

M.S., M.A.
Ph.D.
(See Interdepartmental and Intercampus Programs.)
Thesis and Thesis Substitute
Danny Dyer
469 Pickard Hall, 8172723246
Tie Luo
429 Pickard Hall, 8172723597
Bernfeld, Dragan, Dyer, Han, Ladde, Liao, C. Liu, Luo, Nestell
Cordero, Gillespie, Hawkins, Heath, Korzeniowski, Su
Epperson, Jorgensen, Kojouharov, KribsZaleta, D. Liu, Shipman, Vancliff
Corduneanu, Greenspan
The objectives of the Mathematics Department's program at the master's level are (1) to develop the student's ability to do independent research and prepare for more advanced study in mathematics, and (2) to give advanced training to professional mathematicians, mathematics teachers, and those employed in engineering, scientific, and business areas.
Graduate work will be offered in algebra, complex and real variables, differential equations, functional analysis, geometry, mathematical education, numerical analysis, operations research, probability, statistics and topology.
For unconditional admission, a student must meet the following requirements:
Applicant's who do not satisfy requirements 2 or 3 above may be considered for unconditional admission if further review of their undergraduate transcript, recommendation letters, correspondence or direct interactions with mathematics faculty, and statement of professional or research interests indicates that they are qualified to enter the Master's Program without deficiency.
If an applicant does not meet a majority of standards for unconditional admission outlined above, they may be considered for probationary admission after careful examination of their application materials. Probationary admission requires that the applicant receive a B or better in the first 12 hours of graduate coursework at UTA.
Students who are unconditionally admitted will be eligible for available scholarship and/or fellowship support. Award of scholarships or fellowships will be based on consideration of the same criteria utilized in admission decisions. To be eligible, candidates must be new students coming to UTA in the fall semester, must have a GPA of 3.0 in the last 60 undergraduate credit hours plus any graduate credit hours as calculated by the Graduate School, and must be enrolled in a minimum of 6 hours of coursework in both long semesters to retain the fellowship.
Applicants may be denied admission if they have less than satisfactory performance on a majority of the admission criteria described above.
A deferred decision may be granted when a file is incomplete or when a denied decision is not appropriate. An applicant unable to supply all required documentation prior to the admission deadline, but who otherwise appears to meet admission requirements, may be granted provisional admission.
For unconditional admission a student must meet items 13 or 35.
Applicants who do not satisfy requirements 1 or 2 above may be considered for unconditional admission if further review of their undergraduate transcript, recommendation letters, correspondence or direct interactions with mathematics faculty, and statement of professional or research interests indicates that they are qualified to enter the Master's Program without deficiency.
If an applicant does not meet a majority of standards for unconditional admission outlined above, they may be considered for probationary admission after careful examination of their application materials. Probationary admission requires that the applicant receive a B or better in the first 12 hours of graduate coursework at UTA.
Applicants may be denied admission if they have less than satisfactory performance on a majority of the admission criteria described above.
A deferred decision may be granted when a file is incomplete or when a denied decision is not appropriate. An applicant unable to supply all required documentation prior to the admission deadline, but who otherwise appears to meet admission requirements, may be granted provisional admission.
For unconditional admission a student must meet the following requirements:
Applicants who do not satisfy requirements 2 or 3 above may be considered for unconditional admission if further review of their undergraduate transcript, recommendation letters, correspondence or direct interactions with mathematics faculty, and statement of professional or research interests indicates that they are qualified to enter the Doctoral Program without deficiency.
If an applicant does not meet a majority of standards for unconditional admission outlined above, they may be considered for probationary admission after careful examination of their application materials. Probationary admission requires that the applicant receive a B or better in the first 12 hours of graduate coursework at UTA.
Students who are unconditionally admitted will be eligible for available scholarship and/or fellowship support. Award of scholarships or fellowships will be based on consideration of the same criteria utilized in admission decisions. To be eligible, candidates must be new students coming to UTA in the fall semester, must have a GPA of 3.0 in the last 60 undergraduate credit hours plus any graduate credit hours as calculated by the Graduate School, and must be enrolled in a minimum of 6 hours of coursework in both long semesters to retain the fellowship.
Applicants may be denied admission if they have less than satisfactory performance on a majority of the admission criteria described above.
A deferred decision may be granted when a file is incomplete or when a denied decision is not appropriate. An applicant unable to supply all required documentation prior to the admission deadline, but who otherwise appears to meet admission requirements, may be granted provisional admission.
The Department of Mathematics offers master's degree programs in mathematics with additional emphasis in applied mathematics, computer science, mathematics education, pure mathematics, and statistics. All students are to use either the thesis or thesissubstitute plan.
All master's students must complete one of the following:
1. General Mathematics core requirements:
MATH 5300: Computer Programming and Applications
MATH 5307: Mathematical Analysis I
MATH 5308: Mathematical Analysis II
MATH 5333: Linear Algebra and Matrices
One of the following tracks:
Applied Mathematics: MATH 5350, 5351. and either 5320 or 5321
Computer Science: MATH (5348 and 5349) or (5338 and 5339), and either 5371 or 5373, and six approved hours in computer science engineering
Mathematical Education: Nine hours from MATH 5336, 5337, 53405348, 5352
Pure Mathematics: MATH 5317, and two from MATH 5304, 5331, 5334
2. General Statistics core requirements:
MATH 5300: Computer Programming and Applications
MATH 5307: Mathematical Analysis I
MATH 5333: Linear Algebra and Matrices
One of the following three courses:
MATH 5356: Applied Multivariate Statistical Analysis
MATH 5357: Sample Surveys
MATH 5392: Regression Analysis
MATH 5305: Statistical Methods
MATH 5312: Mathematical Statistics I
MATH 5313: Mathematical Statistics II
In addition:
1. Those students enrolled in the thesis substitute plan must take MATH 5395, and all except those in the computer science track must take at least nine other hours of electives.*
2. Those students enrolled in the thesis plan must take at least six hours of MATH 53985698, and all except those in the computer science track must take at least three other hours of electives.*
*Electives may not be chosen from MATH 5336, 5337, 53405348, 5352
Students in every degree plan must pass a final exam.
The master of arts program in the Department of Mathematics is designed for teachers who are interested in strengthening their understanding of mathematics and enriching their mathematics teaching. The program prepares teachers in subjects such as geometry, algebra, precalculus, analysis/calculus, probability, statistics, discrete mathematics, number theory, and the use of calculators and computers in the classroom. The program models a philosophy of teaching and learning mathematics that is consistent with the current Curriculum and Evaluations Standards for School Mathematics.
The requirements for the master of arts degree are 30 hours of graduate courses from the Department of Mathematics and a 3 hour project.
All students must complete the following:
1. Required Courses (6) and Project:
MATH 5340: Concepts and Techniques in Discrete Mathematics
MATH 5341: Concepts and Techniques in Geometry
MATH 5342: Concepts and Techniques in Algebra
MATH 5343: Concepts and Techniques in Probability and Statistics
MATH 5344: Concepts and Techniques in Computer
MATH 5345: Concepts and Techniques in Analysis
MATH 5395: Project  Individual, DirectorApproved Research
2. Elective Courses (4):
MATH 5300: Computer Programming and Applications
MATH 5305: Statistical Methods
MATH 5307: Mathematical Analysis I
MATH 5308: Mathematical Analysis II
MATH 5333: Linear Algebra and Matrices
MATH 5336: Concepts and Techniques in Number Theory
MATH 5337: Concepts and Techniques in Advanced Placement Calculus
MATH 5346: Concepts and Techniques in Problem Solving
MATH 5347: Concepts and Techniques in Modeling and Applications
MATH 5352: Concepts and Techniques in Precalculus
MATH 5380: Seminar  Study of Current Mathematics Topics
A unique and dynamic program leading to the Doctor of Philosophy degree in the mathematical sciences will aim at both real and demonstrated competency on the part of the student over material from various branches of mathematical sciences. The Doctor of Philosophy degree in Mathematical Sciences provides a program of study that may be tailored to meet the needs of those interested in applied or academic careers. This unique program allows students to pursue topics ranging from traditional mathematics studies to applied and theoretical problems in biology, chemistry, computer science, engineering, geology, information systems, physics and psychology. The nature of the dissertation will range from research in mathematics to the discovery and testing of mathematical models for analyzing given problems in sciences and in locating and developing mathematical and computational techniques for deducing the properties of these models as to solve these problems effectively and efficiently. Such dissertations will be concerned with research problems from such areas as pure mathematics, applied mathematics, probability, statistics, computer science, biology, biometry, chemistry, engineering, geology, information systems, physics, management sciences, and operational sciences.
The Department of Mathematics offers doctoral degree programs in Mathematics (algebra, applied mathematics, game theory, geometry, numerical analysis) and in Statistics.
All doctoral students must complete one of the following:
1. General MATHEMATICS core requirements:
MATH 5308: Mathematical Analysis II
MATH 5317: Real Analysis
MATH 5320: Ordinary Differential Equations
MATH 5322: Complex Variables
MATH 5327: Functional Analysis I
MATH 5331: Abstract Algebra I
One of the following four courses:
MATH 5319: Probability Theory
MATH 5321: Partial Differential Equations
MATH 5334: Differential Geometry
MATH 5338: Numerical Analysis I
In addition to the mathematics core requirements students are
also required to take three area related courses.
2. General STATISTICS core requirements:
MATH 5305: Statistical Methods
MATH 5307: Mathematical Analysis I
MATH 5308: Mathematical Analysis II
MATH 5312: Mathematical Statistics I
MATH 5313: Mathematical Statistics II
MATH 5317: Real Analysis
MATH 5319: Probability Theory
MATH 5322: Complex Variables
MATH 5327: Functional Analysis I
MATH 5333: Linear Algebra
In addition to the statistics core requirements, students are
also required to take two statistics courses from MATH 5311, 5314,
5353, 5354, 5356, 5357, 5358, 5359, 6353, 6356, 6357.
Students in every degree plan must pass a comprehensive exam.
* Effective for students entering the graduate program starting Fall 2001. Returning students may choose the old core requirements.
For additional information on the mathematical sciences program, see the program entry in the Interdepartmental and Intercampus Programs section of this catalog.
The grade of R (research in progress) is a permanent grade; it cannot be changed by completing course requirements in a later semester. To receive credit for an Rgraded course, the student must continue to enroll in the course until a passing grade is received.
An incomplete grade (the grade of X) cannot be given in a course that is graded R, nor can the grade of R be given in a course that is graded X. To receive credit for a course in which the student earned an X, the student must complete the course requirements. A grade of X cannot be changed by enrolling again in the course in which an X was earned. At the discretion of the instructor, a final grade can be assigned through a change of grade form.
Threehour thesis courses and three and sixhour dissertation courses are graded R/F/W only (except social work thesis courses). The grade of P (required for degree completion for students enrolled in thesis or dissertation programs) can be earned only in six or ninehour thesis courses and ninehour dissertation courses. In the course listings below, Rgraded courses are designated either "Graded P/F/R" or "Graded R." Occasionally, the valid grades for a course change. Students should consult the appropriate Graduate Advisor or instructor for valid grade information for particular courses. (See also the sections titled "R" Grade, Credit for Research, Internship, Thesis or Dissertation Courses and Incomplete Grade in this catalog.)
Course fee information is published in the online Student Schedule of Classes at www.uta.edu/schedule. Please refer to this Web site for a detailed listing of specific course fees.
5191. SEMINAR FOR TEACHING ASSISTANTS (01). This course is mandatory for all mathematics graduate teaching assistants. Students will be instructed on classroom procedures and strategies and will be required to deliver lectures under the supervision of math faculty. The purpose is to develop students to be effective lecturers. Admittance to this course is restricted to Math TAs.
5300. COMPUTER PROGRAMMING AND APPLICATIONS (30). Introduction to computing techniques utilizing an algorithmic language such as Fortran. Applications from various areas of numerical analysis. Prerequisite: consent of the instructor.
5301. MATHEMATICAL COMPUTER RESOURCES (30). Introduction to hardware and software available to the scientific graduate student whose studies involve numerical computations. Utilization of the various mathematic/statistical libraries is emphasized rather than programming of mathematic/statistical routines. Prerequisite: MATH 5300 or its equivalent.
5302. FUNDAMENTALS OF MATHEMATICAL SCIENCES I (30). Matrices and operators, linear spaces, multivariable calculus, dynamical systems, applications. Prerequisites: MATH 3318 and 3330 or consent of instructor.
5303. FUNDAMENTALS OF MATHEMATICAL SCIENCES II (30). Wave propagation, potential theory, complex variables, transform techniques, perturbation techniques, diffusion, applications. Prerequisite: MATH 5302 or consent of instructor.
5304. GENERAL TOPOLOGY (30). Introduction to fundamentals of general topology. Topics include product spaces, the Tychonoff theorem, Tietzes Extension theorem, and metrization theorems. Prerequisite: MATH 4304 or 4335.
5305. STATISTICAL METHODS (30). Topics include descriptive statistics, numeracy, and report writing; basic principles of experimental design and analysis; regression analysis; data analysis using the SAS package. Prerequisite: consent of the instructor.
5307. MATHEMATICAL ANALYSIS I (30). Elements of topology, real and complex numbers, limits, continuity, and differentiation, functions of bounded variation, RiemannStieltjes integrals. Prerequisite: MATH 4335 or consent of Graduate Advisor.
5308. MATHEMATICAL ANALYSIS II (30). Analysis in Rn, limits, continuity, Jacobian, extremum problems, multiple integrals, sequences and series of functions, Lebesque integral. Prerequisite: MATH 5307 or consent of Graduate Advisor.
5310. MATHEMATICAL GAME THEORY (30). Two person null sum games. Bimatrix games and Nash equilibrium points. Noncooperative games, existence theorem. Cooperative games, core, Shapley value, the nucleolus. Cost allocation. Market games. Simple games and voting. Prerequisite: MATH 3330.
5311. APPLIED PROBABILITY AND STOCHASTIC PROCESSES (30). Topics include conditional expectations, law of large numbers and central limit theorem, stochastic processes, including Poisson, renewal, birthdeath, and Brownian motion. Prerequisite: MATH 3313 or equivalent.
5312. MATHEMATICAL STATISTICS I (30). Basic probability theory, random variables, expectation, probability models, generating functions, transformations of random variables, limit theory. Prerequisite: MATH 5307 or concurrent registration or consent of instructor.
5313. MATHEMATICAL STATISTICS II (30). Theories of point estimation (minimum variance unbiased and maximum likelihood), interval estimation and hypothesis testing (NeymanPearson and likelihood ratio tests), regression analysis and Bayesian inference. Prerequisite: MATH 5312.
5314. EXPERIMENTAL DESIGN (30). This course covers the classical theory and methods of experimental design, including randomization, blocking, oneway and factorial treatment structures, confounding, statistical models, analysis of variance tables and multiple comparisons procedures. Prerequisite: MATH 5305 or equivalent.
5315. GRAPH THEORY (30). Algorithms for problems on graphs. Trees, spanning trees, connectedness, fundamental circuits. Eulerian graphs and Hamiltonian graphs. Graphs and vector spaces, matrices of a graph. Covering and coloring. Flows. Prerequisite: MATH 3314.
5316. COMBINATORIAL OPTIMIZATION (30). Shortest paths. Minimum weight spanning trees and matroids. Matchings and optimal assignment. Connectivity. Flows in networks, applications. Prerequisite: MATH 3314.
5317. REAL ANALYSIS FOR THE MATHEMATICAL SCIENCES (30). Lebesque measure and integration on Rn. Study of LP spaces. Abstract measure and integration. Prerequisite: MATH 5308.
5318. FUNDAMENTALS OF STOCHASTIC ANALYSIS (30). General properties of stochastic processes, processes with independent increments, martingales, limit theorems including invariance principle, Markov processes, stochastic integral, stochastic differential. Prerequisite: MATH 5308.
5319. PROBABILITY THEORY (30). Probability spaces, random variables, filtrations, conditional expectations, martingales, strong law of large numbers, ergodic theorem, central limit theorem, Brownian motion and its properties. Prerequisite: MATH 5308.
5320. APPLIED DIFFERENTIAL EQUATIONS (30). Fundamentals of the theory of systems of ordinary differential equations: existence, uniqueness, and continuous dependence of solutions on data; linear equations, stability theory and its applications, periodic and oscillatory solutions. Prerequisites: MATH 5307 and 5333.
5321. APPLIED PARTIAL DIFFERENTIAL EQUATIONS (30). General first order equations. Basic linear theory for elliptic, hyperbolic, and parabolic second order equations, including existence and uniqueness for initial and boundary value problems. Prerequisites: MATH 5307 and 5333.
5322. COMPLEX VARIABLES I (30). Fundamental theory of analytic functions, residues, conformal mapping and applications. Prerequisite: MATH 5307.
5324. APPLIED COMPLEX VARIABLES (30). Analytic functions of a complex variable; the line integral, residues, applications; conformal mappings; harmonic functions and applications to physical problems; elements of transform theory. Prerequisite: MATH 3335 or consent of the instructor.
5325. ALGEBRAIC NUMBER THEORY (30). Field extensions, number fields and number rings, ramification theory, class groups, elliptic curves and their group structure, applications to Fermat's last theorem. Prerequisites: MATH 3321.
5326. ALGEBRAIC TOPOLOGY (30). Fundamental groups, covering space, singular homology, relative homology, MayerVietoris sequence, Betti numbers, Euler characteristic. Prerequisites: MATH 3321, MATH 3335.
5327. FUNCTIONAL ANALYSIS I (30). Introduction to Hilbert and Banach spaces: HahnBanach, BanachSteinhaus, and closed graph theorems. Riesz representation theorem and bounded linear operators in Hilbert space. Prerequisite: MATH 5308.
5328. FUNCTIONAL ANALYSIS II (30). The theory of distributions and Sobolev spaces, with applications to differential equations. Compact operators and Fredholm theory. Spectral theory for unbounded operators. Prerequisite: MATH 5327.
5330. ALGEBRAIC GEOMETRY (30). Theory of ideals in polynomial rings, Nullstellensatz, Hilbert's basis theorem, Groebner basis and computation in polynomial rings, affine and projective varieties, singular and smooth points on varieties. Prerequisites: MATH 3321.
5331. ABSTRACT ALGEBRA I (30). Zorn's Lemma, groups, including free groups and dihedral groups. Rings including factorization, localization, rings of polynomials, and formal power series. An introduction to modules. Prerequisite: MATH 3321.
5332. ABSTRACT ALGEBRA II (30). Modules, including free, projective, and injective. Exact sequences and tensor products of modules. Chain conditions, primary decomposition, Noetherian rings and modules. Prerequisite: MATH 5331.
5333. LINEAR ALGEBRA AND MATRICES (30). Liner spaces, linear transformations, vector norms, Gaussian elimination, Jordan form, eigenvalues, quadratic forms, and related topics. Prerequisite: MATH 3330 or consent of instructor.
5334. DIFFERENTIAL GEOMETRY (30). Introduction to the theory of curves and surfaces in three dimensional Euclidean space. Prerequisite: MATH 4334 or 4335.
5335. APPLIED VECTOR AND TENSOR ANALYSIS (30). Vector algebra, vector and tensor calculus; applications to differential geometry, engineering sciences, and dynamics including surface theory, geodiscs, minimal surfaces, elasticity, particle dynamics, special relativity, and general relativity. Prerequisite: MATH 5302.
5336. NUMBER THEORY (30). Topics include
mathematical induction, fundamental theorem or arithmetic, inequalities,
special sequences and sums, divisibility properties, greatest common
divisor,
division and Euclidean algorithm, properties of congruence
and Diophanune equations.
5337. ADVANCED PLACEMENT CALCULUS (30). Topics studied include limits, continuity, differentiation, integration, numerical approximations, applications and Taylor series. All topics will be studied in a manner consistent with the AP Examination and grading process.
5338. NUMERICAL ANALYSIS I (30). Solution of equations, interpolation and approximation, numerical differentiation and quadrature, and solution of ordinary differential equations. Prerequisite: MATH 3345.
5339. NUMERICAL ANALYSIS II (30). Rigorous treatment of numerical aspects of linear algebra and numerical solution of boundary value problems in ordinary differential equations: also, an introduction to numerical solution of partial differential equations. Prerequisite: MATH 3345.
5340. DISCRETE MATHEMATICS (30). Topics from combinatorics, sequences and recurrence relations, finite graph theory, and applications of matrices with the use of a graphing calculator and other appropriate technology.
5341. MATHEMATICS FOR TEACHERSGEOMETRY (30). Selected materials from geometry.
5342. MATHEMATICS FOR TEACHERSALGEBRA (30). Selected materials from algebra, including probability, statistics, and theory of equations.
5343. PROBABILITY AND STATISTICS (30). Consideration of (1) exploring data: descriptive statistics of situations involving one and two variables; (2) anticipating patterns: probability and simulation; (3) design of experiments and planning a study; (4) statistical inference: confirming models. Use of a graphing calculator and other appropriate technology.
5344. MATHEMATICS FOR TEACHERSCOMPUTER (30). Selected materials from the literature on the usage of microcomputers in the classroom.
5345. MATHEMATICS FOR TEACHERSANALYSIS (30). Selected materials from analysis including concepts and topics consistent with precalculus and elementary calculus.
5346. MATHEMATICS FOR TEACHERSPROBLEM SOLVING (30). Instruction in the application of various heuristics or general problem strategies.
5347. MATHEMATICAL MODELING WITH APPLICATIONS (30). Topics studied include algebraic, graphical, geometrical and numerical techniques to model and solve applied problems involving money, science, algebra, geometry, probability, statistics and advanced applications in both mathematical and nonmathematical situations.
5348. ANALYSIS OF NUMERICAL METHODS I (30). Rigorous treatment of topics in numerical analysis including roundoff error effects, solution of linear and nonlinear systems, interpolation, and numerical integration. Emphasis on analysis of methods as well as computation. Prerequisites: MATH 3335 and 3345.
5349. ANALYSIS OF NUMERICAL METHODS II (30). Continuation of MATH 5348. Topics include QR decomposition, eigenvalue approximation, singular value decomposition, least squares problems, numerical approximation of ODE's and PDE's, and iterative methods for large sparse systems. Emphasis on analysis of methods as well as computation. Prerequisite: MATH 5348.
5350. APPLIED MATHEMATICS I (30). Development of models arising in the natural sciences and in engineering. Emphasis will be on the mathematical techniques and theory needed to analyze such models; these include aspects of the theory of differential and integral equations, boundary value problems, theory of distributions and transforms. Prerequisites: MATH 5307 and 5333.
5351. APPLIED MATHEMATICS II (30). Continuation of MATH 5350; models arising in the physical sciences whose analysis includes such topics as the theory of operators in a Hilbert space, variational principles, branching theory, perturbation and stability analysis. Prerequisite: MATH 5350.
5352. CONCEPTS AND TECHNIQUES IN PRECALCULUS (30). Topics studies include Functions (transcendental, inverse, parametric, ploar, transformations), asymptotic behavior, conics, sequences, complex numbers.
5353. APPLIED LINEAR MODELS (30). The course covers, at an operational level, three topics: 1) the univariate linear model, including a selfcontained review of the relevant distribution theory, basic inference methods, several parameterizations for experimental design and covariateadjustment models and applications, and power calculation; 2) the multivariate linear model, including basic inference (e.g. the four forms of test criteria and simultaneous methods), applications to repeated measures experiments and power calculation; and 3) the univariate mixed model, including a discussion of the likelihood function and its maximization, approximate likelihood inference, and applications to complex experimental designs, missing data, unbalanced data, time series observations, variance component estimation, random effects estimation, power calculation and a comparison of the mixed model's capabilities relative to those of the classical multivariate model. Knowledge of the SAS package is required. Prerequisite: MATH 5358 (Regression Analysis) or equivalent.
5354. CATEGORICAL DATA ANALYSIS (30). This course covers classical methods for analyzing categorical data from a variety of response/factor structures (univariate or multivariate responses, with or without multivariate factors), based on several different statistical rationales (weighted least squares, maximum likelihood and randomizationbased). Included are logistic regression, multiple logit analysis, mean scores analysis, observer agreement analysis, association measures, methods for complex experimental designs with categorical responses and Poisson regression. The classical loglinear model for the association structure of multivariate responses is briefly reviewed. Randomizationbased inference (e.g. MantelHaenzel) is discussed as well. The necessary distribution theory (multinomial, asymptotics of weighted least squares and maximum likelihood) are discussed at an operational level. Knowledge of the SAS package is required. Prerequisite: MATH 5358 (Regression Analysis).
5355. STATISTICAL THEORY FOR RESEARCH WORKERS (30). Designed for graduate students not majoring in mathematics. Topics include basic probability theory, distributions of random variables, point estimation, interval estimation, testing hypotheses, regression, and an introduction to analysis of variance. Graduate credit not given to math majors. Prerequisite: MATH 2325.
5356. APPLIED MULTIVARIATE STATISTICAL ANALYSIS (30). Statistical analysis for data collected in several variables, topics including sampling from multivariate normal distribution, Hotelling's T'2, multivariate analysis of variance, discriminant analysis, principal components, and factor analysis. Prerequisite: MATH 5312 or consent of instructor.
5357. SAMPLE SURVEYS (30). A comprehensive account
of sampling theory and methods, illustrations to show
methodology and practice, simple random sampling, stratified random
sample, ratio estimates, regression estimates, systematic sampling,
cluster
sampling, and nonsampling errors. Prerequisite: MATH 5312
or consent of instructor.
5358. REGRESSION ANALYSIS (30). A comprehensive course including multiple linear regression, nonlinear regression and logistic regression. Emphasis is on modeling, inference, diagnostics and application to real data sets. The course begins by developing a toolbox of methods via a sequence of guided homework assignments. It culminates with projects based on consultinglevel data analysis problems involving stratification, covariate adjustment and messy data sets. Some knowledge of the SAS package is required. Prerequisites: MATH 5305, basic knowledge of matrices.
5359. SURVIVAL ANALYSIS (30). This course covers analysis of lifetime data, which has applications to actuarial science and health fields. Topics include the survivor function, hazard function, censoring, parametric regression models (e.g. the weibull), nonparametric regression models (e.g. the Cox proportional hazards model), categorical survival data methods, competing risks and methods for multivariate survival data. Knowledge of the SAS package is required. Prerequisites: MATH 5358 (Regression Analysis) and preferably MATH 5313. (Students without 5313 can still succeed if they have some basic calculusbased probability, such as MATH 3313).
5361. APPLIED CALCULUS OF VARIATION (30). Functionals, variation, extremization, Euler's equation, direct and indirect approximation methods; applications to mechanics and control theory. Prerequisite: MATH 5302.
5362. MATHEMATICS OF LINEAR PROGRAMMING (30). The simplex method and the revised simplex method. Linear algebra for polyhedra and polytopes. Duality theory. Sensitivity analysis. Applications to transportation problems, network flow problems, matrixgames and scheduling problems. Integer programming. Quadratic programming. Prerequisite: MATH 3330.
5363. OSCILLATIONS AND WAVES (30). Development of methods and results related to phenomena in nature that exhibit oscillatory motion; mathematical techniques include Fourier series, ordinary and partial differential equations, and the theory of almost periodic functions. Prerequisite: MATH 3318.
5364. INTRODUCTION TO MATHEMATICAL CONTROL THEORY (30). Systems in science, engineering, and economics and their mathematical description by means of functional equations (ordinary, partial, integral, delaytype). Basic properties of various classes of systems: observability, controllability, stability, and oscillating systems; optimal control problems and applications. Prerequisite: MATH 3318 or 4320.
5365. BIOMATHEMATICS (30). Mathematical techniques used in modeling such as perturbation theory, dimensional analysis, Fourier analysis, and differential equations. Applications to morphogenetics, population dynamics, compartmental systems, and chemical kinetics. Prerequisite: consent of instructor.
5366. INTRODUCTION TO NEURAL AND COGNITIVE MODELING (30). Principles of neural network modeling; application of these principles to the simulation of cognitive processes in both brains and machines; models of associative learning, pattern recognition, and classification. Prerequisite: consent of instructor.
5371. NUMERICAL LINEAR ALGEBRA (30). Methods and theory related to the numerical solution of linear algebraic systems and eigenvalueeigenvector problems. Both direct and iterative techniques are developed and discussed for full and sparse systems. Convergence, convergence rates, and error analysis. Prerequisites: MATH 3330 and 3345.
5372. NUMERICAL FUNCTIONAL ANALYSIS (30). Numerical implementation of abstract operator methods, including Newton's method for linear and nonlinear algebraic, transcendental, differential, integral, and functional equations; some aspects of approximation theory. Prerequisite: MATH 5308.
5373. NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS (30). Theoretical analysis of methods for approximating solutions of initial value problems, boundary value problems, and problems with periodic solutions; existence, uniqueness, convergence, stability, and error analysis are stressed for both single equations and for systems. Prerequisite: MATH 5338 or consent of instructor.
5374. NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS (30). Theoretical analysis for numerical methods for approximating solutions of elliptic, parabolic, hyperbolic, mixed, and systems of partial differential equations problems; existence, uniqueness, convergence, stability, and error analysis are stressed. Prerequisite: MATH 5339 or consent of instructor.
5380. SEMINAR (30). Current topics in mathematics, may be repeated for credit twice. Prerequisite: consent of instructor.
5391. SPECIAL TOPICS IN MATHEMATICS (30). Topics in mathematics assigned individual students or small groups. Faculty members closely supervise the students in their research and study. In areas where there are only three hours offered, the special topics may be used by students to continue their study in the same area. Graded P/F/R. Prerequisite: permission of instructor.
5392. SELECTED TOPICS IN MATHEMATICS (30)/(31). May vary from semester to semester depending upon need and interest of the students. May be repeated for credit. Prerequisite: permission of instructor.
5395. SPECIAL PROJECT. Graded P/F/R. Prerequisite: permission of Graduate Advisor.
5398, 5698. THESIS. 5398 graded R/F only; 5698 graded P/F/R. Prerequisite: permission of Graduate Advisor.
6313. TOPICS IN PROBABILITY AND STATISTICS (30). May be repeated for credit when the content changes.
6353. GENERALIZED LINEAR MODELS (30). This course
covers modern methods for analyzing bernoulli, multinomial and
count data. It begins with a development of generalized linear model
theory, including the exponential family, link function and
maximum likelihood. Second is a discussion of the case of models
for independent observations. Next is a discussion of models for
repeated
measures, based on quasilikelihood methods. These include
models (such as markov chains) for categorical time series. Next is a
treatment of models with random effects. Finally is a discussion of methods
for handling missing data. Knowledge of the SAS package is
required. Prerequisites: MATH 5358 (Regression Analysis) and
preferably MATH 5313. (Students without 5313 can still
succeed but must deal with the slightly higher mathematical level of this course).
6356. TIME SERIES ANALYSIS (30). This course covers classical methods of time series analysis, for both the time and frequency domains. For covariance stationary series, these include ARIMA modeling and spectral analysis. For nonstationary series, they include methods for detrending and filtering. Also included is a treatment of multivariate series, as well as a discussion of the Kalman filter statespace model. Knowledge of the SAS package is required. Prerequisites: MATH 5358 (Regression Analysis) and MATH 5313.
6357. NONPARAMETRIC STATISTICS (30). This is a survey of classical nonparametric methods for inference in standard observational settings (onesample, twosample, ksamples and the univariate linear model), and includes a development of Ustatistics, rank statistics and their asymptotic distribution theory. The mathematical level is fairly high. Prerequisite: MATH 5313.
6391. SPECIAL TOPICS IN MATHEMATICS (30). Faculty directed individual study and research. May be repeated for credit when the content changes. Graded P/F/R.
DISSERTATIONSee Mathematical Sciences.
A limited number of undergraduate mathematics courses may be applicable to a graduate program in mathematics if approved by the Graduate Advisor. These must be chosen from the following list and shall not exceed six hours total credit.
4303. INTRODUCTION OF TOPOLOGY
4313. APPLICATIONS OF MATHEMATICAL STATISTICS
4314. ADVANCED DISCRETE MATHEMATICS
4320. ADVANCED DIFFERENTIAL EQUATIONS
4321. INTRODUCTION TO ABSTRACT ALGEBRA II
4322. INTRODUCTION TO COMPLEX VARIABLES
4324. INTRODUCTION TO PARTIAL DIFFERENTIAL EQUATIONS
4334. ADVANCED MULTIVARIABLE CALCULUS
4335. ANALYSIS II
4345. NUMERICAL ANALYSIS AND COMPUTER APPLICATIONS II