Return to: Academic Programs
The Department of Experimental Statistics is the principal source of statistical education, research, and service at LSU. This department is unique in its strong orientation toward the application of statistics. Faculty provide expert statistical support for the university community.
Faculty also routinely serve on graduate committees in other departments and collaborate on interdisciplinary research projects in addition to directing graduate students in statistics and conducting independent research programs. The department has approximately 30 master’s students who interact closely with the faculty.
Applications and supporting materials for all graduate study must be submitted through the online application site for the LSU Graduate School: www.lsu.edu/gradapply. Official transcripts, official test scores, and other materials that come from third-party sources must be mailed to: Graduate Student Services, 114 West David Boyd Hall, Baton Rouge, LA 70803. These paper documents are stored electronically and departments have access to all materials submitted by and/or on behalf of a student applying to graduate study.
Students must satisfy all admission requirements of The Graduate School. Application materials, obtained from the department, must be completed and returned to The Graduate School. Transcripts and three letters of recommendation must also be sent to The Graduate School. Letters should be written by individuals who have knowledge of the student’s academic and professional qualifications.
Admission is based on aptitude, interest, and background as documented in application materials. Evidence of a strong aptitude comes from GRE scores and grades in previous college courses. Breadth of background, particularly in the applied sciences, is advantageous. Previous training in probability and statistics is desirable but not required.
To complete the program successfully, students need a working knowledge of multidimensional calculus and linear (matrix) algebra. Qualified students who have not had adequate training in mathematics can be admitted and allowed to schedule appropriate courses to satisfy this requirement. These background courses will not count for degree credit.
Graduate assistantships are awarded competitively with the approval of the department head. Nine-month assistantships pay $10,800 and require 20 hours of work per week. Academic qualifications and ability to carry out assistantship duties are the major considerations in awarding assistantships.
Some assistantships, particularly those funded by contracts, may require special skills or qualifications. The department will normally provide assistantship support for a maximum of two calendar years. International students must pass a test in spoken English prior to receiving a teaching assistantship.
Graduate Course Offering
The department offers a two-semester sequence in statistical methods that is taken by graduate students from departments throughout the university. Specialized courses in mathematical statistics, nonparametric statistics, regression, experimental design, applied least squares, multivariate statistics, categorical data analysis, sampling, reliability and survival analysis, spatial statistics, population statistics, statistical data mining, statistical computing, Bayesian analysis, and statistical genetics are offered.
Additionally, special topics courses are offered. These courses serve the educational needs of departmental graduate students in addition to fulfilling part of the department’s service mission by providing statistical training to the campus as a whole.
This department, located in the Martin D. Woodin Hall, is centrally located and convenient to all campus facilities. The department’s computer facilities include state-of-the-art laboratories equipped with approximately 100 stations, with access to the department’s servers, the campus high performance computing systems, and the Internet. These labs are used by students taking both undergraduate and graduate statistical methods courses, as well as by students taking advanced statistics courses. Moreover the labs are used for workshops, computationally intensive statistical research, and statistical data analysis for a wide variety of university research projects for which the department, faculty, staff, and students provide statistical support. In addition, the department has a computer lab reserved for graduate student statistical computing. The lab’s servers provide file and printer services as well as streaming video services used in statistical instruction, including distance education.
(check current listings by department by clicking this link)
David C. Blouin (M) • Experimental design, mixed models
Luis A. Escobar (M) • Statistical theory, nonlinear methods, survival analysis, engineering reliability, industrial statistics
James P. Geaghan (M) • Biological modeling, quantitative ecology, fisheries statistics
Lynn R. LaMotte (3P) • Regression analysis, mixed linear models, linear estimation
Bin Li (M) • Data mining, statistical learning
Brian D. Marx (M) • Smoothing, signal regression and chemometrics, generalized linear and additive models, illconditioned data, penalized likelihood
Kevin S. McCarter (7M) • Survival analysis, computationally intensive statistical methods, biostatistics, design and analysis of clinical trials, mathematical statistics, statistical education
Charles J. Monlezun (3A) • Linear models, statistical methodology, mathematical statistics
Faculty members conduct research in traditional and modern areas of statistics, as well as in applications in diverse areas of agriculture and life, social, environmental, physical, and engineering sciences. Faculty also provide statistical expertise as members of interdisciplinary teams conducting research in such areas as agriculture, forestry, wildlife, fisheries, social sciences, the physical and life sciences, and clinical trials. Faculty publish in applied and theoretical statistics journals and in journals in other fields of application.
ProgramsMaster of Applied Statistics
Return to: Academic Programs