Graduate Course List

STA 5066 Data Management and Analysis with SAS (3)

This course introduces SAS software in lab-based format. SAS is the world's most widely used statistical package for managing and analyzing data. The objective of this course is for the student to develop the skills necessary to address data management and analysis issues using SAS. This course includes a complete introduction to data management for scientific and industrial data, an overview of SAS statistical procedures including statistical graphics, an introduction to SAS's macro capabilities for automating repeated analyses, and an introduction to IML Plus, SAS's recently released interface to its interactive matrix language.
Prerequisite: Some exposure to introductory statistics or instructor permission.

 

STA 5067 Advanced Data Management and Analysis with SAS (3)

This course is a sequel to STA5066, Data Management and Analysis with SAS and assumes knowledge of the material provided in that course. The course will present additional methods for managing and analyzing data with the SAS system. It covers as many of the following topics as time permits. Advanced Data step Topics, Manipulation of Data with Proc SQL. The SAS Macro Facility, Analyses with Proc IML.

 

STA 5106 Computational Methods in Statistics I (3)

Matlab and a programming language (C/Fortran) will be used. Floating point arithmetic, numeric matrix analysis, multiple regression analysis, nonlinear optimization, root finding, numerical integration, Monte Carlo sampling.

Prerequisite: At least one previous course in statistics above STA 1013; some previous programming experience; or instructor permission.

 

STA 5107 Computational Methods in Statistics II (3)

Matlab and a programming language (C/Fortran) will be used. A continuation of STA 5106 in computational techniques for linear and nonlinear statistics. Statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, Gibbs sampling.

Prerequisite: STA 5106 or instructor permission.

 

STA 5126 Introduction to Applied Statistics (3)

Data collection, sample variation, basic probability, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation, regression, nonparametric statistics.

Prerequisite: MAC 1105

 

STA 5166 Statistics in Applications I (3)

Comparison of two treatments, random sampling, and randomization and blocking with two comparisons, statistical inference for means, variances, proportions and frequencies, and analysis of variance.

Prerequisite: MAC 2313

 

STA 5167 Statistics in Applications II (3)

Special designs in analysis of variance, linear and nonlinear regression, least squares and weighted least squares, case analysis, model building, non least squares estimation.

Prerequisite: STA 5166

 

STA 5168 Statistics in Applications III (3)

Response surface methods, repeated measures and split-plot designs, basic log-linear and logit models for two-way and multiway tables, and multinomial response models.

Prerequisite: STA 5167

 

STA 5172 Fundamentals of Biostatistics (3)

This course introduces students to the statistical methods used in studying the prevention of disease in human populations.

Prerequisite: A previous course in statistics or instructor permission.

 

STA 5176 Statistical Modeling with Applications to Biology (3)

Maximum likelihood principle, missing data and EM algorithm; assessment tools such as bootstrap and cross-validation; Markov chain and hidden Markov models; classification and regression tress (CART); Bayesian models and Markov Chain Monte Carlo algorithms..

Prerequisite: STA 4442 or 5440

 

STA 5179 Applied Survival Analysis (3)

This course is an applied introduction to survival analysis, one of the most commonly used analytic tools in biomedical studies. Topics to be covered include censoring and time scale, descriptive methods, parametric methods, and regression methods, which stress the proportional hazards model.

Prerequisite: STA 2171 or instructor permission.

 

STA 5198 Epidemiology for Statisticians (3)

This course covers fundamental methods of epidemiology for statisticians. With a focus on identification of risk factors for disease, topics include exposure-disease association, design of cohort, matched and randomized studies; cross-sectional and longitudinal studies; statistical analysis of data arising from such studies, confounding, adjustment and causality; and evaluation of diagnostic and screening tests.

Prerequisite: Material equivalent to STA2171 or instructor permission.

 

STA 5206 Analysis of Variance and Design of Experiments (3)

One and two-way classifications, nesting, blocking, multiple comparisons, incomplete designs, variance components, factorial designs, confounding.

Prerequisite: One of STA 2122, 4322, 5126 or 5354.

 

STA 5207 Applied Regression Methods (3)

General linear hypothesis, analysis of covariance, multiple correlation and regression, response surface methods.

Prerequisite: One of STA 2122, 4322, 5126, or 5354.

 

STA 5208 Linear Statistical Models (3)

Prerequisite: STA 5327

 

STA 5225 Sample Surveys (3)

Simple stratified, systematic, and cluster random sampling. Ratio and regression estimation. Multistage sampling.

Prerequisite: A course in statistics above STA 1013 or instructor permission.

 

STA 5238 Applied Logistic Regression (3)

This course is an applied introduction to logistic regression, one of the most commonly used analytic tools in biomedical studies. Topics include fitting the model, interpretation of the model, model building, assessing model fit, model validation, and model uncertainty.

Prerequisite: Prerequisite: STA 2171

 

STA 5244 Clinical Trials (3)

This course offers an introduction to clinical trials. Topics to be covered include defining the research question, basic study designs, randomization, blinding, sample size, baseline assessment, data collection and quality control, monitoring, issues in data analysis, closing out a trial, reporting and interpreting results, and issues in multicenter trials.

Prerequisite: Material equivalent of STA2171 or instructor permission.

 

STA 5323 Introduction to Mathematical Statistics (3)

Distributions of random variables, conditional probability and independence, multivariate distributions, sampling distributions, Bayes' rule, counting problems, expectations.

Prerequisite: MAC 2313 or equivalent.

 

STA 5325 Mathematical Statistics (3)

Sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian models.

Prerequisite: STA 4442 or 5440 and either MAC 2313 or STA 5326.

 

STA 5326 Distribution Theory and Inference (3)

Introduction to Probability, random variables, distributions, limit laws, conditional distributions, and expectations.

Prerequisite: MAC 2313; at least one previous course in statistics or probability.

 

STA 5327 Statistical Inference (3)

Preferred Description: Sufficient statistics, complete statistics, ancillary statistics, method of moments, maximum likelihood estimation, Bayesian estimation, minimum variance unbiased estimates, Fisher information, asymptotic behavior of maximum likelihood estimates, hypothesis testing, the Neyman-Pearson lemma, and likelihood ratio tests. (Current Bulletin description: Statistical Inference viewed at a measure-theoretic level.)

Prerequisite: Preferred prerequisites: STA 5326 or equivalent. (Bulletin prerequisites: STA 5326,5446.)

 

STA 5334 Limit Theory of Statistics (3)

Convergence of distribution and random variables, laws of large numbers, central limit theorems, asymptotic distributions, asymptotic efficiency, rates of convergence, the weak invariance principle.

Prerequisite: STA 5327

 

STA 5440 Introductory Probability I (3)

Random variables, probability of random variables, generating functions, central limit theorem, laws of large numbers.

Prerequisite: MAC 2311

 

STA 5446 Probability and Measure (3)

Classes of sets, probability measures, construction of probability measures, random variables, expectation and integration, independence and product measures.

Prerequisite: MAA 4227, 5307, or the equivalent.

 

STA 5447 Probability Theory (3)

Prerequisite: STA 5326, STA 5446

 

STA 5507 Applied Nonparametric Statistics (3)

Applications of nonparametric tests, estimates, confidence intervals, multiple comparison procedures, multivariate nonparametric methods, and nonparametric methods for censored data.

Prerequisite: A course in statistics above STA 1013 or instructor permission.

 

STA 5635 Applied Machine Learning (3)

This course is a hands-on introduction to statistical methods for supervised, unsupervised, and semi-supervised learning. It explores fundamental techniques including but not limited to Support Vector Machines, Decision Trees, Linear Discriminant Analysis, Random Forests, Neural Networks, and different flavors of Boosting.

Prerequisite: Prerequisite: STA 3032 or instructor permission.

 

STA 5666 Statistics for Quality and Productivity (3)

Statistics for quality control and productivity; graphical methods; control charts; acceptance sampling; design and experiment for product and process improvement.

Prerequisite: STA 5167 or instructor permission, and either STA 4322 or 5126.

 

STA 5676 Reliability Theory and Life Testing (4)

Prerequisite: A basic course in probability and statistics.

 

STA 5707 Applied Multivariate Analysis (3)

Inference about mean vectors and covariance matrices, canonical correlation, principal components, discriminant analysis, cluster analysis, computer techniques.

Prerequisite: One of STA 5167, 5207, or 5327.

 

STA 5721 High-Dimensional Statistics (3)

This course covers a range of modern statistical topics in high dimensional modeling and analysis. The course teaches methods, theory and computation with rich high-dimensional data applications from signal processing, machine learning, bioinformatics and econometrics.

Prerequisite: One of STA 5167 and 5326. STA 5168 is also recommended.

 

STA 5746 Multivariate Analysis (3)

Prerequisite: STA 5327

 

STA 5807r Topics in Stochastic Processes (3)

Prerequisite: STA 5326

 

STA 5856 Time Series and Forecasting Methods (3)

Autoregressive, moving average and mixed models, autocovariance and autocorrelation functions, model identification, forecasting techniques, seasonal model identification estimation and forecasting, intervention and transfer function model identification, estimation and forecasting.

Prerequisite: STA 5126, QMB 3200, or equivalent.

 

STA 5906r Directed Individual Study (1-12)

Special Note: (S/U grade only). May be repeated.

 

STA 5910r Supervised Research (1-6)

Special Note: (S/U grade only.) May be repeated to a maximum of twelve (12) semester hours. A maximum of three (3) hours may apply to the master's degree

 

STA 5920r Statistics Colloquium (1)

Special Note: (S/U grade only.) May be repeated to a maximum of twelve (12) semester hours.

 

STA 5934r Selected Topics in Statistics, Probability, or Operations Research (2-3)

Special Note: May be repeated to a maximum of twelve (12) semester hours.

 

STA 5938 Topics in Medical Consulting (3)

This is a "hands-on" course in consulting. Two to four reasonably complex problems are identified each time the course is offered, the investigators present the problem to the class. Statistical topics covered in class are those identified by the class as required to solve the problems presented.

Prerequisite: STA 2171 or permission of instructor.

 

STA 5939 Introduction to Statistical Consulting (1-3)

Formulation of statistical problems from client information; the analysis of complex data sets by computer; practical consulting experience.

Prerequisite: STA 5167 or 5327.

 

STA 5940r Supervised Consulting (1-3)

Special Note: (S/U grade only.) May be repeated to a maximum of twelve (12) semester hours. A maximum of three (3) hours may apply to the master's degree.

 

STA 5941r Supervised Teaching (1-5)

(S/U grade only.) May be repeated to a maximum of five semester hours. A maximum of three hours may apply to the master's degree.

 

STA 6174r Advanced Methods in Epidemiology (3)

This course presents advanced methods for describing, analyzing, and modeling data from observational studies. The initial offering includes introductions to meta-analytic methods, bootstrap methods, and randomization tests. Topics vary with each offering. May be repeated up to a maximum of six semester hours.

Prerequisite: STA 5167 and 5325 or instructor permission.

 

STA 6246r Advanced Topics in Applied Statistics ()

 

STA 6346 Advanced Probability and Inference I (3)

The course covers the basics of the probability theory, random elements, and stochastic processes; characteristic functions and probability inequalities; central limit theorems; elements of Markov dependence and martingale theory; common scholastic processes arising in biostatistics; advanced treatment of sufficient statistics, exponential families, estimation, and testing; as well as elements of asymptotic theory of statistical inference.

Prerequisite: STA 5326 and STA 5327

 

STA 6448 Advanced Probability and Inference II (3)

The course covers unbiased and locally most powerful tests (including the multiparameter case); envelope power function; best average power test; Bayes and empirical Bayes procedures; likelihood, quasi likelihood, and profile likelihood; order statistics and empirical distributions; general central limit theorems; variance stabilizing transformations; U-statistics; least squares, weighted least squares, and generalized least squares estimation; generalized estimating equations; asymptotic theory for BAN estimators; asymptotic theory for likelihood ratio, Wald, and score tests; log-linear models; asymptotics for linear inference; as well as robust statistical inference.

Prerequisite: STA5326 and STA5327

 

STA 6466 Advanced Probability (3)

Prerequisite: STA 5447

 

STA 6468 Advanced Topics in Probability and Statistics (2-3)

Special Note: May be repeated to a maximum of twelve (12) semester hours.

 

STA 6555 Nonparametric Curve Estimation (3)

Estimation of regression and density functions and their derivatives where no parametric model is assumed. Kernel, local polynomial, spline, and wavelet methods. Emphasis on analysis and applications of the smoothing techniques, and data-based smoothing parameter selectors.

Prerequisite: STA 5327 or instructor permission.

 

STA 6557 Object Data Analysis (3)

This course covers the most inclusive type of data analysis known in statistics; examples of such data in astronomy, biology, digital imagery, medical imaging, computer vision, pattern recognition, astrophysics, learning, Earth sciences including meteorology and geology; introduction to abstract manifolds, tangent bundles, embedding, Riemannian structures; sample spaces with a manifold structure; foundations of nonparametric statistics on manifolds: location and spread parameters for distributions on manifolds; large sample theory on manifolds, density, and function estimation on manifolds; nonparametric inference on manifolds; statistical analysis on special manifolds arising in statistics: directional and axial data analysis, projective, affine, and similarity shape data analyses, size-and-shape data analysis, diffusion tensor image analysis; concrete case studies in astronomy, image analysis, medical imaging: MRI, CT, Confocal Laser Tomography, eye imaging, brain imaging, bioinformatics, computer vision, and 3D scene recognition.

Prerequisite: STA 5707, STA 5327, or STA 5746.

 

STA 6709 Spatial Statistics (3)

Familiarity with S-Plus or SAS software. Methods for the analysis of spatial data, including geostatistical data, lattice data, and point patterns. Theory and applications of basic principles and techniques.

Prerequisite: STA 5208, 5327.

 

STA 6906r Directed Individual Study (1-12)

Special Note: (S/U grade only.) May be repeated.

 

STA 8964 Preliminary Doctoral Examination ()

 

STA 8966 Master's Comprehensive Examination ()

 

STA 8976 Master's Thesis Defense ()

 

STA 8985 Defense of Dissertation ()

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