nonparametric Inference (4 credits) BST 487 Seminar in Statistical Literature (1 credit) BST 591 Reading Course at the PhD Level (3 credits) Year 3+ Mostly reading and research, with some 400-level and 500-level courses. Notes: Training in the use of statistical software (BST 477, BST 478) is offered during the first six weeks of the summer as a computing rotation (no formal credit). BST 487 Seminar (1 credit) is offered every semester. PhD students are required to register for at least six semesters. This course (1) provides students with experience in organizing, preparing, and delivering oral presentations, (2) introduces students to the process of searching the statistical literature, (3) enables students to acquire knowledge of a focused area of statistical research, and (4) introduces students to the research interests of members of the faculty. All PhD students are required to have at least four credits of supervised teaching and/or supervised consulting (BST 590, BST 592). Advanced courses listed as BST 511, 512, 550, or 570, for varying numbers of credits, are offered depending on interests of students and instructors. Recent examples include: Monte Carlo Methods and Modeling of Biomedical Dynamic Systems Permutation Tests Frailty Models High-Dimensional Data Analysis Statistical Methods in Epidemiology Smoothing Methods Introduction to ROC Methodology Statistical Inference Under Order Restrictions The Bootstrap, the Jackknife, and Resampling Methods Semiparametric Inference Program for the Degree of Master of Arts in Statistics The requirements for entry into the MA program are the same as those for entry into the PhD program. (See above.) The MA degree requires satisfactory completion of at least 32 credits and a final comprehensive written examination; no thesis is required. Of the 32 credits, at least 24 must be in departmental courses primarily at the 400 level or above, including at least one semester of BST 487. A typical program of study would include most of the courses shown above in Year 1 of the PhD program. A balanced program is worked out with the student’s advisor. The final comprehensive examination is administered during the summer following completion of coursework.