Statistics

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Admission Requirements

For the in-person Master program, knowledge of multivariable calculus (comparable to MA 242 at NCSU) and matrix algebra (comparable to MA 305/MA 405 at NCSU) are the minimal requirements for entry. For the PhD program, students are expected to have a good foundation in the material covered in the core courses (ST 701, ST 702, ST 703, ST 704 and ST 705), even if their master’s degree was received at another institution. Some students with previous master’s degrees find it useful to take these courses at NCSU. However, this tends to lengthen the time to degree. Students are also expected to have had a course comparable to MA 425 Mathematical Analysis I at NCSU.

Students may apply to either the Master or PhD program directly from a Bachelor’s degree. GRE General and Subject Tests scores are NOT required for admission to the Statistics Graduate Programs including both master and PhD programs.  Due to the differences in student backgrounds, there is a separate admissions process for the online and in-person programs. A completed application consists of:

  1. An online application form;
  2. A transcript from each postsecondary institution;
  3. Three letters of recommendation;
  4. English proficiency scores (TOEFL or IELTS);
  5. A written personal statement, which should not exceed two pages and should describe the applicant's academic and career goals as well as special interests in the area of statistics;
  6. A resume or curriculum vita;
  7. An application fee.

Students wishing to pursue the Ph.D. degree: Apply directly to the Ph.D. program. The master’s program is not an intermediate step in that path. Ph.D. applicants are admitted only in the fall semester. Complete applications received by December 15 will receive highest priority for admission and financial aid. Applications received later than February 15 will rarely be considered.

Students wishing to obtain only a master’s degree (not as a route to the Ph.D.):

  1. Applicants to the in-person master’s degree may choose to start in the summer (late May) or in the fall (August). The summer session courses are offered online, so students would not be required to attend classes on campus until fall. The summer enrollment is only for students who are already in the United States. Fall enrollment is often the best choice for international students.
  2. Applicants for the online master’s degree are accepted throughout the year and can start any semester.

Priority is given to applications received by January 15. The final deadline is March 25. No offers of financial support are provided to master’s students.

Master's Degree Requirements

All Master of Statistics degrees require a minimum of 30 semester hours. This includes 21 hours of common coursework: ST 517 Applied Statistical Methods I & ST 518 Applied Statistical Methods II, ST 501 Fundamentals of Statistical Inference I & ST 502 Fundamentals of Statistical Inference II, ST 503 Fundamentals of Linear Models and Regression, ST 542 Statistical Practice, and ST 555 Statistical Programming I. Each specific concentration will have additional requirements and/or electives to reach the minimum credit hours. These requirements are listed on each concentration page, listed below:

Doctoral Degree Requirements

Students that join our doctoral program with a Master of Statistics from another university are required to have a minimum of 54 credit hours in their doctoral Plan of Work (POW). Students who receive their master’s degree from NC State must have a minimum of 72 credit hours on the master’s and Ph.D. POWs combined. The POW may include research credit hours (ST 895); however, students are required to take 24 hours of coursework consisting of core courses, a consulting course, and electives as detailed on the degree requirements page.

Student Financial Support

Departmental assistantships and fellowships are awarded to students in the Ph.D. program each year on a competitive basis. 

Other Relevant Information

With a large graduate faculty representing virtually all major statistical specializations, the department is recognized as a world leader in graduate education and research in statistics. The Department provides a dynamic environment for teaching, core research and collaborative research across disciplines, with formal program concentrations in biostatistics, bioinformatics, environmental, financial and mathematical statistics.

Faculty

  • Dennis D. Boos
  • Marie Davidian
  • Sujit K. Ghosh
  • Subhashis Ghoshal
  • Kevin Gross
  • Marcia Lynn Gumpertz
  • Jacqueline M. Hughes-Oliver
  • Eric Benjamin Laber
  • Wenbin Lu
  • Ryan G. Martin
  • Spencer V. Muse
  • Jason A. Osborne
  • Brian J. Reich
  • Rui Song
  • Ana-Maria Staicu
  • Leonard A. Stefanski
  • Jeffrey L. Thorne
  • Jung-Ying Tzeng
  • Alyson Gabbard Wilson
  • Fred Andrew Wright
  • Daowen Zhang
  • Xinge Jessie Jeng
  • Arnab Maity
  • Donald Eugene Kemp Martin
  • Thomas W. Reiland
  • Charles Eugene Smith
  • Eric C. Chi
  • Emily Hector
  • Karl Timothy LeRoy Pazdernik
  • Srijan Sengupta
  • Jonathan W. Stallrich
  • Minh Tang
  • Jonathan Paul Williams
  • Luo Xiao
  • Shu Yang
  • Jonathan W. Duggins
  • Emily H. Griffith
  • Herle M. McGowan
  • Logan J. Opperman
  • Justin B. Post
  • Paul R. Savariappan
  • Shuting Wang
  • William Reid Atchley
  • Peter Bloomfield
  • Cavell Brownie
  • David Alan Dickey
  • Thomas Michael Gerig
  • Harvey J. Gold
  • Thomas Johnson
  • John F. Monahan
  • Kenneth Hugh Pollock
  • Charles P. Quesenberry
  • John Oren Rawlings
  • Don L. Ridgeway
  • Moon Won Suh
  • William H. Swallow
  • Anastasios A. Tsiatis
  • John L. Wasik
  • Howard D. Bondell
  • Soumendra Nath Lahiri
  • Alison Anne Motsinger-Reif
  • Eric A. Stone
  • Yichao Wu

Courses

ST 501  Fundamentals of Statistical Inference I  (3 credit hours)  

First of a two-semester sequence in probability and statistics taught at a calculus-based level. Probability: discrete and continuous distributions, expected values, transformations of random variables, sampling distributions. Credit not given for both ST 701 and ST 501. Note: this course will be offered in person (Fall) and online (Summer).

Prerequisite: MA 242 or equivalent

Typically offered in Fall and Summer

ST 502  Fundamentals of Statistical Inference II  (3 credit hours)  

Second of a two-semester sequence in probability and statistics taught at a calculus-based level. Statistical inference: methods of construction and evaluation of estimators, hypothesis tests, and interval estimators, including maximum likelihood. Credit not given for both ST 702 and ST 502. Note: this course will be offered in person (Spring) and online (Fall).

Prerequisite: ST 501

Typically offered in Fall and Spring

ST 503  Fundamentals of Linear Models and Regression  (3 credit hours)  

Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 705 and ST 503. Note: this course will be offered in person (Spring) and online (Summer).

P: ST 501 and MA 405 or equivalent (Linear Algebra); C: ST 502

Typically offered in Spring and Summer

ST 505/ST 405  Applied Nonparametric Statistics  (3 credit hours)  

Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered.

Prerequisite: ST 508 or ST 512 or ST 514 or ST 516

Typically offered in Fall only

ST 507  Statistics For the Behavioral Sciences I  (3 credit hours)  

A general introduction to the use of descriptive and inferential statistics in behavioral science research. Methods for describing and summarizing data presented, followed by procedures for estimating population parameters and testing hypotheses concerning summarized data.

Prerequisite: Graduate standing

Typically offered in Fall and Spring

ST 511  Statistical Methods For Researchers I  (3 credit hours)  

Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance and elementary experimental design, regression and correlation, chi-square.

Prerequisite: Graduate Standing

Typically offered in Fall, Spring, and Summer

ST 512  Statistical Methods For Researchers II  (3 credit hours)  

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.

Prerequisite: ST 511 or ST 513 or ST 517

Typically offered in Fall, Spring, and Summer

ST 513  Statistics for Management and Social Sciences I  (3 credit hours)  

This course introduces important ideas about collecting high quality data and summarizing that data appropriately both numerically and graphically. We explore the use of probability distributions to model data and find probabilities. Estimation of parameters and properties of estimators are discussed. Construction and interpretation of commonly used confidence intervals and hypothesis tests are investigated. Students will gain considerable experience working with data. Software is used throughout the course with the expectation of students being able to produce their own analyses.

Prerequisite: Graduate standing

Typically offered in Fall and Spring

ST 514  Statistics For Management and Social Sciences II  (3 credit hours)  

Linear regression, multiple regression and concepts of designed experiments in an integrated approach, principles of the design and analysis of sample surveys, use of computer for analysis of data.

Prerequisite: ST 513

Typically offered in Spring and Summer

ST 515  Experimental Statistics for Engineers I  (3 credit hours)  

General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance,enumeration data and experimental design.

Prerequisite: Graduate standing

Typically offered in Fall and Spring

ST 516  Experimental Statistics For Engineers II  (3 credit hours)  

General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance, enumeration data and experimental designs.

Prerequisite: ST 515

Typically offered in Fall and Spring

ST 517  Applied Statistical Methods I  (3 credit hours)  

Course covers basic methods for summarizing and describing data, accounting for variability in data, and techniques for inference. Topics include basic exploratory data analysis, probability distributions, confidence intervals, hypothesis testing, and regression analysis. This is a calculus-based course. Statistical software is used; however, there is no lab associated with the course. Credit not given for this course and ST 511 or ST 513 or ST 515. This course does NOT count as an elective towards a degree or a minor in Statistics. Note: the course will be offered in person (Fall) and online (Fall and Summer).

Prerequisites: MA 241 or equivalent (Calculus II) and MA 405 or equivalent (Linear Algebra)

Typically offered in Fall and Summer

ST 518  Applied Statistical Methods II  (3 credit hours)  

This second course in statistics for graduate students is intended to further expand students' background in the statistical methods that will assist them in the analysis of data. Course covers many fundamental analysis methods currently used to analyze a wide array of data, mostly arising from designed experiments. Topics include multiple regression models, factorial effects models, general linear models, mixed effect models, logistic regression analysis, and basic repeated measures analysis. This is a calculus-based course. Statistical software is used, however, there is no lab associated with the course. Credit not given for this course and ST 512 or ST 514 or ST 516. Note: this course will be offered in person (Spring) and online (Fall and Spring).

Prerequisite: ST 517

Typically offered in Fall and Spring

ST 519/EMS 519  Teaching and Learning of Statistical Thinking  (3 credit hours)  

This course is designed to bridge theory and practice on how students develop understandings of key concepts in data analysis, statistics, and probability. Discussion of students' understandings, teaching strategies and the use of manipulatives and technology tools. Topics include distribution, measures of center and spread, sampling, sampling distribution, randomness, and law of large numbers. Must complete a first level graduate statistics course ( ST 507, ST 511, or equivalent) before enrolling.

Prerequisite: ST 507 or ST 511

Typically offered in Spring only

ST 520  Statistical Principles of Clinical Trials  (3 credit hours)  

Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to-Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Introduction to meta-analysis. There is also discussion of Epidemiological methods time permitting.

Corequisite: ST 501 or ST 521 or ST 701

Typically offered in Fall only

ST 525  Statistics and Computing for Agricultural Data Science  (3 credit hours)  

The fundamentals of designed experiments, analysis of variance, and regression modeling. Categorical data analysis including logistic regression will be covered. Regular access to a computer for homework, class exercises, and statistical computing is required. The emphasis in this class is on the practical aspects of statistical modeling. Assignments will concentrate on problem solving rather than formal proofs and derivations.

P: ST 511 or equivalent

Typically offered in Fall and Spring

ST 531  Experimental Design  (3 credit hours)  

Overview and comparison of observational studies and designed experiments followed by a thorough discussion of design principles. Review of estimation and inference for regression and ANOVA models from an experimental design perspective. Review of design and analysis for completely randomized, randomized complete block, and Latin square designs. Designs and analysis methods for factorial experiments, general blocking structures, incomplete block designs, confounded factorials, split-plot experiments, and fractional factorial designs. Examples used to illustrate application and analysis of these designs.

Prerequisite: ST 512, or ST 515, or ST 516, or ST 517, or ST 703

Typically offered in Fall only

ST 533/ST 433  Applied Spatial Statistics  (3 credit hours)  

Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.

Typically offered in Spring only

ST 534/ST 434  Applied Time Series  (3 credit hours)  

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the statistical software packages will be emphasized.

Prerequisite: ST 422 and ST 430

Typically offered in Fall only

ST 535/ST 435  Statistical Methods for Quality and Productivity Improvement  (3 credit hours)  

Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing.

Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307

Typically offered in Fall only

ST 537/ST 437  Applied Multivariate and Longitudinal Data Analysis  (3 credit hours)  

An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data.

Prerequisite: ST 422 and ST 430

Typically offered in Fall and Spring

ST 540/ST 440  Applied Bayesian Analysis  (3 credit hours)  

Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.

Prerequisite: ST 422 and ST 430

Typically offered in Spring only

ST 542  Statistical Practice  (3 credit hours)  

This course will provide a discussion-based introduction to statistical practice geared towards students in the final semester of their Master of Statistics degree. Note: the course will be offered in person (Fall) and online (Spring and Summer).

Prerequisite: (ST 512 or ST 514 or ST 516 or ST 518) and (ST 502 or ST 522 or ST 702)

Typically offered in Fall, Spring, and Summer

ST 544  Applied Categorical Data Analysis  (3 credit hours)  

This course focuses on the concepts, methods, and models used to analyze categorical data, particularly contingency tables, count data and binary/binomial type of data. The topics covered include Pearson Chi-squared independence test for contingency tables, measures of marginal and conditional associations, small-sample inference, logistic regression models for independent binary/binomial data and many extended models for correlated binary/binomial data including matched data and longitudinal data. The course emphasizes the implementation of methods/models using SAS and the interpretation of the results from the output.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 516

Typically offered in Fall only

ST 546/MA 546  Probability and Stochastic Processes I  (3 credit hours)  

Modern introduction to Probability Theory and Stochastic Processes. The choice of material is motivated by applications to problems such as queueing networks, filtering and financial mathematics. Topics include: review of discrete probability and continuous random variables, random walks, markov chains, martingales, stopping times, erodicity, conditional expectations, continuous-time Markov chains, laws of large numbers, central limit theorem and large deviations.

Prerequisite: MA 421 and MA 425 or MA 511

Typically offered in Fall only

ST 555  Statistical Programming I  (3 credit hours)  

An introduction to programming and data management using SAS, the industry standard for statistical practice. Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications are also introduced, such as graphics and advanced reporting. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is not expected.

Prerequisite: Graduate standing

Typically offered in Fall, Spring, and Summer

ST 556  Statistical Programming II  (3 credit hours)  

Statistical procedures for importing/managing complex data structures using SQL, automated analysis using macro programming, basic simulation methods and text parsing/analysis procedures. Students learn SAS, the industry standard for statistical practice. Regular access to a computer for homework and class exercises is required.

P: ST 555 or Base SAS Certification

Typically offered in Spring and Summer

ST 558  Data Science for Statisticians  (3 credit hours)  

Methods for reading, manipulating, and combining data sources including databases. Custom functions, visualizations, and summaries. Common analyses done by data scientists. Methods for communicating results including dashboards. Regular access to a computer for homework and class exercises is required.

Prerequisites: (ST 511 or ST 517 or equivalent) and (ST 555 or equivalent)

Typically offered in Fall and Summer

ST 561/ECG 561  Applied Econometrics I  (3 credit hours)  

Introduction and application of econometrics methods for analyzing cross-sectional data in economics, and other social science disciplines, such as OLS, IV regressions, and simultaneous equations models. Students should have had a statistical methods course at the 300 level or above as well as Calculus I and II.

Typically offered in Fall only

ST 562  Data Mining with SAS Enterprise Miner  (3 credit hours)  

This is a hands-on course using modeling techniques designed mostly for large observational studies. Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis. Clustering and association analysis are covered under the topic "unsupervised learning," and the use of training and validation data sets is emphasized. Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves. SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 516 or ST 517

Typically offered in Spring only

ST 563  Introduction to Statistical Learning  (3 credit hours)  

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 517

Typically offered in Summer only

ST 590  Special Topics  (1-6 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 630  Independent Study  (1-3 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 693  Master's Supervised Research  (1-9 credit hours)  

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 695  Master's Thesis Research  (1-9 credit hours)  

Thesis Research

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 701  Statistical Theory I  (3 credit hours)  

Probability tools for statistics: description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, orderstatistics, multivariate distributions, concept of random sample, derivation of many sampling distributions.

Typically offered in Fall only

ST 702  Statistical Theory II  (3 credit hours)  

General framework for statistical inference. Point estimators: biased and unbiased, minimum variance unbiased, least mean square error, maximum likelihood and least squares, asymptotic properties. Interval estimators and tests of hypotheses: confidence intervals, power functions, Neyman-Pearson lemma, likelihood ratio tests, unbiasedness, efficiency and sufficiency.

Prerequisite: ST 701

Typically offered in Spring only

ST 703  Statistical Methods I  (3 credit hours)  

Introduction of statistical methods. Examples include multiple linear regression, concepts of experimental design, factorial experiments, and random-effects modeling. A computing laboratory addresses computational issues and use of statistical software. This course is a prerequisite for most advanced courses in statistics. This section is restricted to statistics and closely related majors.

R: 17STPHD Students Only

Typically offered in Fall only

ST 704  Statistical Methods II  (3 credit hours)  

This course will introduce many methods that are commonly used in applications. Examples include: model generation, selection, assessment, and diagnostics in the context of multiple linear regression (including penalized regression); linear mixed models; generalized linear models; generalized linear mixed models; nonparametric regression and smoothing; and finite-population sampling basics. Coverage will include some theory, plus implementation using SAS and/or R.

Prerequisite:ST 703; Corequisites: ST 702 and ST 705

Typically offered in Spring only

ST 705  Linear Models and Variance Components  (3 credit hours)  

Theory of estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markoff theorem. Estimability and properties of best linear unbiased estimators. General linear hypothesis. Application of dummy variable methods to elementary classification models for balanced and unbalanced data. Analysis of covariance. Variance components estimation for balanced data.

Corequisite: ST 702

Typically offered in Spring only

ST 706/MA 706/OR 706  Nonlinear Programming  (3 credit hours)  

An advanced mathematical treatment of analytical and algorithmic aspects of finite dimensional nonlinear programming. Including an examination of structure and effectiveness of computational methods for unconstrained and constrained minimization. Special attention directed toward current research and recent developments in the field.

Prerequisite: OR(IE,MA) 505 and MA 425

Typically offered in Spring only

ST 715  Theory Of Sampling Applied To Survey Design  (3 credit hours)  

Principles for interpretation and design of sample surveys. Estimator biases, variances and comparative costs. Simple random sample, cluster sample, ratio estimation, stratification, varying probabilities of selection. Multi-stage, systematic and double sampling. Response errors.

Prerequisite: ST 422, ST 512

Typically offered in Fall only

ST 721/GN 721  Genetic Data Analysis  (3 credit hours)  

Analysis of discrete data, illustrated with genetic data on morphological characters allozymes, restriction fragment length polymorphisms and DNA sequences. Maximum likelihood estimation, including iterative procedures. Numerical resampling. Development of statistical techniques for characterizing genetic disequilibrium and diversity. Measures of population structure and genetic distance. Construction of phylogenetic trees. Finding alignments and similarities between DNA sequences. Locating genes with markers.

Prerequisite: ST 430 and GN 311

Typically offered in Spring only

ST 732  Longitudinal Data Analysis  (3 credit hours)  

Introduction to modeling longitudinal data; Population-averaged vs. subject-specific modeling; Classical repeated measures analysis of variance methods and drawbacks; Review of estimating equations; Population-averaged linear models; Linear mixed effects models; Maximum likelihood, restricted maximum likelihood, and large sample theory; Review of nonlinear and generalized linear regression models; Population-averaged models and generalized estimating equations; Nonlinear and generalized linear mixed effects models; Implications of missing data; Advanced topics (including Bayesian framework, complex nonlinear models, multi-level hierarchical models, relaxing assumptions on random effects in mixed effects models, among others). Implementation in SAS and R.

Prerequisites: ST 702 and ST 705

Typically offered in Spring only

ST 733  Spatial Statistics  (3 credit hours)  

Introduction to the theory and methods of spatial data analysis including: visualization; Gaussian processes; spectral representation; variograms; kriging; computationally-efficient methods; nonstationary processes; spatiotemporal and multivariate models.

Prerequisite: ST 705

Typically offered in Spring only

ST 740  Bayesian Inference and Analysis  (3 credit hours)  

Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS).

Prerequisite: ST 702

Typically offered in Fall only

ST 745  Analysis of Survival Data  (3 credit hours)  

Statistical methods for analysis of time-to-event data, with application to situations with data subject to right-censoring and staggered entry, including clinical trials. Survival distribution and hazard rate; Kaplan-Meier estimator for survival distribution and Greenwood's formula; log-rank and weighted long-rank tests; design issues in clinical trials. Regression models, including accelerated failure time and proportional hazards; partial likelihood; diagnostics.

Prerequisite: ST 502 or ST 702

Typically offered in Spring only

ST 746/MA 746  Introduction To Stochastic Processes  (3 credit hours)  

Markov chains and Markov processes, Poisson process, birth and death processes, queuing theory, renewal theory, stationary processes, Brownian motion.

Prerequisite: MA 405 and MA(ST) 546 or ST 521

Typically offered in Spring only

ST 747/MA 747  Probability and Stochastic Processes II  (3 credit hours)  

Fundamental mathematical results of probabilistic measure theory needed for advanced applications in stochastic processes. Probability measures, sigma-algebras, random variables, Lebesgue integration, expectation and conditional expectations w.r.t.sigma algebras, characteristic functions, notions of convergence of sequences of random variables, weak convergence of measures, Gaussian systems, Poisson processes, mixing properties, discrete-time martingales, continuous-time markov chains.

Prerequisite: MA(ST) 546

Typically offered in Spring only

ST 748/MA 748  Stochastic Differential Equations  (3 credit hours)  

Theory of stochastic differential equations driven by Brownian motions. Current techniques in filtering and financial mathematics. Construction and properties of Brownian motion, wiener measure, Ito's integrals, martingale representation theorem, stochastic differential equations and diffusion processes, Girsanov's theorem, relation to partial differential equations, the Feynman-Kac formula.

Prerequisite: MA(ST) 747

Typically offered in Fall only

ST 750/ECG 750  Introduction to Econometric Methods  (3 credit hours)  

Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares. Extensions to time series and panel data. Consideration of endogeneity and instrumental variables estimation. Limited dependent variable and sample selection models. Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets.

Prerequisite: ST 421; Corequisite: ST 422

Typically offered in Spring only

ST 751/ECG 751  Econometric Methods  (3 credit hours)  

Introduction to important econometric methods of estimation such as Least Squares, instrumentatl Variables, Maximum Likelihood, and Generalized Method of Moments and their application to the estimation of linear models for cross-sectional ecomomic data. Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods.

Prerequisite: ST 421, ST 422

Typically offered in Fall only

ST 752/ECG 752  Time Series Econometrics  (3 credit hours)  

The characteristics of macroeconomic and financial time series data. Discussion of stationarity and non-stationarity as they relate to economic time series. Linear models for stationary economic time series: autoregressive moving average (ARMA) models; vector autoregressive (VAR) models. Linear models for nonstationary data: deterministic and stochastic trends; cointegration. Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity (ARCH) models. Generalized Method of Moments estimation of nonlinear dynamic models.

Prerequisite: ECG(ST) 751

Typically offered in Spring only

ST 753/ECG 753  Microeconometrics  (3 credit hours)  

The characteristics of microeconomic data. Limited dependent variable models for cross-sectional microeconomic data: logit/probit models; tobit models; methods for accounting for sample selection; count data models; duration analysis; non-parametricmethods. Panel data models: balanced and unbalanced panels; fixed and random effects; dynamic panel data models; limited dependent variables and panel data analysis.

Prerequisite: ECG 751

Typically offered in Spring only

ST 756/GN 756  Computational Molecular Evolution  (3 credit hours)  

Phylogenetic analyses of nucleotide and protein sequence data. Sequence alignment, phylogeny reconstruction and relevant computer software. Prediction of protein secondary structure, database searching, bioinformatics and related topics. Project required.

Prerequisite: GN 311 and ST 511

Typically offered in Fall only

ST 757/HS 757/GN 757  Quantitative Genetics Theory and Methods  (3 credit hours)  

The essence of quantitative genetics is to study multiple genes and their relationship to phenotypes. How to study and interpret the relationship between phenotypes and whole genome genotypes in a cohesive framework is the focus of this course. We discuss how to use genomic tools to map quantitative trait loci, how to study epistasis, how to study genetic correlations and genotype-by-environment interactions. We put special emphasis in using genomic data to study and interpret general biological problems, such as adaptation and heterosis. The course is targeted for advanced graduate students interested in using genomic information to study a variety of problems in quantitative genetics.

Prerequisite: ST 511

Typically offered in Fall only

ST 758  Computation for Statistical Research  (3 credit hours)  

Computational tools for research in statistics, including applications of numerical linear algebra, optimization and random number generation, using the statistical language R. A project encompassing a simulation experiment will be required.

Prerequisite: ST 702 and ST 705

Typically offered in Fall only

ST 771/BMA 771/MA 771  Biomathematics I  (3 credit hours)  

Role of theory construction and model building in development of experimental science. Historical development of mathematical theories and models for growth of one-species populations (logistic and off-shoots), including considerations of age distributions (matrix models, Leslie and Lopez; continuous theory, renewal equation). Some of the more elementary theories on the growth of organisms (von Bertalanffy and others; allometric theories; cultures grown in a chemostat). Mathematical theories oftwo and more species systems (predator-prey, competition, symbosis; leading up to present-day research) and discussion of some similar models for chemical kinetics. Much emphasis on scrutiny of biological concepts as well as of mathematical structureof models in order to uncover both weak and strong points of models discussed. Mathematical treatment of differential equations in models stressing qualitative and graphical aspects, as well as certain aspects of discretization. Difference equation models.

Prerequisite: Advanced calculus, reasonable background in biology

Typically offered in Fall only

ST 772/BMA 772/MA 772  Biomathematics II  (3 credit hours)  

Continuation of topics of BMA 771. Some more advanced mathematical techniques concerning nonlinear differential equations of types encountered in BMA 771: several concepts of stability, asymptotic directions, Liapunov functions; different time-scales. Comparison of deterministic and stochastic models for several biological problems including birth and death processes. Discussion of various other applications of mathematics to biology, some recent research.

Prerequisite: BMA 771, elementary probability theory

Typically offered in Spring only

ST 773/BMA 773/MA 773/OR 773  Stochastic Modeling  (3 credit hours)  

Survey of modeling approaches and analysis methods for data from continuous state random processes. Emphasis on differential and difference equations with noisy input. Doob-Meyer decomposition of process into its signal and noise components. Examples from biological and physical sciences, and engineering. Student project.

Prerequisite: BMA 772 or ST (MA) 746

Typically offered in Spring only

ST 779  Advanced Probability for Statistical Inference  (3 credit hours)  

Sets and classes, sigma-fields and related structures, probability measures and extensions, random variables, expectation and integration, uniform integrability, inequalities, L_p-spaces, product spaces, independence, zero-one laws, convergence notions, characteristic functions, simplest limit theorems, absolute continuity, conditional expectation and conditional probabilities, martingales.

Prerequisite: ST 702

Typically offered in Spring only

ST 790  Advanced Special Topics  (1-6 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 793  Advanced Statistical Inference  (3 credit hours)  

Statistical inference with emphasis on the use of statistical models, construction and use of likelihoods, general estimating equations, and large sample methods. Includes introduction to Bayesian statistics and the jackknife and bootstrap.

Prerequisite: ST 702

Typically offered in Fall only

ST 801  Seminar  (1 credit hours)  

Typically offered in Fall and Spring

ST 810  Advanced Topics in Statistics  (1-3 credit hours)  

Typically offered in Fall and Spring

ST 835  Readings  (1-3 credit hours)  

Typically offered in Spring only

ST 841  Statistical Consulting  (1 credit hours)  

Participation in regularly scheduled supervised statistical consulting sessions with faculty member and client. Consultant's report written for each session. Regularly scheduled meetings with course instructor and other student consultants to present and discuss consulting experiences.

Prerequisite: ST 512 and ST 702

Typically offered in Fall only

ST 885  Doctoral Supervised Teaching  (1-3 credit hours)  

Teaching experience under the mentorship of faculty who assist the student in planing for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

Prerequisite: Doctoral student

Typically offered in Fall and Spring

ST 893  Doctoral Supervised Research  (1-9 credit hours)  

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

ST 895  Doctoral Dissertation Research  (1-9 credit hours)  

Dissertation Research

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

ST 896  Summer Dissertation Research  (1 credit hours)  

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

Prerequisite: Doctoral student

Typically offered in Summer only

ST 899  Doctoral Dissertation Preparation  (1-9 credit hours)  

For students who have completed all credit hour requirements, full-time enrollment, preliminary examination, and residency requirements for the doctoral degree, and are writing and defending their dissertations.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer