Christopher Williams, Ph.D.
Mathematics and Statistics Departments
Department Chair Mathematics & Statistical Science, Statistics Professor, and Professor of Bioinformatics and Computational Biology
Campus Locations: Moscow
With UI Since 1992
Ph. D., Statistics, 1988, University of Georgia
M.S., Statistics, 1983, Rutgers University
B.S., Mathematics (magna cum laude), 1980, University of Alaska, Anchorage
Statistical Problems in Natural Resources
Christopher Williams is the Chair of the Mathematics and Statistics Departments a Professor in the Department of Statistics, and Affiliate Professor in the Bioinformatics and Computational Biology Program at the University of Idaho. Dr. Williams has taught a variety of statistics courses, and has helped many graduate students and faculty with their research as a consultant in the Statistical Consulting Center. He has been a member of over 80 graduate committees for students from many departments across the university.
Dr. Williams' research areas are statistical genetics, biostatistics, and statistical methods applied to issues in natural resources. One topic of particular interest is the analysis of human twin data. Another area of interest is the estimation of disease prevalence from various types of data, such as in groups of fish that are collected and have their tissue pooled to test for disease status.
- Williams, C.J., and Moffitt, C.M. 2010. Estimation of fish and wildlife disease prevalence from imperfect diagnostic tests on pooled samples with varying pool sizes, Ecological Informatics, 5: 273-280.
- Forney, L.J., Gajer, P., Williams, C.J., Schneider, G.M., Koenig, S.S.K., McCulle, S.L., Karlebach, S., Brotman, R.M., Davis, C.C., Ault, K., and Ravel, J. 2010. Comparison of self-collected and physician-collected vaginal swabs for microbiome analysis, Journal of Clincal Microbiology, 48: 1741-1748.
- Williams, C.J., and Heglund, P. 2009. A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients, Environmental and Ecological Statistics, 16:495-513.
- Lenzini, L., Antezza, K., Caroccia, B., Wolfert, R., Szczech, R., Cesari, M., Narkiewicz, K., Williams, C.J., and Rossi, G.P. 2009. A twin study of heritability of plasma lipoprotein-associated phospholipase A2 (Lp-PLA2) mass and activity, Atherosclerosis, 205: 181-185.
- Byrne, E., Stillitano, M., Williams, C.J., and Christian, J.C. 2007. The influence of twin pair permutation on likelihood-based estimates of genetic variance, Behavior Genetics 37: 617-620.
- Henderson, D., Williams, C.J., and Miller, J.S. 2007. Forecasting late blight in potato crops of southern Idaho using logistic regression analysis, Plant Disease, 91: 951-956.
- Williams, C.J. and Christian, J.C. 2006. Frequentist Model-Averaged Estimators and Tests for Univariate Twin Models. Behavior Genetics, 36: 687-696.
- Jensen, J., Humes, K., Conner, T., Williams, C.J., and DeGroot, J. 2006. Estimation of biophysical characteristics for highly variable mixed-conifer stands using small footprint LiDAR, Canadian Journal of Forest Research, 36: 1129-1138.
- Abdo, Z., Schuette, U. Bent, S., Williams, C.J., Forney, L.J., and Joyce, P. 2006. Statistical methods for characterizing diversity of microbial communities. Environmental Microbiology, 5: 929–938.
- Williams, C.J., 2003. Editor, Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 22nd International Workshop, Moscow, Idaho, 2-6 August 2002, American Institute of Physics Conference Proceedings.
- Brown, Celeste J., Sachiko Takayama, Andrew M. Campen, Pam Vise, Thomas W. Marshall, Christopher J. Oldfield, Christopher J. Williams, and A. Keith Dunker. 2002. Evolutionary rate heterogeneity in proteins with long disordered regions. Journal of Molecular Evolution 55:104-110.
- Williams, C.J. and Moffitt, C.M. 2001. A critique of methods of sampling and reporting pathogens in populations of fish. Journal of Aquatic Animal Health, 13:300-309.
- Disease prevalence estimation
- Analysis of human twin data
- Dr. Williams offers the following courses via Engineering Outreach: Statistics 431 (Statistical Analysis), Statistics 422 (Sample Survey Methods), and Statistics 550 (Regression)
- Presentations about Statistics at Moscow High School