Course Schedule and Catalog

Long Title: DATA, MODELS, AND REALITY: AN INTRODUCTION TO THE SCIENTIFIC METHOD

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Description: The formation of models of reality and the ways models are tested by their analysis in the light of data are considered. We cover a variety of examples from antiquity to the present time.

Long Title: ELEMENTARY APPLIED STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 4

Description: Topics include basic probability, descriptive statistics, probability distributions, confidence intervals, significance testing, simple linear regression and correlation, association between categorized variables.

Long Title: THE ROLE OF CHANCE IN AN INFORMATION WORLD

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: The concept of probability is a powerful idea pervading many aspects of our lives and this notion has a surprisingly long history. This course is meant to be accessible to any curious undergraduate (or graduate student) and starts out with the colorful early history of the concept. Based on this background, during the next phase of course, we will develop the precise concepts that support the intuition. We move on to discussing the concept of probability and its use in an essential manner in the physics, computer science and mathematical finance. Mathematical background typical of a sophomore or junior in science, engineering, business or any field with a quantitative foundations, will be adequate. Cross-list: COMP 281, ELEC 281.

Long Title: MODEL BUILDING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): MATH 211

Description: Examples to illustrate mathematical and statistical formulation (modeling) of scientific problems, their solution and interpretation. Problems from engineering, epidemiology, economics, and other areas are covered. Real-world situations are emphasized. Satisfies statistics design criteria.

Long Title: INTRODUCTION TO STATISTICS FOR BIOSCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 4

Prerequisite(s): MATH 101 AND MATH 102

Description: An introduction to statistics for Biosciences with emphasis on statistical models and data analysis techniques. Computer-assisted data analysis, examples, is explored in laboratory sessions. Topics include descriptive statistics, correlation and regression, categorical data analysis, statistical inference through confidence intervals and significance testing, rates, and proportions, basic epidemiology. Real-world examples are emphasized; for example, genetics, dose-response, biological assays.

Long Title: PROBABILITY AND STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): MATH 102

Description: Probability and the central concepts and methods of statistics including probability distributions, expectation, estimation, hypothesis testing, sampling distributions, linear models. Section 1 presents the general use in multiple disciplines; section 2 focuses on problem sets and examples in civil and environmental engineering. Cross-list: ECON 382. Recommended prerequisite(s): MATH 212.

Long Title: PROBABILITY & STATISTICS FOR CIVIL AND ENVIRONMENTAL ENGINEERS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 3

Description: Basic methodologies and problem solving of probibility and statistics for civil and environmental engineering. Calculus is required.

Long Title: UNCERTAINTY AND RISK IN URBAN INFRASTRUCTURES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 310 OR ECON 382 OR STAT 312

Description: Practical applications and relevance of infrastructure risk are developed in the context of real engineering problems and phenomena, including unique systems and challenges of the gulf coast area. The course starts with a survey of the roles of probability in engineering and focuses on computer-based methods, the Bayesian approach, risk analysis tools, and infrastructure safety. Cross-list: CEVE 313. Preferably section 2 of STAT 310. Repeatable for Credit.

Long Title: APPLIED PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Description: Elementary probability theory, conditional probability, independence, discrete and continuous random variables, expectation, standard discrete and continuous distributions, transformation techniques, central limit theorems, estimation, and correlation. Selected topics such as the Poisson process, Markov chains, and statistical techniques. Illustrations from engineering are emphasized. Cross-list: ELEC 331.

Long Title: METHODS OF DATA ANALYSIS AND SYSTEM OPTIMIZATION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 4

Prerequisite(s): STAT 280 OR STAT 305 OR STAT 310 OR ECON 382

Description: The three general topic areas covered in this methodology oriented course are statistical methods including regression, sampling, and experimental design; simulation based methods in statistics, queuing and inventory problems; and an introduction to optimization methods. Excel will serve as the basic computing software.

Long Title: ECONOMETRICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (ECON 382 OR STAT 310 OR STAT 381) AND MATH 211 OR MATH 355 OR CAAM 335 or permission of instructor

Description: Survey of estimation and forecasting models. Includes multiple regression time series analysis. A good understanding of linear algebra is highly desirable. Required for mathematical economic analysis majors. Cross-list: ECON 400.

Long Title: STATISTICAL COMPUTING AND GRAPHICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: Programming techniques and tools useful and advanced statistical studies. Higher level graphical methods and exploratory data analysis.

Long Title: INTRODUCTION TO REGRESSION AND STATISTICAL COMPUTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single- and multi-factor studies, analysis of variance, analysis of covariance, model selection, diagnostics. Data analysis using statistical software is emphasized.

Long Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (STAT 310 OR ECON 382)

Description: Advanced topics in statistical applications such as sampling, experimental design and statistical process control.

Long Title: COMPUTATIONAL FINANCE II: TIME SERIES ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Applied time series modeling and forecasting, with applications to financial markets. STAT 621 is a graduate version of STAT 421 with advanced assignments. Graduate/Undergraduate Equivalency: STAT 621. Recommended Prerequisite(s): STAT 410. Mutually Exclusive: Cannot register for STAT 421 if student has credit for STAT 621.

Long Title: BAYESIAN DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Computational methods will be emphasized. Course taught at the University of Texas - Health Science Center. Graduate/Undergraduate Equivalency: STAT 622. Mutually Exclusive: Cannot register for STAT 422 if student has credit for STAT 622.

Long Title: PROBABILITY IN BIOINFORMATICS AND GENETICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. UG/GR version: STAT 623. Graduate/Undergraduate Equivalency: STAT 623. Mutually Exclusive: Cannot register for STAT 423 if student has credit for STAT 623.

Long Title: OVERVIEW OF MATHEMATICAL STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Topics include random variables, distributions, transformations, moment generating functions, common families of distributions, independence, sampling distributions, the basics of estimation theory, hypothesis testing and Bayesian inference.

Long Title: STATISTICS FOR BIOENGINEERING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 1

Prerequisite(s): BIOE 252

Description: Course covers application of statistics to bioengineering. Topics include descriptive statistics, estimation, hypothesis testing, ANOVA, and regression. Offered first five weeks of the semester. Cross-list: BIOE 440.

Long Title: STATISTICAL DESIGN IN PRACTICE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): LPAP 100

Description: This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics. Instructor Permission Required.Graduate/Undergraduate Equivalency: STAT 540.

Long Title: BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 or permission of instructor

Description: An overview of statistical methodologies useful in the practice of Biostatistics. Topics include epidemiology, rates, and proportions, categorical data analysis, regression, and logistic regression, retrospective studies, case-control studies, survival analysis. Real biomedical applications serve as context for evaluating assumptions of statistical methods and models. S-Plus (R) serves as computing software. Graduate/Undergraduate Equivalency: STAT 553. Mutually Exclusive: Cannot register for STAT 453 if student has credit for STAT 553.

Long Title: FROM SEQUENCE TO STRUCTURE: AN INTRODUCTION TO COMPUTATIONAL BIOLOGY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 4

Description: Contemporary introduction to problems in computational biology spanning sequence to structure. The course has three modules: the first introduces students to the design and statistical analysis of gene expression studies; the second covers statistical machine learning techniques for understanding experimental data generated in computational biology; and the third introduces problems in the modeling of protein structure using computational methods from robotics. The course is project oriented with an emphasis on computation and problem-solving. Cross-list: BIOE 470, COMP 470. Recommended Prerequisites: COMP 280 and COMP 212 and (STAT 310 or STAT 331).

Long Title: ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course is a series of group projects. Student assessment is performed through quantification of the role and contributions of each student in the group and the overall strength of the project. Undergraduates will be members of the teams led by graduate students. The grading scale for each group will be separate. Graduate students will be graded on leadership as well as the other aspects of the project. Undergraduates will be graded on their ability to contribute to the group effort. Mutually Exclusive: Cannot register for STAT 484 if student has credit for STAT 684.

Long Title: QUANTITATIVE ENVIRONMENTAL DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is the undergraduate version of STAT 685 with reduced requirements. Graduate/Undergraduate Equivalency: STAT 685. Recommended Prerequisite(s): STAT 305 and STAT 385. Mutually Exclusive: Cannot register for STAT 485 if student has credit for STAT 685.

Long Title: COMPUTATIONAL FINANCE I: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Laboratory

Credit Hours: 3

Prerequisite(s): STAT 310 OR ECON 382 OR STAT 331 OR STAT 312

Description: This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. The course uses computer simulations as an alternative to closed form solutions. UG/GR version: STAT 686. Graduate/Undergraduate Equivalency: STAT 686. Mutually Exclusive: Cannot register for STAT 486 if student has credit for STAT 686.

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1 TO 6

Description:  Repeatable for Credit.

Long Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Satisfactory/Unsatisfactory

Course Type: Independent Study

Credit Hours: 0 TO 6

Description:  Repeatable for Credit.

Long Title: RESEARCH THEMES IN THE MATHEMATICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1 TO 3

Description: A seminar course that will cover selected theme of general research in the mathematical sciences from the perspectives of mathematics, computational and applied mathematics and statistics. The course may be repeated multiple times for credit. Cross-listed with MATH 498 and CAAM 498. Cross-list: CAAM 498, MATH 498, Graduate/Undergraduate Equivalency: STAT 698. Mutually Exclusive: Cannot register for STAT 498 if student has credit for STAT 698. Repeatable for Credit.

Long Title: MATHEMATICAL SCIENCES VIGRE SEMINAR

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1 TO 3

Description: This course prepares a student for research in the mathematical sciences. Topics will change each semester. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. Each semester may introduce new topics. Graduate/Undergraduate Equivalency: STAT 699. Repeatable for Credit.

Long Title: TOPICS IN METHODS AND DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Applications of least squares and general linear mode. Cross-list: POLI 503.

Long Title: ADVANCED PSYCHOLOGICAL STATISTICS I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: See psychology listing for course information. Cross-list: PSYC 502.

Long Title: ADVANCED PSYCHOLOGICAL STATISTICS II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): PSYC 502 OR STAT 509

Description: A continuation of PSYC 502, focusing on multiple regression. Other multivariate techniques and distribution-free statistics are also covered. Instructor Permission Required.Cross-list: PSYC 503.

Long Title: ADVANCED BAYESIAN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 422 OR STAT 622

Description: Modern Topics in Bayesian Statistics.

Long Title: MATHEMATICAL STATISTICS I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 AND STAT 431 or permission of instructor

Description: The first semester in a two-semester sequence in mathematical statistics: random variables, distributions, small and large sample theorems of hypothesis testing, point estimation, and confidence intervals; topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed. Required for graduate students in statistics.

Long Title: ADVANCED STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: A continuation of STAT 532. Required for Ph.D. students in statistics.

Long Title: PRACTICUM IN STATISTICAL MODELING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 2

Description: This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics. Designed for graduate students in statistics. Graduate/Undergraduate Equivalency: STAT 450. Repeatable for Credit.

Long Title: MULTIVARIATE ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Study of multivariate data analysis and theory. Topics include normal theory, principal components, factor analysis, discrimination, estimation and hypothesis testing, multivariate analysis of variance and regression clustering.

Long Title: SIMULATION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Topics in stochastic simulation including; random number generators; Monte Carlo methods, resampling methods, Markov Chain Monte Carlo, importance sampling and simulation based estimation for stochastic processes.

Long Title: GLM & CATEGORICAL DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Contingency tables, association parameters, chi-squared tests, general theory of generalized linear models, logistics regression, loglinear models, poisson regression.

Long Title: DESIGN AND ANALYSIS OF EXPERIMENTS AND SAMPLING THEORY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Long Title: SURVIVAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 AND STAT 431

Description: Lifetime tables, cumulative distribution theory, censored data, Kaplan-Meier survival curves, log-rank tests, Cox proportional hazards models, parametric and non parametric estimation, hypothesis testing.

Long Title: FUNCTIONAL DATA AND WAVELETS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431 AND STAT 410

Description: Statistical methods for functional data, with emphasis on non-parametric bases representations, such as wavelets. Topics include smoothing of noisy signals, non parametric estimation of densities and regression functions, and representations of stochastic processes. Some emphasis is given to Bayesian inferential procedures. Matlab software is used for class demonstrations. No prior knowledge on wavelets is required.

Long Title: NONPARAMETRIC FUNCTION ESTIMATION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Survey of topics in data analysis including data visualization, multivariate density estimation, and nonparametric regression. Advanced applications will include clustering, discrimination, dimension reduction, and bump-hunting using nonparametric density procedures.

Long Title: ADVANCED TOPICS IN TIME SERIES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (STAT 421 OR STAT 621) AND STAT 532

Description: The course will cover current topics in both modeling and forecasting discrete and continuous time series. A brief coverage will also be given to spatial and spatial-temporal processes. Emphasis will be placed on applications in the area of computational finance.

Long Title: APPLIED STOCHASTIC PROCESSES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431

Description: This course covers the theory of some of the most frequently used stochastic processes in application; discrete and continuous time, Markov chains, Poisson and renewal processes, and Brownian motion.

Long Title: BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 or permission of instructor

Description: Same as STAT 453 with advanced problem sets. Graduate/Undergraduate Equivalency: STAT 453. Mutually Exclusive: Cannot register for STAT 553 if student has credit for STAT 453.

Long Title: MATHEMATICAL PROBABILITY I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Measure-theoretic foundations of probability. Open to qualified undergraduates. Required for PhD students in statistics. Cross-list: CAAM 581.

Long Title: MATHEMATICAL PROBABILITY II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 581

Description: Continuation of STAT 581.

Long Title: INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Review of basic probability; Sequences of random variables; Random vectors and estimation; Basic concepts of random processes; Random processes in linear systems, expansions of random processes; Wiener filtering; Spectral representation of random processes, and white-noise integrals. Cross-list: CAAM 583, ELEC 533. Recommended Prerequisite(s): Please see instructor.

Long Title: WAVELET AND SPECTRAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: See ELEC listing for course information. Cross-list: ELEC 532.

Long Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1 TO 15

Description:  Repeatable for Credit.

Long Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1 TO 6

Description:  Repeatable for Credit.

Long Title: GRADUATE SEMINAR IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1

Description: Students participate in the process of researching professional literature (journal articles, book chapters, dissertations), preparing, delivering and critiquing talks. Literature topics change each semester. Repeatable for Credit.

Long Title: GRADUATE SEMINAR IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1

Description:  Repeatable for Credit.

Long Title: ADVANCED ECONOMIC STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Statistical inference and the testing of hypotheses multiple and partial correlation analysis; analysis of variance and regression. Cross-list: ECON 504.

Long Title: SAS STATISTICAL PROGRAMMING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course will cover the following: (1) DATA step including arrays, merging, do-loop processing, if then else statements, set statements importing and exporting, space optimization, (2) PROC TABULATE and PROC REPORT, (3) Brief functions survey, e.g. random number generators, character and mathematical functions, time and data functions etc., (4) Formats, (5) Brief survey of statistical PROC's, (6) SAS ODS (Output Delivery System) from statistical procedures, (7) Output datasets from statistical procedures, (8) PROC GRAPH and Statistical Graphics Procedures (SGPLOT, SGPANEL, SGSCATTER), (9) PROC SQL (includes built-in short course on basic SQL), (10) PROC IML including functions, subroutines and optimization etc., (11) Macro programming facility. Priority registration is given to STAT majors.

Long Title: ECONOMETRICS I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): ECON 504

Description: Estimation and inference in single equation regression models, multicollinearity, autocorrelated and heteroskedastic disturbances, distributed lags, asymptotic theory, and maximum likelihood techniques. Emphasis is placed on the ability to analyze critically the literature. Cross-list: ECON 510.

Long Title: ECONOMETRICS II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Topics in linear and nonlinear simultaneous equations estimation, including qualitative and categorical dependent variables models and duration analysis. Applied exercises use SAS and the Wharton Quarterly Econometric Model. Cross-list: ECON 511.

Long Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Seminar on advanced topics in Statistics. Repeatable for Credit.

Long Title: COMPUTATIONAL FINANCE II: TIME SERIES ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Applied time series modeling and forecasting, with applications to financial markets with advanced problem sets. UG/GR version: STAT 421. This is a graduate version of STAT 421 with advanced assignments. Graduate/Undergraduate Equivalency: STAT 421. Mutually Exclusive: Cannot register for STAT 621 if student has credit for STAT 421.

Long Title: BAYESIAN DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Graduate/Undergraduate Equivalency: STAT 422. Mutually Exclusive: Cannot register for STAT 622 if student has credit for STAT 422.

Long Title: PROB BIOFORMATICS & GENETICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. Graduate/Undergraduate Equivalency: STAT 423. Mutually Exclusive: Cannot register for STAT 623 if student has credit for STAT 423.

Long Title: TOPICS IN CLINICAL TRIALS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course deals with fundamental concepts in the design of clinical studies, ranging from early dose-finding studies (phase I) to screening studies (phase II) to randomized comparative studies (phase III). The goal is to prepare the student to read the clinical trial literature critically and to design clinical studies. Additionally, the faculty will introduce newer designs for clinical studies that incorporate prior knowledge and/or satisfy optimality considerations. Topics include protocol writing; randomization; sample size calculation; study design options; interim monitoring; adaptive designs; multiple end points; and writing up the results of a clinical trial for publication.

Long Title: GRAPHICAL MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course will focus on providing diverse mathematical tools for graduate students from statistical inference and learning; graph theory, signal processing and systems; coding theory and communications, and information theory. We will discuss exact and approximate statistical inference over large number of interacting variables, and develop probablistics and optimization-based computational methods. We will cover hidden Markov models, belief propagation, Monte Carlo sampling algorithms, and variational Bayesian methods. Cross-list: ELEC 633.

Long Title: EXTREME VALUE THEORY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 3

Prerequisite(s): STAT 532

Description: Extreme Value Theory is used in many areas such as financial markets, risk management, environmental studies, as well as network design. In this course we will study the theory and practice of extreme value theory. Recommended prerequisite(s): STAT 533.

Long Title: DATA MINING AND STATISTICAL LEARNING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Survey of ideas, methods, and tools for analyzing large data sets; techniques for searching for unexpected relationships in data. Topics from supervised and unsupervised learning include regression, discriminant analysis, kernels, model selection, bootstrapping, trees, MARS, boosting, classification, clustering, neural networks, SVM, association rules, principal curves, multidimensional scaling, and projection pursuit.

Long Title: ADVANCED SURVIVAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 547

Long Title: STOCHASTIC DIFFERENTIAL EQUATIONS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course will cover both theory and applications of stochastic differential equations. Topics include: the Langevin equation from physics, the Wiener process, white noise, the martingale theory, numerical methods and simulation, the Ito and Stratonovitch theories, applications in finance, signal processing, materials science, biology, and other fields.

Long Title: STATISTICAL GENETICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course centers on applications of statistics in genetic problems, especially as they pertain to genotype-phenotype association. Various data structures will be the centerpiece of the course, including genotype, allele-sharing, and gene-expression. Topics include family and population-based study design, linkage, association, differential gene expression. Genetic analysis software will also be discussed and used. *The course will meet at M D Anderson Cancer Prevention Building, CPB4.3650.*

Long Title: ADVANCED METHODS GENOMICS AND PROTEOMICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: We propose to discuss development & application of statistical methods in the analysis of high-throughput bioinformatics data that arise from problems in medical research, in particular cancer research, molecular and structural biology. We present a broad overview of statistical inference problems related to three main high- throughput platforms: microarray gene expression, serial analysis gene expression (SAGE), and mass spectrometry proteomic profiles. Our main focus is on the design, statistical inference and data analysis, from a statistician's perspective, of data sets arising from such high throughput experiments.

Long Title: MICROARRAY DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course is an introduction to the statistical and bioinformatic analysis of microarray data. The course covers both Affymetrix oligonucleotide arrays and two- color fluorescence cDNA microarrays. The course introduces students to the full range of processing microarray experiments, from experimental design, through image processing, background correction, normalization, and quality control to the downstream statistical analysis of differential expression. The course includes coverage of the key statistical concept of multiple testing. The covers common methods of pattern identification and pattern recognition in the context of microarrays. It also includes the bioinformatic interpretation of the results through tools to interact with public genome databases. All concepts will be illustrated through hands-on interaction with publicly available microarray data sets. Homework assignments will require some knowledge of R, a statistical programming language. The course will include a brief introduction to R. This class meets in the GSBS library (BSRB 53.8351).

Long Title: ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Learn and apply quantitative risk assessment methodology to estimate human health risk from environmental exposure to contamination in air, soil and water. Students will conduct a series of team projects focused on toxicology, risk based screening levels, exposure concentration estimation and risk characterization. Mutually Exclusive: Cannot register for STAT 684 if student has credit for STAT 484.

Long Title: QUANTITATIVE ENVIRONMENTAL DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 3

Description: A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is required for EADM students. Graduate/Undergraduate Equivalency: STAT 485. Mutually Exclusive: Cannot register for STAT 685 if student has credit for STAT 485.

Long Title: COMPUTATIONAL FINANCE I: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. The course uses computer simulations as an alternative to closed form solutions with advanced problem sets. UG/GR version: STAT 486. Graduate/Undergraduate Equivalency: STAT 486. Mutually Exclusive: Cannot register for STAT 686 if student has credit for STAT 486.

Long Title: DECISION THEORY WITH MEDICAL APPLICATION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 422 AND STAT 410 or permission of instructor

Description: Statistical inference, decision theory, and simulation as applied to assist in making individual clinical decisions, policy recommendations, and as a guide to study design and research; topics include statistical decision theory, decision analysis, decision trees, markvo models and simulation, cost-effectiveness analysis, meta-analysis, and sensitivity analysis. Grading will be based on regularly assigned homework exercises and term projects.

Long Title: RESEARCH THEMES IN THE MATHEMATICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1 TO 3

Description: A seminar course that will cover selected theme of general research in the mathematical sciences from the perspectives of mathematics, computational and applied mathematics and statistics. The course may be repeated multiple times for credit. Cross-listed with MATH 698 and CAAM 698. Cross-list: CAAM 698, MATH 698, Graduate/Undergraduate Equivalency: STAT 498. Mutually Exclusive: Cannot register for STAT 698 if student has credit for STAT 498. Repeatable for Credit.

Long Title: MATHEMATICAL SCIENCES VIGRE SEMINAR

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1 TO 3

Description: This course prepares a student for research in the mathematical sciences on a specific topic. Each section is dedicated to a different topic. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. The topics change each semester. Graduate/Undergraduate Equivalency: STAT 499. Repeatable for Credit.

Long Title: THESIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Research

Credit Hours: 1 TO 15

Description:  Repeatable for Credit.