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/Laboratory

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: HISTORY OF NUMBERS AND GAMES OF CHANCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: Starting with the colorful history of numbers, we discover their use to characterize chance or luck through probability; students will participate in one major project and submit a report-application areas include physics, computer science, sports, finance, etc. The course is accessible to sophomores and juniors in science, engineering or business. 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 or permission of instructor

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 and examples, are 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. Real-world examples are emphasized. Recommended Prerequisite(s): MATH 212

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 of random variables, expectation, sampling distributions, estimation, confidence intervals, and hypothesis testing. Cross-list: ECON 307. 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

Prerequisite(s): MATH 102

Description: Probability and the central concepts and methods of statistics including probability, distributions of random variables, expectation, sampling distributions, estimation, confidence intervals, and hypothesis testing. Examples are predominantly from civil and environmental engineering. Recommended Prerequisite(s): MATH 212.

Long Title: UNCERTAINTY AND RISK IN URBAN INFRASTRUCTURES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): STAT 312 OR STAT 310 OR ECON 307 OR ECON 382 OR STAT 331 OR ELEC 331

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. Repeatable for Credit.

Long Title: APPLIED PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): MATH 211 AND MATH 212

Description: Applied probability with applications from reliability, operations research, and population biology. Topics include: axioms of probability; conditional probability; in dependence; random variables; probability distribution functions; parametric families of distributions; expectation and conditional expectation; generation functions; law of large numbers; central limit theorem; discrete-time Markov chains; branching and Poisson processes. Cross-list: ELEC 331.

Long Title: STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 1

Prerequisite(s): STAT 331 OR STAT 312 OR ELEC 303

Description: This one hour course covers application and development of methods in statistical inference using R.

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 307 OR STAT 312

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: STATISTICAL COMPUTING AND GRAPHICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 3

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

Long Title: REGRESSION ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 4

Prerequisite(s): STAT 310 OR STAT 312 OR ECON 307 OR ECON 382 or permission of instructor

Description: An introduction to linear regression and its applications. Topics include simple and multiple linear regression, least squares, analysis of variance, model selection, diagnostics, remedial measures. Applications to real data using statistical software are emphasized. Graduate/Undergraduate Equivalency: STAT 615. Recommended Prerequisite(s): CAAM 335 or MATH 355. Mutually Exclusive: Cannot register for STAT 410 if student has credit for STAT 615.

Long Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (STAT 310 OR STAT 312 OR ECON 307 OR ECON 382) AND STAT 410 OR STAT 615) or permission of instructor

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

Long Title: APPLIED TIME SERIES AND FORECASTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 310 OR ECON 307 OR STAT 312 or permission of instructor

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

Prerequisite(s): STAT 410

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. STAT 422 will have assignments and examinations focusing more on basic concepts than on theoretical methods. 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

Prerequisite(s): STAT 310 OR STAT 312 OR STAT 331 OR STAT 431 or permission of instructor

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 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

Prerequisite(s): STAT 310 OR ECON 307 OR STAT 312 OR STAT 331

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. BIOE 252 may be taken concurrently with STAT 440. BIOE 440/STAT 440 and BIOE 439 cannot both be taken for credit. Cross-list: BIOE 440, Graduate/Undergraduate Equivalency: STAT 640. Mutually Exclusive: Cannot register for STAT 440 if student has credit for BIOE 439.

Long Title: DATA MINING AND STATISTICAL LEARNING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (STAT 410 OR STAT 615) AND (CAAM 210 OR COMP 140 OR STAT 405)

Description: Survey of ideas, methods, and tools for analyzing large data sets. Topics from supervised and unsupervised learning include penalized regression and classification, support vector machines, kernel methods, model selection, matrix factorizations and completion, graphical models, clustering, boosting and ensemble learning. STAT 444 will have applied assignments and exams focusing on data analysis. Mutually Exclusive: Cannot register for STAT 444 if student has credit for STAT 640.

Long Title: QUANTITATIVE FINANCIAL RISK MANAGEMENT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): MATH 211 AND MATH 212 AND (ECON 400 OR STAT 400 OR ECON 409 OR STAT 410) OR STAT 310 OR ECON 307 OR STAT 312 OR STAT 331 OR ELEC 331

Description: This course covers the use of financial securities and derivatives to take or hedge financial risk positions. Most commonly used instruments, from simple forwards and futures to exotic options and swaptions are covered. The pricing of derivatives securities will also be studied, but the emphasis will be on the mechanics and uses of financial engineering methods. STAT 449 is mutually exclusive to ECON 449. Credit cannot be given for both. Graduate/Undergraduate Equivalency: STAT 649. Mutually Exclusive: Cannot register for STAT 449 if student has credit for ECON 449/STAT 649.

Long Title: STATISTICAL DESIGN IN PRACTICE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 1 TO 3

Description: Students engage in individual or team-oriented statistical projects to solve problems motivated by theory, computation, or application to real problems and data. Typical projects involve statistical modeling, data analysis, and computing to answer substantive questions in engineering or the physical, biological, or social sciences. Participants attend regular seminars addressing project development, research techniques and effective written and verbal communication skills in presenting statistical results. Repeatable for Credit.

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. R serves as the 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 Prerequisite(s): COMP 280 and (STAT 310 or STAT 331).

Long Title: QUANTITATIVE FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: A modern approach to fundamental analytics of securities, the classic works of Graham and Dodd. Deconstructing the Efficient Market Hypothesis Financial Statement Analysis, Capital Market Theory, CAPM, APT, Fama-French Empirical Financial Forecasting. Graduate/Undergraduate Equivalency: STAT 682. Mutually Exclusive: Cannot register for STAT 482 if student has credit for STAT 682.

Long Title: ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 280 OR STAT 305 or permission of instructor

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. Cross-list: CEVE 484, Graduate/Undergraduate Equivalency: STAT 684. Mutually Exclusive: Cannot register for STAT 484 if student has credit for STAT 684.

Long Title: ENVIRONMENTAL STATISTICS AND DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 385

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: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Laboratory

Credit Hours: 3

Prerequisite(s): STAT 310 OR ECON 307 OR ECON 382 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. This undergraduate course uses computer simulations as an alternative to closed form solutions. 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: Standard Letter

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-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 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: NEURAL MACHINE LEARNING I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: Review of major neural machine learning (Artificial Neural Network) paradigms. Analytical discussion of supervised and unsupervised neural learning algorithms and their relation to information theoretical methods. Practical applications to data analysis such as pattern recognition, clustering, classification, function approximation/regression, non-linear PCA, projection pursuit, independent component analysis, with lots of examples from image and digital processings. Details are posted at www.ece.rice.edu/~erzsebet/ANNcourse.html. Cross-list: COMP 502, ELEC 502.

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: Introduction to inferential statistics, with emphasis on analysis of variance. Students who do not meet registration requirements as Graduate and Psychology Majors must receive instructor permission to register. 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. Cross-list: PSYC 503.

Long Title: INTRODUCTION TO BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: Presents basic and advanced methods of statistics as applied to problems in bioengineering. Demonstrates techniques for data organization, exploration, and presentation. Foundations of statistical estimation, inference, and testing are reviewed. Optimal planning of experiments is explored. Advanced techniques include multiple regression, variable selection, logistic regression, analysis of variance, survival analysis, multiple measurements and measurements over time. Additional topics, such as Bayesian methods, will be discussed as time allows. Labs will use the statistical software JMP and/or R. Cross-list: BIOE 514.

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 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 decision theory and Bayesian methods, 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: MATHEMATICAL STATISTICS II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 532

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. Repeatable for Credit.

Long Title: MULTIVARIATE ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431 AND (STAT 410 OR STAT 615)

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

Prerequisite(s): STAT 431 AND (STAT 615 OR STAT 410)

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

Prerequisite(s): STAT 431 AND (STAT 410 OR STAT 615)

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

Prerequisite(s): STAT 431 AND STAT 615

Long Title: SURVIVAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431 AND STAT 615

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 ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 533 AND STAT 581

Description: Statistical methods for functional data; spaces of functions; pre-processing of functional data; probability models for functional data; basis representations including spline functions, orthogonal bases such as wavelets, and functional principal components; methods of inference for functional data including both frequentist and Bayesian methods.

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 621 OR STAT 552 or permission of instructor

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.

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 615 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.

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: STATISTICS COLLOQUIUM

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 OR STAT 604

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: INTRODUCTION TO REGRESSION AND STATISTICAL COMPUTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 310 OR STAT 312 OR ECON 307 OR ECON 382

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. This is a graduate version of STAT 410 with advanced assignments. Graduate/Undergraduate Equivalency: STAT 410. Mutually Exclusive: Cannot register for STAT 615 if student has credit for STAT 410.

Long Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 3

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

Long Title: APPLIED TIME SERIES AND FORECASTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 615 or permission of instructor

Description: Applied time series modeling and forecasting, with applications to financial markets with advanced problem sets. This is a graduate version of STAT 421 with advanced assignments. The courses STAT 615 and STAT 431 may be taken concurrently with STAT 621 if courses are not in history. 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

Prerequisite(s): STAT 431 AND (STAT 615 OR STAT 410)

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. STAT 622 will have more advanced assignments and examinations focusing on theoretical methods. Graduate/Undergraduate Equivalency: STAT 422. Mutually Exclusive: Cannot register for STAT 622 if student has credit for STAT 422.

Long Title: PROBABILITY IN BIOINFORMATICS AND GENETICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 310 OR STAT 331 or permission of instructor

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

Prerequisite(s): STAT 431 AND (STAT 410 OR STAT 615)

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

Prerequisite(s): (STAT 410 OR STAT 615) AND (CAAM 210 OR COMP 140 OR STAT 405)

Description: Survey of ideas, methods, and tools for analyzing large data sets. Topics from supervised and unsupervised learning include penalized regression and classification, support vector machines, kernel methods, model selection, matrix factorizations and completion, graphical models, clustering, boosting and ensemble learning. STAT 640 will have advanced assignments and exams focusing on theory and methods. Graduate/Undergraduate Equivalency: STAT 440. Mutually Exclusive: Cannot register for STAT 640 if student has credit for STAT 444.

Long Title: DATA VISUALISATION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 405 AND STAT 431 AND STAT 615

Description: This advanced graduate course will address critical evaluation of data through visualization. The focus is on statistical graphics, graphics that display "statistical" data (observations are in rows and variables in columns), with some forays into the field of information visualization. Recommended Prerequisite(s): STAT 541.

Long Title: ADVANCED SURVIVAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 547

Long Title: GRAPHICAL MODELS AND NETWORKS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431

Description: Graphical models – aka Bayes networks, Markov networks, Gaussian networks, etc. – have been widely used to represent complex phenomena with dependence. The course aims to stimulate interest in graphical models and covers directed and undirected graphical models, exponential-family representations of graphical models, statistical inference, finite-sample and large-sample properties, and applications.

Long Title: QUANTITATIVE FINANCIAL RISK MANAGEMENT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431 OR STAT 615

Description: This course covers the use of financial securities and derivatives to take or hedge financial risk positions. Most commonly used instruments, from simple forwards and futures to exotic options and swaptions are covered. The pricing of derivatives securities will also be studied, but the emphasis will be on the mechanics and uses of financial engineering methods. Students receiving graduate credit in STAT 649 will be expected to address additional homework and test questions targeting a graduate level understanding of the material. Graduate/Undergraduate Equivalency: STAT 449. Mutually Exclusive: Cannot register for STAT 649 if student has credit for STAT 449.

Long Title: STOCHASTIC DIFFERENTIAL EQUATIONS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 581

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

Prerequisite(s): STAT 431 AND STAT 615

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: PROBABILITY AND STATISTICS FOR SYSTEMS BIOLOGY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 552

Description: Stochastic modeling of signaling pathways; models of cancer growth and spread (stochastic and spatial); stem cell differentiation dynamics; viral infection and the immune system, plus background topics (dispersed) such as Markov processes, diffusion equations, branching processes and other.

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: QUANTITATIVE FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Description: A modern approach to fundamental analytics of securities, the classic works of Graham and Dodd. Deconstructing the Efficient Market Hypothesis Financial Statement Analysis, Capital Market Theory, CAPM, APT, Fama-French Empirical Financial Forecasting. Graduate/Undergraduate Equivalency: STAT 482. Mutually Exclusive: Cannot register for STAT 682 if student has credit for STAT 482.

Long Title: ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 280 OR STAT 305 or permission of instructor

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. Cross-list: CEVE 684, Graduate/Undergraduate Equivalency: STAT 484. Mutually Exclusive: Cannot register for STAT 684 if student has credit for STAT 484.

Long Title: ENVIRONMENTAL STATISTICS AND DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 385

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: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431 AND (STAT 615 OR STAT 410)

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. This graduate course uses computer simulations as an alternative to closed form solutions with advanced problem sets. 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 615 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-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 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. Fall 2014, Section 002: This seminar course will focus on developing computational, statistical and machine learning sills required to analyze and make predictions for Big-Data. Assignments will include projects tackling real Big-Data problems. Graduate/Undergraduate Equivalency: STAT 499. Repeatable for Credit.

Long Title: THESIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1 TO 15

Description:  Repeatable for Credit.