Jun Zhu

Jun Zhu


437A Russell Laboratories
1630 Linden Drive
Madison, WI 53706


PhD  Iowa State University, Ames, 2000 (Statistics)
MSE  Johns Hopkins University, Baltimore, 1995 (Mathematical Sciences)
BA  Knox College, Galesburg 1994 (Mathematics and Computer Science)

The main components of my research activities are statistical methodological research and scientific collaborative research.  My statistical methodological research concerns developing statistical methodology for analyzing spatially referenced data (spatial statistics) and spatial data repeatedly sampled over time (spatio-temporal statistics), that arise often in the biological, physical, and social sciences.   My collaborative research concerns applying modern statistical methods, especially spatial and spatio-temporal statistics, to studies of agricultural, biological, ecological, environmental, and social systems conducted by research scientists.  To a large extent, my overall research program involves a close connection between the two types of research activities: Problems in my collaborative research that do not have adequate statistical tools motivate my statistical methodological research; whereas the new methods I develop in statistical methodological research are applied in my collaborative projects.

Statistics 571: Statistical Methods for Bioscience I
Statistics 572: Statistical Methods for Bioscience II
Statistics 575: Statistical Methods for Spatial Data
Statistics 701: Applied Time Series Analysis, Forecasting, and Control I
Statistics 849: Theory and Application of Regression and Analysis of Variance I
Statistics 992: Statistics for Spatial Data: Theory and Methods
Statistics 998: Statistical Consulting
Entomology 901: The Tao of Statistics

Department of Statistics
Department of Entomology
Biometry Program, College of Agricultural and Life Sciences

Statistical Methodology Publications:

  • Cressie, N., Zhu, J., Baddeley, A.J., and Nair, M.G. (2000). Directed Markov point processes as limits of partially ordered Markov models. Methodology and Computing in Applied Probability, 2, 5–21.
  • Zhu, J., Lahiri, S.N., and Cressie, N. (2002). Asymptotic inference for spatial CDFs over time. Statistica Sinica, 12, 843–861.
  • Zhu, J., Eickhoff, J.C., and Kaiser, M.S. (2003). Modeling the dependence between number of trials and success probability in a beta-binomial–Poisson mixture distribution. Biometrics, 59, 957–963.
  • Eickhoff, J.C., Zhu, J., and Amemiya, Y. (2004). On the simulation size and the convergence of the Monte Carlo EM algorithm via likelihood-based distances. Statistics and Probability Letters, 67, 161–171.
  • Zhu, J., Morgan, C.L.S., Norman, J.M., Yue, W., and Lowery, B. (2004). Combined mapping of soil properties using a multi-scale tree-structured spatial model. Geoderma, 118, 321–334.
  • Zhu, J. and Morgan, G.D. (2004). Comparison of spatial variables over subregions using a block bootstrap. Journal of Agricultural, Biological, and Environmental Statistics, 9, 91–104.
  • Zhu, J. and Morgan, G.D. (2004). A nonparametric procedure for analyzing repeated-measures of spatially correlated data. Environmental and Ecological Statistics, 11, 431–443.
  • Tracey, J.A., Zhu, J., and Crooks, K. (2005). A set of nonlinear regression models for animal movement in response to a single landscape feature. Journal of Agricultural, Biological, and Environmental Statistics, 10, 1–18.
  • Zhu, J., Eickhoff, J.C., and Yan, P. (2005). Generalized linear latent variable models for repeated measures of spatially correlated multivariate data. Biometrics, 61, 674–683.
  • Zhu, J., Huang, H.-C., and Wu, J. (2005). Modeling spatial-temporal binary data using Markov random fields. Journal of Agricultural, Biological, and Environmental Statistics, 10, 212–225.
  • Zhu, J. and Yue, W. (2005). A multiresolution tree-structured spatial linear model. Journal of Computational and Graphical Statistics, 14, 168–184.
  • Ives, A.R. and Zhu, J. (2006). Statistics for correlated data: phylogenies, space, and time. Ecological Applications, 16, 20–32.
  • Lahiri, S.N. and Zhu, J. (2006). Resampling methods for spatial regression models under a class of stochastic designs. Annals of Statistics, 34, 1774–1813.
  • Yue, W. and Zhu, J. (2006). On estimation and prediction for multivariate multiresolution tree-structured models. Statistica Sinica, 16, 981–1020.
  • Zhu, J. and Lahiri, S.N. (2007). Bootstrapping the empirical distribution function of a spatial process. Statistical Inference for Stochastic Processes, 10, 107–145.
  • Rasmussen, J.G., Møller, J., Aukema, B.H., Raffa, K.F., and Zhu, J. (2007). Bayesian inference for multivariate point processes observed at sparsely distributed times. Journal of the Royal Statistical Society Series B, 69, 701–713.
  • Chi, G. and Zhu, J. (2008). Spatial regression models for demographic analysis. Population Research and Policy Review, 27, 17–42.
  • Zheng, Y. and Zhu, J. (2008). Markov chain Monte Carlo for a spatial-temporal autologistic regression model. Journal of Computational and Graphical Statistics, 17, 123–137.
  • Zheng, Y., Zhu, J., and Li, D. (2008). Analyzing spatial panel data of cigarette demand: A Bayesian hierarchical modeling approach. Journal of Data Science, 6, 467–489.
  • Zhu, J., Rasmussen, J.G., Møller, J., Aukema, B.H., and Raffa, K.F. (2008). Spatial-temporal modeling of forest gaps generated by colonization from below- and above-ground bark beetle species. Journal of the American Statistical Association, 103, 162–177.
  • Zhu, J., Zheng, Y., Carroll, A.L., and Aukema, B.H. (2008). Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood. Journal of Agricultural, Biological, and Environmental Statistics, 13, 84–98.
  • Wang, H. and Zhu, J. (2009). Variable selection in spatial regression via penalized least squares. Canadian Journal of Statistics, 37, 607–624.
  • Zheng, Y., Zhu, J., and Roy, A. (2010). Nonparametric Bayesian inference for the spectral density function of a random field. Biometrika, 97, 238–245.
  • Zhu, J., Huang, H.-C., and Reyes, P.E. (2010). On selection of spatial linear models for lattice data. Journal of the Royal Statistical Society Series B, 72, 389–402.
  • Chu, T., Zhu, J., and Wang, H. (2011). Penalized maximum likelihood estimation and variable selection in geostatistics. Annals of Statistics, 39, 2607-2625.
  • Tracey, J.A., Zhu, J., and Crooks, K.R. (2011). Modeling and inference of animal movement using artificial neural network. Environmental and Ecological Statistics, 18, 393-410.
  • Zheng, Y. and Zhu, J. (2012). On the asymptotics of maximum likelihood estimation for spatial linear models on a lattice. Sankhya Series A, 74, 29-56.
  • Lin, F. and Zhu, J. (2012). Additive hazards regression and partial likelihood estimation for ecological monitoring data across space. Statistics and Its Inference, 5, 195-206.
  • Lin, F. and Zhu, J. (2012). Continuous-time proportional hazards regression for ecological monitoring data. Journal of Agricultural, Biological, and Environmental Statistics, 17, 163-175.
  • Reyes, P.E., Zhu, J., and Aukema, B.H. (2012). Selection of spatial-temporal lattice models: Assessing the impact of climate conditions on a mountain pine beetle outbreak. Journal of Agricultural, Biological, and Environmental Statistics, 17, 508-525.
  • Jin, C., Zhu, J., Steen-Adams, M.M., Sain, S., and Gangnon, R.E. (2013). Spatial multinomial regression models for nominal categorical data: A study of land cover in northern Wisconsin. Environmetrics, 24, 98-108.
  • Tracey, J.A., Zhu, J., Boydston, E., Lyren, L., Fisher, R.N., and Crooks, K.R. (2013). Mapping behavioral landscapes for animal movement: A finite mixture modeling approach. Ecological Applications, 23, 654-669.
  • Fu, R., Thurman, A.L., Chu, T., Steen-Adams, M.M., and Zhu, J. (2013). On estimation and selection of autologistic regression models via penalized pseudolikelihood. Journal of Agricultural, Biological, and Environmental Statistics, 18, 429-449.
  • Chu, T., Wang, H., and Zhu, J. (2014). On semiparametric inference of geostatistical models via local Karhunen-Loeve expansion. Journal of the Royal Statistical Society Series B, 76, 817-832.
  • Thurman, A.L. and Zhu, J. (2014). Variable selection for spatial Poisson point processes via a regularization method. Statistical Methodology, 17, 113-125.
  • Feng, X., Zhu, J., Lin, P.-S., and Steen-Adams, M.M. (2014). Composite likelihood estimation for spatial ordinal data and spatial proportional data with zero/one inflation. Environmetrics, 25, 571-583.
  • Thurman, A.L., Fu, R., Guan, Y., and Zhu, J. (2015). Regularized estimating equations for model selection of clustered spatial point processes. Statistica Sinica, 25, 173-188.
  • Feng, X., Zhu, J., and Steen-Adams, M.M. (2015). On regression analysis of spatial proportional data with zero/one values. Spatial Statistics, 14, 452-471.
  • Al-Sulamid, D., Lu, Z., Jiang, Z., and Zhu, J. (201-). Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data. Econometrics and Statistics. Accepted for publication.
  • Ludwig, G., Chu, T., Zhu, J., Wang, H., and Koehler, K. (201-). Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment. Annals of Applied Statistics. Accepted for publication.
  • Feng, X., Zhu, J., Steen-Adams, M.M., and Lin, P.-S. (201-). Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial com- positional data with exact zero values. Environmental and Ecological Statistics. Accepted for publication.
  • Chen, C.-S., Zhu, J., and Chu, T. (201-). A generalized measure of uncertainty in geo- statistical model selection. Statistica Sinica. Accepted for publication.
  • Lee, J., Gangnon, R., and Zhu, J. (201-). Cluster detection of spatial regression coefficients. Statistics in Medicine. Accepted for publication.

Scientific Collaboration Publications:

  • Morgan, G.D., MacGuidwin, A.E., Zhu, J., and Binning, L.K. (2002). Root lesion nematode (Pratylenchus penetrans) population dynamics over a three-year crop rotation. Agronomy Journal, 94, 1146–1155.
  • Morgan, G.D., Stevenson, W.R., MacGuidwin, A.E., Kelling, K.A., Binning, L.K., and Zhu, J. (2002). Plant pathogen population dynamics in potato fields. Journal of Nematology, 34, 189–193.
  • Ingham, S.C., Vivio, L.L., Losinski, J.A., and Zhu, J. (2004). Manual shaking as an alternative to mechanical stomaching in preparing ground meats for microbiological analysis. Food Protection Trends, 24, 253–256.
  • Anderson, D.A., Turner, M.G., Forester, J.D., Zhu, J., Boyce, M.S., Beyer, H., and Stowell, L. (2005). Scale-dependent summer habitat use for reintroduced elk (Cervus canadensis) in Wisconsin, USA. Journal of Wildlife Management, 69, 298–310.
  • Ingham, S.C., Fanslau, M.A., Engel R., Breuer, J.R., Breuer, J.E., Wright, T.H., Reith-Rozelle, J.K., and Zhu, J. (2005). Evaluation of fertilization-to-planting and fertilizationto-harvest intervals for safe use of non-composted bovine manure in Wisconsin vegetable production. Journal of Food Protection, 68, 1134–1142.
  • Ingham, S.C., Engel, R.A., Fanslau, M.A., Schoeller, E.L., Searls, G.A., Buege, D.R., and Zhu, J. (2005). Fate of Staphylococcus aureus on vacuum-packaged ready-to-eat meat products stored at 21oC. Journal of Food Protection, 68, 1911–1915.
  • Magle, S.B., Zhu, J., and Crooks, K. (2005). Behavioral responses to repeated human intrusion by black-tailed prairie dogs (Cynomys Ludovicianus). Journal of Mammalogy, 86, 524–530.
  • Smithwick, E.A.H., Mack, M.C., Turner, M.G., Chapin III, F.S., Zhu, J., and Balser, T.C. (2005). Spatial heterogeneity of ecosystem processes after severe fire in a black spruce(Picea mariana) forest, Alaska. Biogeochemistry, 76, 517–537.
  • Aukema, B.H., Carroll, A.L., Zhu, J., Raffa, K.F., Sickley, T.A., and Taylor, S.W. (2006). Landscape level analysis of mountain pine beetle in British Columbia, Canada: Spatiotemporal development and spatial synchrony within the present outbreak. Ecography, 29, 427–441.
  • Algino, R.J., Ingham, S.C., and Zhu, J. (2007). Survey of antimicrobial effects of beef carcass intervention treatments in very small state-inspected slaughter plants. Journal of Food Science, 72, 173–179.
  • Ruby, J.R., Zhu, J., and Ingham, S.C. (2007). Using indicator bacteria and Salmonella spp. test results from three large-scale beef abattoirs over an 18-month period to evaluate intervention system efficacy and plan carcass testing for Salmonella spp. Journal of Food Protection, 70, 2732–2740.
  • Vander-Zanden, M.J., Joppa, L.N., Allen, B.C., Chandra, S., Gilroy, D., Hogan, Z., Maxted, J.T., and Zhu, J. (2007). Modeling spawning dates of Hucho taimen in Mongolia to establish fishery management zones. Ecological Applications, 17, 2281–2289.
  • Aukema, B.H., Carroll, A.L., Zheng, Y., Zhu, J., Raffa, K.F., Moore, R.D., and Stahl, K. (2008). Movement of outbreak populations of mountain pine beetle: Influence of spatiotemporal patterns and climate. Ecography, 31, 348–358.
  • Mu˜noz, G.R., Kelling, K.A., Rylant, K.E., and Zhu, J. (2008) Field evaluation of nitrogen availability from fresh and composted manure. Journal of Environmental Quality, 37, 944–955.
  • Qin, X., Han, J., and Zhu, J. (2009). Spatial analysis of road weather safety data using a Bayesian hierarchical modeling approach. Advances in Transportation Studies, 18, 69–78.
  • Aukema, B.H., Zhu, J., Møller, J., Rasmussen, J.G., and Raffa, K.F. (2010). Interactions between below- and above-ground herbivores drive a forest decline and gap-forming syndrome. Forest Ecology and Management, 259, 374–382.
  • Magle, S.B., Reyes, P., Zhu, J., and Crooks, K. (2010). Investigating local extinction, colonization, and habitat destruction for a keystone species in urban habitat. Biological Conservation, 143, 2146-2155.
  • Steen-Adams, M.M., Mladenoff, D.J., Langston,N.E., Liu, F., and Zhu, J. (2011). Influence of biophysical factors and differences in Ojibwe reservation versus Euro-American social histories on forest landscape change in northern Wisconsin, USA. Landscape Ecology, 26, 1165-1178.
  • Turner, M.G., Romme, W.H., Smithwick, E.A.H., Tinker, D.B., and Zhu, J. (2011). Variation in aboveground cover influences soil nitrogen availability at fine spatial scales following severe fire in subalpine conifer forests. Ecosystems, 14, 1081-1095.
  • Sambaraju, K.R., Carroll, A.L., Zhu, J., Moore, D., Stahl, K., and Aukema, B.H. (2012). Climate change could alter the distribution of mountain pine beetle outbreaks in western Canada. Ecography, 35, 211-223.
  • Curtis, K.J., Reyes, P.E., O’Connell, H., and Zhu, J. (2013). Assessing the spatial concentration and temporal persistence of poverty: Industrial structure, racial/ethnic concentration, and the complex links to poverty. Spatial Demography, 1, 178-194.
  • Bradbury, K.R., Borchardt, M.A., Gotkowitz, M., Spencer, S.K., Zhu, J., and Hunt, R.J. (2013). Source and transport of human enteric viruses in deep municipal water supply wells. Environmental Science and Technology, 47, 4096-4103.
  • Bosak, E.J., Seidl-Adams, I.H., Zhu, J., and Tumlinson, J.H. (2013). Maize developmental stage affects indirect and direct defense expression. Environmental Entomology, 42, 1309-1321.
  • Tracey, J.A., Sheppard, J., Zhu, J., Wei, F., Swaisgood, R.R., and Fisher, R.N. (2014). Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation. PLoS ONE, 9, e101205.
  • Bradshaw, T., Fu, R., Bowen, S., Zhu, J., Forrest, L., and Jeraj, R. (2015). Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy. Physics in Medicine and Biology, 60, 5211.
  • Lake, K.A., Zhu, J., Wang, H., Volckens, J., Koehler, K.A. (2015). Effects of data sparsity and spatiotemporal variability on hazard maps of workplace noise. Journal of Occupational and Environmental Hygiene, 12, 256–265.
  • Stenglein, J.L., Zhu, J., Clayton, M.K., and Van Deelen, T.R. (2015). Are the numbers adding up? Exploiting discrepancies among complementary population models. Ecology and Evolution, 5, 368–376.
  • Corsi, S.R., Borchardt, M.A., Carvin, R.B., Burch, T.R., Spencer, S.K., Lutz, M.A., McDermott, C.M., Busse, K.M., Kleinheinz, G.T., Feng, X., and Zhu, J. (2016). Human and bovine viruses and bacteria at three Great Lakes beaches: Environmental variable associations and health risk. Environmental Science and Technology, 50, 987-995.
  • Gotkowitz, M.B., Bradbury, K.R., Borchardt, M.A., Zhu, J., and Spencer, S.K. (2016). Effects of climate and sewer condition on virus transport to groundwater. Environmental Science & Technology, 50, 8497-8504.
  • Jonhston, M.R., Balster, N.J., and Zhu, J. (2016). Impact of residential prairie gardens on the physical properties of urban soil in Madison, WI. Journal of Environmental Quality, 45, 45-52.
  • Liang, J, T. W. Crowther, N. Picard, S. Wiser, M. Zhou, G. Alberti, E.-D. Schulze, A. D. McGuire, F. et al. and P. B. Reich. (2016). Positive biodiversity-productivity relationship predominant in global forests. Science, 354.
  • Paciorek, C.J., Goring, S.J., Thurman, A.L., Cogbill, C.V., Williams, J.W., Mladenoff, D.J., Peters, J.A., Zhu, J., and McLachlan, J.S. (2016). Statistically-estimated tree com- position for the Northeastern United States at Euro-American settlement. PloS one 11.2: e0150087.
  • Smidt, E.R., Arriaga, F.J., Zhu, J. and Conley, S.P. (2016). Identifying field attributes that predict soybean yield using random forest analysis. Agronomy Journal, 108, 637-646.
  • Stack Whitney, K., Meehan, T.D., Kucharik, C.J., Zhu, J., Townsend, P.A., Hamilton, K., and Gratton, C. (2016). Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance. Ecological Applications, 26, 2598-2608.

Book Review:

  • Zhu, J. (2006). Review of “Statistical Methods for Spatial Data Analysis” by O. Schabenberger and C. A. Gotway. Journal of the American Statistical Association, 101, 389–390.
  • Zhu, J. (2013). Review of “Spatio-Temporal Heterogeneity: Concepts and Analyses” by P.R.L. Dutilleul. Biometrics, 69, 557-558.

Book Chapters:

  • Zhu, J., Lahiri, S.N., and Cressie, N. (2001). Asymptotic distribution of the empirical CDF predictor under nonstationarity. Spatial Statistics: Methodological Aspects and Some Applications, Ed. M. Moore. Springer, New York. pp.1-26.
  • Zhu, J., Wolkowski, R.P., Yue, W., and Xu, R. (2005). On spatial lattice modeling of soil properties. Geographic Information Technologies for Environmental Soil-Landscape Modeling, Ed. S. Grunwald. Marcel Dekker. pp.393-416.
  • Zhu, J. and Thurman, A.L. (2013). Model-based geostatistics. Encyclopedia of Environmetrics, 2nd ed. Eds. A.-H. El-Shaarawi and W. Piegorsch. Wiley, Chichester. pp.1202-1204.
  • Zhu, J. and Zheng, Y. (2016). Autologistic regression models for spatial-temporal binary data. Handbook of Discrete-Valued Time Series, Eds. R. Davis, S. Holan, R. Lund, and N. Ravishanker. Chapman & Hall, Boca Raton. pp.367-386.
  • Lin, P.-S., Zhu, J., Kuo, S.-F., and Curtis, K. (2016). A statistical method for change-set analysis. Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics, Eds. J. Lin, B. Wang, X. Hu, K. Chen, and R. Liu. Springer, Switzerland. pp.281-292.

Resampling Methods:

Earlier in my career, I worked on asymptotic inference for spatial cumulative distribution functions using spatial subsampling with application to environmental monitoring and assessment (Zhu et al. 2001, 2002).  I then investigated spatial resampling methods.  On the one hand, I worked on the theory of the spatial block bootstrap, which is a resampling method alternative to spatial subsampling (Lahiri and Zhu 2006; Zhu and Lahiri 2007).  On the other hand, motivated by my collaborative research work with Dr. G. Morgan in Horticulture on the population dynamics of root lesion nematodes in potato fields (Morgan et al. 2002a, 2002b), I developed new statistical methodology based on the theory of the spatial block bootstrap. In particular, I developed a spatial block bootstrap for comparing spatial variables in different subregions (Zhu and Morgan 2004a) and for comparing spatial variables over time (Zhu and Morgan 2004b).

Spatio-Temporal Statistics:

I have collaborated with Drs. K. Raffa,  B. Aukema, and their research groups in Forest Entomology on population dynamics and interactions among trees, insects, and fungi in forest stands of Wisconsin.  The nature of the data is complex, involving spatial, temporal, and multiple response variables that are not necessarily normally distributed, which motivated me to pursue research in spatial-temporal statistics involving statistical methodology for spatial and temporal data  (Rasmussen et al. 2007; Zhu et al. 2008; Aukema et al. 2010).  During a visit to Academia Sinica in Taiwan, I worked with Drs. H.-C. Huang and J.-P. Wu on a spatio-temporal random field model for binary data with application to outbreaks of southern pine beetles (Zhu et al. 2005).  The modeling framework is an extension of the autologistic model for purely spatial data on a lattice.  My collaborators and I adapted the model for quantifying spatio-temporal patterns of mountain pine beetle outbreak in Western Canada (Aukema et al. 2006; Aukema et al. 2008; Zhu et al. 2008).  Furthermore, recognizing that practical statistical tools were limited for complex spatial and spatio-temporal data that are not necessarily normal, I developed new statistical methodologies such as a latent variable model that has generalized linear models for the response variables and a spatio-temporal multivariate process for the latent variables (Zhu et al. 2005), continuous-time models (Rasmussen et al. 2007), and  general modeling frameworks for spatio-temporal binary data while addressing challenging computational issues (Zheng and Zhu 2008).

Spatial Model/Variable Selection and Other Current Interests:

My recent statistical methodology research focused on model/variable selection in spatial linear regression for geostatistical data (Wang and Zhu 2009; Chu et al. 2011; Chen et al. forthcoming), lattice data (Zhu et al. 2010; Reyes et al. 2012; Fu et al. 2013) and spatial point patterns (Thurman and Zhu 2014; Thurman et al. 2015).

Other current interests include nonparametric Bayesian inference for spatial processes (Zheng et al. 2010), asymptotic frameworks for maximum likelihood estimation in spatial models (Chu et al. 2011; Zheng and Zhu 2011), time-to-event models for ecological monitoring (Lin and Zhu 2011; 2012), modeling spatial categorical and proportional data (Jin et al. 2013; Feng et al. 2014; 2015), semiparametric inference of geostatistical models (Chu et al. 2014; Ludwig et al. forthcoming), spatial clustering (Lee et al. forthcoming), and semiparametric spatial regression (Al-Sulamid et al. forthcoming).

Animal Movement Models:

I have been collaborating with Drs. K. Crooks, S. Magle, and  J. Tracey in Wildlife Ecology on the behavior and conservation of wildlife.  One research project concerns the conservation of mountain lions and other large mammals in southern California where the animals’ territories are threatened by rapid urban sprawl.   We quantified animal movement paths in terms of the angle and length of each move across landscape features over time by developing statistical nonlinear regression models and inference.  In the beginning, we considered the situation of one animal responding to one type of landscape feature and extended the traditional circular statistics to accommodate explanatory variables such as distance to a landscape feature (Tracey et al. 2005). We continued to extend the work to the situation of one animal responding to multiple types of landscape features, as well as from individual animal based inference to population-based inference using artificial neural network (Tracey et al. 2011 ) and finite mixture models (Tracey et al. 2013).  In another research project, we studied prairie dogs in the front range of Colorado, comparing prairie dogs in urban areas and those in rural areas using linear regression and logistic regression analysis (Magle et al. 2005) and  assessing the impact of human activities on the colonization and extirpation of prairie dogs over time (Magle et al. 2010).  Most recently we developed new tools for inference and visualization of space use in 3D based on movement data (Tracey et al. 2014; R mkde package).

Applications in Other Studies:

I have been collaborating with research scientists in a variety of disciplines, especially in Wildlife Ecology, Conservation Biology, and Landscape Ecology (Zhu et al 2003; Anderson et al. 2005; Smithwick et al. 2005; Ives and Zhu 2006; Vander-Zanden et al. 2007; Turner et al. 2011; Stenglein et al. 2015; Liang et al. 2016; Stack Whitney et al. 2016). Other areas of applications include Agronomy (Morgan et al. 2002; Smidt et al. 2016), Civil and Environmental Engineering (Qin et al. 2009), Environmental Entomology (Bosak et al. 2013), Food Science (Ingham et al. 2004, 2005; Algino et al. 2007; Ruby et al. 2007), Soil Science (Zhu et al. 2004; Munoz et al. 2008; Jonhston et al. 2016), Spatial Demography (Chi and Zhu 2008; Curtis et al. 2013), Environmental History (Steen-Adams et al. 2011; Jin et al. 2013), Environmental Health (Bradbury et al. 2013; Corsi et al. 2016; Gotkowitz et al. 2016), Medical Physics (Bradshaw et al. 2015), and Occupational Health (Lake et al. 2015).


The three courses I teach regularly are Statistics 571-Statistical Methods for Bioscience I, Statistics 572-Statistical Methods for Bioscience II, and Statistics 575-Statistical Methods for Spatial Data.  My objective in Stat571 is to provide research-oriented students in the agricultural, biological, and environmental sciences with a thorough grounding in the basic statistical methods.  My teaching philosophy is to stress an understanding of the procedures along with applications.  While keeping the mathematical complexities to a minimum, I give considerable attention to the analysis of real data.  I view the development of the ability to interpret results and to evaluate critically the methods used as of paramount importance.  My objective in Stat572 is to provide students in bioscience with a thorough understanding of modern statistical procedures.  Like in Stat571, I emphasize underlying concepts rather than an extensive coverage of a wide range of topics.  To a large extent the assignments involve the analysis of data sets that approach the real-world complexity of data encountered in research and substantial use is made of the computer in conducting such analyses.  The course Stat575 is directed towards graduate students who are interested in analyzing spatial data, including students from the environmental and ecological sciences, urban and regional planning, soil sciences, plant and animal sciences, and statistics.  Similar to Statistics 571 and 572, I focus mostly on statistical methods and stress an understanding of the underlying concepts, as opposed to simply providing a cookbook of statistical formulas.

In addition, I have taught the following graduate-level statistics courses: STAT701 (3 Cr) Applied Time Series Analysis, STAT849 (4 Cr) Theory and Application of Regression and Analysis of Variance I, STAT992 (3 Cr) Statistics for Spatial Data: Theory and Methods, and STAT998 (3 Cr) Statistical Consulting.