Master of Science in Statistics, Applied Statistics Concentration

Douglas G. Simpson, Department Chair
101 Illini Hall, MC-374
725 South Wright Street
Champaign, IL 61820 USA
PH: 217-333-2167

The Department of Statistics offers the Master of Science in Statistics with specialization in a variety of areas of application. The degree program consists of a core of statistics courses covering statistical theory, linear models, and statistical consulting, and further coursework in the field of application and in statistics. The program offers an additional degree for students earning an advanced degree in the area of application.

To be eligible for this program, students must be pursuing an advanced degree in a department other than Statistics at the Urbana-Champaign campus. Students interested in economic statistics should apply for the applied concentration. Full statements of degree requirements are available from the head of the unit offering a specialization or from the Graduate Advisor of the Department of Statistics.

Five graduate courses must be completed in your primary field, in an area relevant to the field of Statistics.20
STAT 410/MATH 464Statistics and Probability II (or equivalent proficiency)4
STAT 425Applied Regression and Design4
or STAT 424 Analysis of Variance
STAT 427Statistical Consulting (or experience in applied statistics) 0-4
or STAT 593 STAT Internship
Select one of the following:4
Analysis of Variance
Applied Regression and Design
Sampling and Categorical Data
Statistical Computing
Time Series Analysis
Computational Statistics
Multivariate Analysis (if not used to fulfill another requirement)
Total hours32-36

Other Requirements1

Other requirements may overlap
A concentation is not required.
Minimum 500-level Hours Required Overall: 12
Minimum GPA: 3.0

STAT Class Schedule


STAT 510   Mathematical Statistics I   credit: 4 Hours.

Distributions, transformations, order-statistics, exponential families, sufficiency, delta-method, Edgeworth expansions; uniformly minimum variance unbiased estimators, Rao-Blackwell theorem, Cramer-Rao lower bound, information inequality; equivariance. Prerequisite: STAT 410.

STAT 511   Mathematical Statistics II   credit: 4 Hours.

Bayes estimates, minimaxity, admissibility; maximum likelihood estimation, consistency, asymptotic efficiency; testing and confidence intervals; Neyman-Pearson lemma, uniformly most powerful tests; likelihood ratio tests and large-sample approximation; nonparametrics. Prerequisite: STAT 510.

STAT 525   Computational Statistics   credit: 4 Hours.

Various topics, such as ridge regression; robust regression; jackknife, bootstrap, cross-validation and resampling plans; E-M algorithm; projection pursuit; all with a strong computational flavor. Same as CSE 525. May be repeated if topics vary. Prerequisite: STAT 425, STAT 426, and STAT 511; or consent of instructor.

STAT 534   Advanced Survival Analysis   credit: 4 Hours.

Introduction to the analysis of time-to-event outcomes. Topics include censoring, discrete survival, parametric models, nonparametric one- and K-sample methods, Cox regression, regression diagnostics, time-dependent covariates, and multivariate survival outcomes. Emphasis on key underlying concepts. Counting process-based theoretical justification and practical implementation will also be discussed. 4 graduate hours. No professional credit. Prerequisite: STAT 410, STAT 425, and knowledge of R.

STAT 538   Clinical Trials Methodology   credit: 4 Hours.

The topics of the course focus on clinical trials designs and inferential techniques that are commonly used in the pharmaceutical industry. Topics include fixed sample designs for normal and survival data, two-sided group sequential design, Pocock's and O'Brien-Fleming boundaries, general theory of group sequential design, alpha and beta spending functions, one-sided designs with early stopping to accept the null hypothesis, non-inferiority designs, and inferential techniques. Computing in SAS will be emphasized. 4 graduate hours. No professional credit. Prerequisite: STAT 410, STAT 425, and familiarity with SAS.

STAT 542   Statistical Learning   credit: 4 Hours.

Modern techniques of predictive modeling, classification, and clustering are discussed. Examples of these are linear regression, nonparametric regression, kernel methods, regularization, cluster analysis, classification trees, neural networks, boosting, discrimination, support vector machines, and model selection. Applications are discussed as well as computation and theory. Same as ASRM 551 and CSE 542. 4 graduate hours. No professional credit. Prerequisite: STAT 410 and STAT 425.

STAT 545   Spatial Statistics   credit: 4 Hours.

Theory and methods for analyzing univariate and multivariate spatial and spatio-temporal data. Covers both fundamental theories and cutting-edge research advances for geostatistics, and statistical methods for aggregated data and point processes. Real data examples will be provided in class and statistical software will be used to illustrate the data analysis. 4 graduate hours. No professional credit. Prerequisite: STAT 425 or equivalent.

STAT 553   Probability and Measure I   credit: 4 Hours.

Measures and probabilities; integration and expectation; convergence theorems and inequalities for integrals and expectations; independence; convergence in probability, almost surely, and mean; Three Series Theorem; laws of large numbers. Prerequisite: MATH 447 or consent of instructor.

STAT 554   Probability and Measure II   credit: 4 Hours.

Measure extensions, Lebesque-Stieltjes measure, Kolmogorov consistency theorem; conditional expectation, conditional probability, martingales; distribution functions and characteristic functions; convergence in distribution; Central Limit Theorem; Brownian Motion. Credit is not given for both STAT 554 and either MATH 561 or MATH 562.

STAT 571   Multivariate Analysis   credit: 4 Hours.

Inference in multivariate statistical populations emphasizing the multivariate normal distribution; derivation of tests, estimates, and sampling distributions; and examples from the natural and social sciences. Prerequisite: STAT 410 and MATH 415, or consent of instructor.

STAT 575   Large Sample Theory   credit: 4 Hours.

Limiting distribution of maximum likelihood estimators, likelihood ratio test statistics, U-statistics, M-, L-, and R-estimators, nonparametric test statistics, Von Mises differentiable statistical functions; asymptotic relative efficiencies; asymptotic expansions. Same as ECON 578. Prerequisite: STAT 511 and either MATH 561 or STAT 554.

STAT 578   Topics in Statistics   credit: 4 Hours.

May be repeated if topics vary. Prerequisite: Consent of instructor.

STAT 590   Individual Study and Research   credit: 0 to 8 Hours.

Directed reading and research. Approved for letter and S/U grading. May be repeated with approval. Prerequisite: Consent of instructor.

STAT 593   STAT Internship   credit: 0 to 8 Hours.

Supervised, off-campus experience in a field in which statistical science plays an important role. Approved for letter and S/U grading. Prerequisite: STAT 425 and consent of instructor.

STAT 595   Preparing Future Faculty   credit: 2 Hours.

Prepares Ph.D. students who are interested in an academic career to develop a successful academic career path, and to prepare graduate students for their future roles as teachers, and researchers. The course will focus on profession, job search, research, teaching and service. The course will involve guest panels, small and large group presentations and interactive Q&A with student participation.

STAT 599   Thesis Research   credit: 0 to 16 Hours.

Approved for S/U grading only. May be repeated. Prerequisite: Consent of instructor.