Florida State University Graduate Bulletin 2007-2009
Department of Statistics
College of Arts and Sciences
Chair:Dan McGee; Director, Statistical Consulting Center: Ramsier; Professors: Hollander, Huffer, McGee, Niu; Associate Professors: Bunea, Patrangenaru, Song, Srivastava, Wegkamp; Assistant Professors: Chicken, Dixon, Wu; Assistant in Statistics: Bose; Professors Emeriti: Leysieffer, Marsaglia, Meeter, Sethuraman; Associate in Statistics: Ramsier
The Department of Statistics offers programs leading to the master of science (MS) in Statistics, the MS in Biostatistics, and the doctor of philosophy (PhD) degrees. Emphases in probability theory and stochastic processes, mathematical statistics, biostatistics, and applied statistics are possible.
The MS program prepares the student for future graduate study toward the doctorate or for professional careers in industry and government. There are two options in the MS in Statistics program. The applied statistics option is a four-semester program that emphasizes the statistical and consulting skills necessary for a professional statistician immediately employable in business, industry, and government. While some statistical theory is taught, the emphasis is on the proper applications of statistical techniques. Within this applied statistics option, the student may pursue a course of study that emphasizes computational biology. The mathematical statistics option is a four-semester program together with a comprehensive examination, which emphasizes both applied and theoretical statistics. With the deeper training in the theory of statistics this program provides, students are prepared for immediate employment in industry. It also prepares them for continuation into the doctoral program in the department. MS students planning to continue to the doctoral program should select this option. For more information, including revisions or additions to options and programs, please contact the department by phone at (850) 644-3218, or visit the departmental Web site at http://stat.fsu.edu.
The MS program in Biostatistics prepares students for future careers in private and public sector research and health care settings. Students gain the ability to apply statistical principles, processes, applications, and analytic methods to design, implement, and analyze health related studies including both experimental (clinical trials) and observational (epidemiological) studies. The degree requirements include course work in biostatistics and statistical theory and methods.
The PhD program prepares the student for research, university teaching, and research participation in government and industry. Doctoral programs are planned in order to permit study up to the research level in two specializations, only one of which need be in the Department of Statistics; examples are probability and mathematical statistics, probability and functional analysis, mathematical statistics and economic theory, and mathematical statistics and population genetics. The dissertation must constitute scholarly research in the advancement of knowledge in the theory or utilization of probability and statistics.
The Department of Statistics offers a wide selection of graduate and undergraduate courses in statistical methods for non-majors with minimal background in mathematics. Course outlines for recent offerings of these courses are available on the departmental Web site.
Facilities
The Department of Statistics provides statistical consultation on University research through the Statistical Consulting Center. The center works cooperatively with faculty and graduate students in research and plays a role with research teams in the design of experiments and the analysis of data. Graduate students who anticipate theses and dissertations involving statistical analyses should plan their programs to include basic training in statistics in order to take full advantage of the services of the center.
The Department of Statistics has a local area network of workstations and PC's running Solaris, IRIX, Linux and Windows operating systems, as well as networked printers. Linked to the campus-wide network, they may be used to access the university operated supercomputers, other university systems, and Internet and Internet2 networks.
Faculty members of the Department of Statistics are engaged in basic research supported by grants and contracts with such agencies as the National Science Foundation, the National Institutes of Health, the National Imagery and Mapping Agency, and the United States Army Research Office. The department was one of four units of the University that participated in a National Science Foundation Science Development grant designed to develop centers of excellence in the sciences. The program of the department is currently designated by the State of Florida as one of its programs of distinction.
The Department of Statistics maintains a departmental library and reading room, the Wilcoxon Memorial Room, and provides facilities for computation in connection with course work and research. The Laboratory for Computational Vision, funded by federal grants, houses high performance Silicon Graphics computers for large computations and visualizations, and sophisticated imaging facilities. Ongoing research includes development of probability models and computational algorithms for automated recognition of objects from their camera images. Participating students gain expertise in computational statistics, imaging concepts, and high performance computing. The lab is an important part of the department's thrusts in multi-disciplinary research.
College Requirements
Please review all college-wide degree requirements summarized in the "College of Arts and Sciences" chapter of this Graduate Bulletin.
Admission Requirements
Prior work in statistics is not a requirement for admission to graduate study. Normally, students who elect the mathematical statistics MS option should have the essentials of an undergraduate mathematics major. Students who have not had mathematics MAA 4226–4227 or the equivalent must expect to progress at a somewhat slower rate. Students who elect the applied statistics option should have had the equivalent of three semesters of calculus. A score of at least 1100 on the aptitude test of the Graduate Record Examinations (GRE) is required. Individual programs of study are developed in consultation with the departmental faculty through supervisory committees appointed during the first semester of graduate study.
Master of Science Degree
The following options of the master of science degree are possible:
1. A program emphasizing applied statistics which can normally be completed in four semesters without a comprehensive examination
or
2. A program emphasizing biostatistics, which results in an MS in Biostatistics degree. No comprehensive examination is required.
or
3. Undergraduates may enroll in a 5-year combined BS/MS degree, which requires no comprehensive examination.
or
4. A program emphasizing mathematical statistics, which can normally be completed in four semesters with a comprehensive examination
Master of science degree candidates intending to continue to the doctoral program in this department must select the mathematical statistics option. A detailed description of the master of science program in statistics can be obtained on the department's Web site. Full course programs are prepared in consultation with the student's master's supervisory committee.
The Doctor of Philosophy Degree
Doctoral students concentrate course work in two areas of specialization, to the extent that the course work brings them to the frontiers of knowledge in the areas chosen. Unusual flexibility exists within this program, in that only one of the areas needs to be chosen from within the department.
The department offers an Interdisciplinary Option (IO) within the doctoral program. This program is consistent with the departments emphasis on interdisciplinary research. IO students select an area of interest in a field related to statistics. To begin taking graduate level courses in an area of interest, IO students are recommended to have prior course work or experience in their selected area. IO students take at least three graduate level courses in their area of interest as well as the core courses required for the standard PhD option.
The department also offers a standard PhD program that features concentration in four areas: 1) probability theory and stochastic processes; 2) statistical inference; 3) applied statistics, including biostatistics; and 4) reliability theory and survival analysis. A student may choose both concentrations from the above areas. A student preparing for an academic career in a department of mathematics may wish to combine study in probability and stochastic processes with functional analysis in the Department of Mathematics. A student interested in applying statistics to environmental problems might combine study in applied statistics with ecological studies in the Department of Biological Science. Many such combinations are possible and have been completed by graduates of our program.
The course program must include a minimum of twelve (12) semester hours at the 6000 level, with the selection of courses subject to the approval of the student's supervisory committee. There is no formal language requirement, although a student's advisory committee may suggest reading knowledge of a foreign language if that is relevant to the research work being planned or the student's career plans.
Course programs and exact degree requirements are determined individually for students through consultation with their supervisory committee. Many students enter the doctoral program through the master's program. Students entering the program with equivalent work at other institutions will not be required to repeat it here. In preparing a course program, however, students should keep in mind that they are required to pass the PhD qualifying examination as one step toward the degree.
Definition of Prefix
STA—Statistics
Graduate Courses
STA 5106. Computational Methods in Statistics I (3). Prerequisites: At least one previous course in statistics above STA 1013; some previous programming experience; or permission of the instructor. Matlab and a programming language (C/Fortran) will be used. Floating point arithmetic, numerical matrix analysis, multiple regression analysis, nonlinear optimization, root finding, numerical integration, Monte Carlo sampling.
STA 5107. Computational Methods in Statistics II (3). Prerequisite: STA 5106 or permission of the instructor. Matlab and a programming language (C/Fortran) will be used. A continuation of STA 5106 in computational techniques for linear and nonlinear statistics. Statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, Gibbs sampling.
STA 5126. Introduction to Applied Statistics. (4). Prerequisite: MAC 1105. Graduate credit for non-statistics majors only. Data collection, sample variation, basic probability, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation, regression, nonparametric statistics.
STA 5166. Statistics in Applications I (3). Prerequisite: MAC 2313. Comparison of two treatments, random sampling, randomization and blocking with two comparisons, statistical inference for means, variances, proportions and frequencies, and analysis of variance.
STA 5167. Statistics in Applications II (3). Prerequisite: STA 5166. Special designs in analysis of variance, linear and nonlinear regression, least squares and weighted least squares, case analysis, model building, nonleast squares estimation.
STA 5168. Statistics in Applications III (3). Prerequisite: STA 5167. Response surface methods, repeated measures and split-plot designs, basic log-linear and logit models for two-way and multiway tables, and multinomial response models.
STA5172. Statistics for Epidemiology (3). Prerequisite: STA 2171. This course introduces the statistical methods developed for and used in epidemiology. Topics to be covered include statistical design issues in epidemiological studies, measures of disease occurrence, measures of association, and adjusting for confounding without and with multivariate models.
STA 5176. Statistical Modeling with Application to Biology (3). Prerequisites: STA 4442 or 5440. Maximum likelihood principle, missing data and EM algorithm; assessment tools such as bootstrap and cross-validation; Markov chain and hidden Markov models; classification and regression trees (CART); Bayesian models and Markov Chain Monte Carlo algorithms.
STA 5179. Applied Survival Analysis (3). Prerequisite: STA 2171. This course is an applied introduction to survival analysis, one of the most commonly used analytic tools in biomedical studies. Topics to be covered include censoring and time scale, descriptive methods, parametric methods, and regression methods, which stress the proportional hazards model.
STA 5206. Analysis of Variance and Design of Experiments (3). Prerequisite: One of STA 2122, 4322,or 5126. Graduate credit for non-statistics majors only. One and two-way classifications, nesting, blocking, multiple comparisons, incomplete designs, variance components, factorial designs, confounding.
STA 5207. Applied Regression Methods (3). Prerequisite: One of STA 2122, 4322, or 5126. Graduate credit for non-statistics majors only. General linear hypothesis, analysis of covariance, multiple correlation and regression, response surface methods.
STA 5208. Linear Statistical Models (3). Prerequisite: STA 5327.
STA 5225. Sample Surveys (3). Prerequisite: A course in statistics above STA 1013 or consent of instructor. Simple, stratified, systematic, and cluster random sampling. Ratio and regression estimation. Multistage sampling.
STA 5238. Applied Logistic Regression (3). Prerequisite: STA 2171. This course is an applied introduction to logistic regression, one of the most commonly used analytic tools in biomedical studies. Topics include fitting the model, interpretation of the model, model building, assessing model fit, model validation, and model uncertainty.
STA 5244. Clinical Trials (3). Prerequisite: STA 2171. This course offers an introduction to clinical trials. Topics to be covered include defining the research question, basic study designs, randomization, blinding, sample size, baseline assessment, data collection and quality control, monitoring, issues in data analysis, closing out a trial, reporting and interpreting results, and issues in multicenter trials.
STA 5323. Introduction to Mathematical Statistics (3). Prerequisite: MAC 2313 or equivalent. Distributions of random variables, conditional probability and independence, multivariate distributions, sampling distributions, Bayes' rule, counting problems, expectations.
STA 5325. Mathematical Statistics (3). Prerequisites: STA 4442 or 5440 and either MAC 2313 or STA 5326. Sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian models.
STA 5326. Distribution Theory and Inference (3). Prerequisite: MAC 2313; at least one previous course in statistics or probability. Introduction to probability, random variables, distributions, limit laws, conditional distributions, and expectations.
STA 5327. Statistical Inference (3). Prerequisites: STA 5326, 5446. Statistical inference viewed at a measure-theoretic level.
STA 5334. Limit Theory of Statistics (3). Prerequisite: STA 5327. Convergence of distribution and random variables, laws of large numbers, central limit theorems, asymptotic distributions, asymptotic efficiency, rates of convergence, the weak invariance principle.
STA 5440. Introductory Probability I (3). Prerequisite: MAC 2311. Random variables, probability of random variables, generating functions, central limit theorem, laws of large numbers.
STA 5446. Probability and Measure (3). Prerequisites: MAA 4227, 5307, or the equivalent. Classes of sets, probability measures, construction of probability measures, random variables, expectation and integration, independence and product measures.
STA 5447. Probability Theory (3). Prerequisites: STA 5326, STA 5446.
STA 5507. Applied Nonparametric Statistics (3). Prerequisite: A course in statistics above STA 1013 or consent of instructor. Applications of nonparametric tests, estimates, confidence intervals, multiple comparison procedures, multivariate nonparametric methods, and nonparametric methods for censored data.
STA 5666. Statistics for Quality and Productivity (3). Prerequisites: STA 5167 or consent of the instructor, and either STA 4322 or 5126. Statistics for quality control and productivity; graphical methods; control charts; design and experiment for product and process improvement.
STA 5676. Reliability Theory and Life Testing (4). Prerequisite: A basic course in probability and statistics.
STA 5707. Applied Multivariate Analysis (3). Prerequisite: One of STA 5167, 5207, or 5327. Inference about mean vectors and covariance matrices, canonical correlation, principal components, discriminant analysis, cluster analysis, computer techniques.
STA 5746. Multivariate Analysis (3). Prerequisite: STA 5327.
STA 5807r. Topics in Stochastic Processes (3). Prerequisite: STA 5326. May be repeated to a maximum of twelve (12) semester hours.
STA 5856. Time Series and Forecasting Methods (3). Prerequisite: STA 5126, QMB 3200, or equivalent. Autoregressive, moving average and mixed models, autocovariance and autocorrelation functions, model identification, forecasting techniques, seasonal model identification estimation and forecasting, intervention and transfer function model identification, estimation and forecasting.
STA 5906r. Directed Individual Study (1–12). (S/U grade only.) May be repeated.
STA 5910r. Supervised Research (1–5). (S/U grade only.) May be repeated to a maximum of five (5) semester hours. A maximum of three (3) hours may apply to the master's degree.
STA 5920r. Statistics Colloquium (1). (S/U grade only.) May be repeated to a maximum of twelve (12) semester hours.
STA 5934r. Selected Topics in Statistics, Probability, or Operations Research (2–3). May be repeated to a maximum of twelve (12) semester hours.
STA 5936. Graduate Orientation Seminar (1). (S/U grade only.)
STA 5938. Topics in Medical Consulting (3). Prerequisite: STA 2171. This is a "hands-on" course in consulting. Two to four reasonably complex problems are identified each time the course is offered, the investigators present the problem to the class. Statistical topics covered in class are those identified by the class as required to solve the problems presented.
STA 5939. Introduction to Statistical Consulting (3). (S/U grade only.) Prerequisites: STA 5167 or 5327. Formulation of statistical problems from client information; the analysis of complex data sets by computer; practical consulting experience.
STA 5940r. Supervised Consulting (1–3). (S/U grade only.) May be repeated to a maximum of twelve (12) semester hours.
STA 5941r. Supervised Teaching (1–5). (S/U grade only.) May be repeated to a maximum of five (5) semester hours. A maximum of three (3) hours may apply to the master's degree.
STA 6174r. Advanced Methods in Epidemiology (3). Prerequisites: STA 5167, 5325. This course presents advanced methods for describing, analyzing, and modeling data from observational studies. The initial offering includes introductions to meta-analytic methods, bootstrap methods, and randomization tests. Topics vary with each offering. May be repeated up to a maximum of six (6) semester hours.
STA 6246r. Advanced Topics in Applied Statistics (2–3). Prerequisite: STA 5167. May be repeated to a maximum of twelve (12) semester hours.
STA 6346. Advanced Statistical Inference (3). Prerequisite: STA 5327.
STA 6466. Advanced Probability (3). Prerequisite: STA 5447.
STA 6468r. Advanced Topics in Probability and Statistics (2–3). May be repeated to a maximum of twelve (12) semester hours.
STA 6555. Nonparametric Curve Estimation (3). Prerequisite: STA 5327 or consent of instructor. Estimation of regression and density functions and their derivatives where no parametric model is assumed. Kernel, local polynomial, spline and wavelet methods. Emphasis on analysis and applications of the smoothing techniques and data-based smoothing parameter selectors.
STA 6709. Spatial Statistics (3). Prerequisites: STA 5208, 5327; familiarity with S-Plus or SAS software. Methods for the analysis of spatial data, includinggeostatistical data, lattice data and point patterns. Theory and applications of basic principles and techniques.
STA 6906r. Directed Individual Study (1–12). (S/U grade only.) May be repeated.
STA 6980r. Dissertation (1–12). (S/U grade only.)
STA 8964. Preliminary Doctoral Examination (0). (P/F grade only.)
STA 8966. Master's Comprehensive Examination (0). (P/F grade only.)
STA 8976. Master's Thesis Defense (0). (P/F grade only.)
STA 8985. Defense of Dissertation (0). (P/F grade only.)