- Ph.D. Purdue University
- M.S. Institute of Applied Mathematics of Academic Sinica
- B.S. Fudan University
Keying Ye is a professor of statistics in the Department of Management Science and Statistics at The University of Texas at San Antonio. He previously worked at Virginia Tech and Shanghai Jiaotong University. He received his Ph.D. degree in statistics from Purdue University, an M.S. degree in mathematics from the Institute of Applied Mathematics of Academia Sinica in China and a B.S. degree in mathematics from Fudan University in China. He has published articles in statistical methodology and application in various journals and books. He teaches a variety of courses and short courses in statistics. Currently he is an associate editor for Bayesian Analysis and Journal of Statistical Computation and Simulation.
- Bayesian inference and methods
- Mathematical statistics
- Statistical applications in biostatistics, cyber security, environmental, ecological and geological sciences, experimental design and industrial statistics
- “Comparing VaR Approximation Methods That Use the First Four Moments As Inputs,” (with Donald Lien and Christopher Stroud), Communications in Statistics – Simulation and Computation, Vol. 45, No. 2, 2016, pp. 491-503.
- “Componentwise Variable Selection in Finite Mixture Regression,” (with B. Chen), Statistics and Its Interface, Vol. 8, 2015, pp. 239-254.
- “A Bayesian Hierarchical Approach to Dual Response Surface Modeling,” Journal of Applied Statistics, Vol. 38, 2011, pp. 1963-1975.
- “Determining the Number of Clusters Using the Weighted Gap Statistic,” Biometrics, Vol. 63, 2007, pp. 1031-1037.
- “A Bayesian Approach to Evaluating Site Impairment,” Environmental and Ecological Statistics, Vol. 9, 2003, pp. 379-392.
- “Bayesian Two-stage Optimal Design for Mixture Models,” Journal of Statistical Compution and Simulation, Vol. 66, 2000, pp. 209-231.
- “Estimating a Ratio of the Variances in a Balanced One-way Random Effects Model,” Statistics and Decisions, 1998, pp. 163-180.
- “Comparative Calibration Without a Gold Standard,” Statistics in Medicine, Vol. 16, 1997, pp. 1889-1905.
- “Reference Prior Bayesian Analysis for Normal Mean Products,” Journal of the American Statistical Association, Vol. 90,1995, pp. 589-597.
- “Reference Priors When the Stopping Rule Depends on the Parameter of Interest,” Journal of the American Statistical Association, Vol. 88, 1993, pp. 360-363.
- “Noninformative Priors for Inferences in Exponential Regression Models,” Biometrika, Vol. 78, 1991, pp. 645-656.