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
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|
|Select one of the following:||4|
|Analysis of Variance|
|Applied Regression and Design|
|Sampling and Categorical Data|
|Time Series Analysis|
|Multivariate Analysis (if not used to fulfill another requirement)|
|STAT 424||Analysis of Variance||4|
|or STAT 425||Applied Regression and Design|
|STAT 427||Statistical Consulting (or experience in applied statistics)||0-4|
|or STAT 593||STAT Internship|
|STAT 410/MATH 464||Statistics and Probability II (or equivalent proficiency)||4|
|Other requirements may overlap|
|A concentation is not required.|
|Minimum 500-level Hours Required Overall:||12|
Graduate College Courses
The Office of Undergraduate Research (OUR) and the Graduate College offer the opportunity for first and second-year undergraduate students to assist advanced graduate students with their research projects. Through a one-on-one research experience with their graduate student mentor, undergraduate students will have the opportunity to learn what it means to do research, create knowledge, and produce scholarship. This course will serve as a formal record of the graduate mentoring side of the apprenticeship (i.e., "practicum"). 0 graduate hours. No professional credit. Approved for S/U grading only. May be repeated.
For doctoral students who have a guaranteed student loan that needs deferral, have completed the credit requirements for the doctorate, have passed the preliminary examination, do not have any financial assistance that would cover tuition and fees, and are eligible to register for 599 in their own academic units. Approved for S/U grading only. May be repeated.
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.
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.
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.
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 CSE 542. Prerequisite: STAT 410 and STAT 425.
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.
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.
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.
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.
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.
May be repeated if topics vary. Prerequisite: Consent of instructor.
Directed reading and research. Approved for letter and S/U grading. May be repeated with approval. Prerequisite: Consent of instructor.
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.
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.
Approved for S/U grading only. May be repeated. Prerequisite: Consent of instructor.