Ronald H. Bremer

Information Systems and Quantitative Sciences College of Business Administration Texas Tech University Lubbock, Texas 79409-2101 (806) 742-2170 Office: BA 609 e-mail from campus: odron@coba2.ttu.edu

EDUCATION:

Dr. Ronald H. Bremer, Ph.D. in statistics, Texas A&M University, 1987.

CURRENT POSITION:

Associate Professor, Information Systems and Quantitative Sciences, Texas Tech University, 1988-present.

PAST POSITIONS AND AFFILIATIONS:

Visiting Assistant Professor, Business Analysis Department, Texas A&M University, Nontenure track full-time position held September 1987 to August 1988.

Lecturer, Department of Statistics, Texas A&M University, Nontenure track half-time position held January 1986 to August 1987.

Research Associate, Texas Transportation Institute, The Texas A&M University System, Full-time position held September 1985 to January 1986.

Research Assistant, Texas Transportation Institute, The Texas A&M University System, Half-time position held September 1983 to August 1985.

Teaching Assistant, Department of Statistics, Texas A&M University, Half-time position held September 1981 to August 1983.

Teaching Assistant, Department of Mathematics, Mankato State University, Half-time position held September 1979 to August 1981.

MAJOR DISCIPLINES:

Statistics with specialties in Linear Models and Applied Time Series Analysis.

PROFESSIONAL ORGANIZATIONS:

Member of the American Statistical Association (ASA) since 1985.

CHIEF PUBLICATIONS:

Articles in Journals:

Pendelton, O.J., Hatfield N.J., Bremer R.H. (1986), "Alcohol Involvement in Texas Driver Fatalities: Accident Reports Versus Blood Alcohol Concentration," Transportation Research Record: Traffic Accident Analysis Involvement and Evaluation of Highway Improvement, 1068, 65-70.

Pendleton, O.J., Von Tress M., Bremer R.H. (1986), "Interpretation of the Four Types of Analysis of Variance Tables in SAS," Communication in Statistics, A15, No. 9, 2785-2808.

Hocking, R.R., Bremer R.H., Green J.W. (1989), "Variance Component Estimation with Model-Based Diagnostics," Technometrics, 31, 227-239.

Bremer, R.H. (1989), "Numerical Study of Small Sample Variances of Estimators of Variance Components in the Two-Way Factorial Model," Communication in Statistics, Simulation and Computation, 18, 3, 985-1010.

Bremer, R.H. (1990), "Numerical Study of the Small Sample Variances of the MINQUE Estimators of Variance Components in the Two-Way Factorial Design," Communication in Statistics, Theory and Methods, 19, 4, 1261-1280.

Bremer R.H. (1991), "Comment to Interpreting Blocks and Random Factors," Journal of the American Statistical Society, 86, 415.

Westfall Ph.H., and Bremer R.H. (1994), "Efficiency Properties of Cell Means Variance Component Estimates," Journal of Statistical Planning and Inference, 38, 159-178.

Bremer, R.H. (1980), "Review of Estimation of Variance Components and Applications", Technometrics, 31, 496.

PROCEEDINGS PUBLISHED:

Hocking, R.R., Bremer, R.H., and Green, J. (1987) "Estimation of Variance Components in Mixed Factorial Models Including Model-based Diagnostics", Proceedings of the SAS User's Group International Twelfth Annual Conference, Feb. 8-11, 1987, 1162-1167.

Bremer, R.H. & Westfall, P.H. (1990) "Explicit Cell Means Representation of MINQUE() in Factorial Models," Proceedings of the 1990 Joint Statistical Meetings, Aug. 6-9, 1990.

Bremer R.H. (1992), "Choosing and Modeling Your Mixed Linear Model," Proceedings of Thirtieth Anniversary Technical Conference, Department of Statistics, Texas A&M University, 195-225.

Bremer, R.H. and Langevin, G.J. (1993), "The Genetic Algorithm for Identifying the Structure of a Mixed Model," Presented at the 1993 Joint Statistical Meetings, Aug. 8-11, 1993. It will appear in the proceedings for the Statistical Computation section of this meeting.

RESEARCH:

I am currently writing a book tentatively to be titled "The Design of Experiments with an Emphasis on the Mixed Model". My continuing area of research is in mixed linear model including variance component estimation, model choose for mixed models and the mixed model as a mean and covariance structure model.