John W. Seaman, Jr., Ph.D.
Professor of Statistical Science
Ph.D., Mathematical Sciences, University of Texas at Dallas, 1983
M.S., Mathematical Sciences, University of Texas at Dallas, 1979
B.S., Mathematical Sciences, University of Texas at Dallas, 1978
Major area of research
Bayesian Inference, with an Emphasis on Biomedical Applications
Courses currently teaching
• 5360 Bayesian Methods for Data Analysis
• 5364 Survival and Reliability Theory
• 6351 Large Sample Theory
• 6352 Bayesian Theory
As a statistician, I have had the pleasure of delving into a broad spectrum of scientific problems, from evolutionary biology to engineering to drug development. Thinking about such a variety of problems is the chief attraction of statistical science for me. To share that enthusiasm with graduate and undergraduate students is a pleasure.
In the department, I spend much of my time directing dissertation research. I also enjoy working on research with my former students, many of whom have become like an extended family.
I am a member of the American Statistical Association, the American Association for the Advancement of Science, and the Union of Concerned Scientists. Personally, I enjoy reading, especially history and science, hiking, and scale model building.
Bennett, M., Crowe, B., Price, K., Stamey, J., and Seaman, J. (2013). Comparison of Bayesian and frequentist meta-analytical approaches for analyzing time to event data, Journal of Biopharmaceutical Statistics, 23, 129-145.
Seaman, J. III*, Seaman, J., and Stamey, J. (2012) Hidden dangers of specifying noninformative priors, The American Statistician, 66, 77-84.
Faries,D., Peng, X., Pawaskar, M., Price,* K., Stamey, J., Seaman, J. (2012)“Evaluating the Impact of Unmeasured Confounding with Internal Validation Data: An Example Cost Evaluation in Type 2 Diabetes” accepted to appear in Value in Health
Natanagara*, F., Stamey, J.,
Seaman, J. (2012) “Bayesian sample size determination for a clinical
trial with correlated continuous and binary outcomes,” accepted for
Journal of Biopharmaceutical Statistics.
Spann, M., Lindborg, S., Seaman, J., Baker, R., Dunayevich, E., and Breier, A., (2009) “Bayesian adaptive non-inferiority with safety assessment: retrospective case study to highlight potential benefits and limitations of the approach,” J. of Psychiatric Research, 43, 561-567.
Holt, M., Stamey, J., Seaman, J., and Young, D. (2009) Bayesian test and sample size determination methods for binary outcomes in fixed-dose combination drug studies; accepted in Journal of Biopharmaceutical Statistics, 19, 120-132.
Lavery, A., Barnes, S., Keith, M., Seaman J., and Armstrong, D. (2008) Prediction of healing for complex diabetic foot wounds based on early wound area progression: Diabetes Care, 31, 26-29.
Stamey, J., Seaman, J., and Young, D. (2008); A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression; Statistics in Medicine, 27, 2440-2452.
McGlothlin, A., Stamey, J., and Seaman, J. (2008). Binary regression with misclassified response and covariate subject to measurement error: a Bayesian approach; Biometrical Journal, 50, 123-134.
Stamey, J., Seaman, J., and Young, D. (2007) Bayesian estimation of intervention effect with pre and post misclassified binomial data, Journal of Biopharmaceutical Statistics, 11, 93-108.
Stamey, J., Seaman, J., and Young, D. (2006). Bayesian inference for a correlated 2x2 table with a structural zero, Biometrical Journal, 48, 233-244.
Barnes, S., Lindborg, S., and Seaman, J. (2006) Multiple Imputation Techniques in Small Sample Clinical Trials, Statistics in Medicine, 25, #2, 233-245.
Stamey, J., Seaman, J., and Young, D. (2005) Bayesian analysis of complementary Poisson rate parameters with data subject to misclassification. Journal of Statistical Planning and Inference, 134, 36-48.
Stamey, J., Seaman, J., and Young, D. (2005) Bayesian sample size determination for inference on two binomial populations with no gold standard classifier. Statistics in Medicine, 24(19): 2963-2976.
Manner, D., Seaman, J., and Young, D. (2004) Bayesian Methods for Regression Using Surrogate Variables. Biometrical Journal, 46, 750-759.
Holt, M., Stamey, J., Seaman, J., and Young, D. (2004) A note on tests for interaction in quantal response data,” J. of Statistical Computation and Simulation, 74, 683-690.
Umble, L., Seaman, J., & Martz, H. “A distirbution-free Bayesian approach for determining the joint probability of failure of materials subject to multiple proof loads,” Technometrics, 41, 183-191, 1999.
Laviolette, M., Seaman, J., Woodall, W., and Barrett, J. “Issues Regarding the Use of Fuzzy Methods,” with discussants and reply in Technometrics, 37, 249-292, 1995 (Awarded the Youden Prize for Best Expository Paper, 1996).