Thomas E. Wehrly
Affiliations: | Texas A & M University, College Station, TX, United States |
Area:
StatisticsGoogle:
"Thomas Wehrly"Bio:
Parents
Sign in to add mentor| Gouri Kanta Bhattacharyya | grad student | 1976 | UW Madison | |
| Richard A. Johnson | grad student | 1976 | UW Madison | |
| (Models for Bivariate Directional Data and Angular-Linear Data with Applications to Statistical Inference.) | ||||
Children
Sign in to add trainee| Jihnhee Yu | grad student | 2003 | Texas A & M |
| Nai-Wei Chen | grad student | 2011 | Texas A & M |
| Daniel L. Glab | grad student | 2011 | Texas A & M |
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Publications
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| Chen HC, Wehrly TE. (2016) Approximate uniform shrinkage prior for a multivariate generalized linear mixed model Journal of Multivariate Analysis. 145: 148-161 |
| Chen HC, Wehrly TE. (2015) Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model. Statistics in Medicine. 34: 704-20 |
| Yu J, Wehrly TE. (2004) An approach to the residence time distribution for stochastic multi-compartment models. Mathematical Biosciences. 191: 185-205 |
| Matis JH, Wehrly TE. (1998) A general approach to non-Markovian compartmental models Journal of Pharmacokinetics and Biopharmaceutics. 26: 437-456 |
| Cardwell KF, Wehrly TE. (1997) A rank test for distinguishing environmentally and genetically induced disease resistance in plant varieties Biometrics. 53: 195-206 |
| Ellis TE, Rudd MD, Rajab MH, et al. (1996) Cluster analysis of MCMI scores of suicidal psychiatric patients: Four personality profiles Journal of Clinical Psychology. 52: 411-422 |
| Matis JH, Wehrly TE. (1994) 17 Compartmental models of ecological and environmental systems Handbook of Statistics. 12: 583-613 |
| Baek J, Wehrly TE. (1993) Kernel estimation for additive models under dependence Stochastic Processes and Their Applications. 47: 95-112 |
| Hart JD, Wehrly TE. (1993) Consistency of cross-validation when the data are curves Stochastic Processes and Their Applications. 45: 351-361 |
| Hart JD, Wehrly TE. (1992) Kernel regression when the boundary region is large, with an application to testing the adequacy of polynomial models Journal of the American Statistical Association. 87: 1018-1024 |