Research Program

I hold a joint appointment in the Departments of Plant Pathology and Statistics. My research deals both with the development of theoretical statistics and with the use of statistical tools to address complex problems in the agricultural, environmental and biological sciences. A particular focus is on the detection and description of patterns of plant and human diseases across large geographical regions.

In epidemiological studies it is often of interest to know whether the occurrence of a given disease is clustered, and if so, where the clusters occur. For example we may wish to know whether cases of childhood leukemia appear in clusters within Wisconsin, and if so, where those clusters occur. If they occur near nuclear power plants, for example, or in highly polluted urban centers, then this would lead to hypotheses of cause for the cases of leukemia. The difficulty in making these assessments are manifold. For example, cases often appear to be clustered in cities, but that would be expected simply because more people are living together in cities anyway. (The underlying population is clustered to begin with.) Thus we need to determine whether the clustering is above and beyond that of the population. Second, the prevalence rates of some diseases under study are low, and thus clusters are not easily found.  We use a variety of Bayesian and nonBayesian methods, coupled with Markov chain Monte Carlo techniques, to address these problems.

Another broad area of interest involves studying the association between variables measured across regions. When categorical data are spatially correlated, for example, the usual chi-squared tests of independence can be invalid.  One approach we take is to use a multinomial autologistic model coupled with a Markov chain Monte Carlo approach for deriving Bayesian inferences from the data.  In other work in this general area, we seek to find models that can be used to relate data obtained through remote sensing.  In a sense, this consists of “regressing” one image on another.  Our approach involves, again, both nonBayesian and Bayesian approaches, combined with a variety of additional statistical tools.

More broadly, I collaborate with numerous scientists on a diverse array of problems, including survey design for assessing human nutrition, determining indicators of dairy herd health, modeling patterns of wolf re-establishment in northern Wisconsin, and describing soil formation processes in Africa, to name a few.

Selected Recent Publications

Gangnon RE, Clayton MK. 2000. Bayesian detection and modeling of spatial disease clustering. Biometrics. 56:922-935.

Gangnon RE, Clayton MK. 2001. A weighted average likelihood ratio test for spatial clustering of disease. Statistics in Medicine 20:2977-2987.

Hsiao C-F, Clayton MK. 2001. Bayes discrete sequential boundaries for clinical trials. Communications in Statistics – Theory and Methods. 30:1381-1394.

Yue JC, Clayton MK, Lin F-C. 2001. A nonparametric estimator of species overlap. Biometrics. 57:743-749.

Burrows SN, Gower ST, Clayton MK, Mackay DS, Ahl DE, Norman JM, Diak G. 2002. Applications of geostatistics to characterize LAI for flux towers to landscapes. Ecosystems. 5:667-679.

Gangnon RE, Clayton MK. 2003. A hierarchical model for spatially clustered disease rates. Statistics in Medicine. 22:3213-3228.

McManus PS, Caldwell RW, Voland RP, Best VM, Clayton MK. 2003. Evaluation of sampling strategies for determining incidence of cranberry fruit rot and fruit rot fungi. Plant Disease. 87:585-590.

Upper CD, Hirano SS, Dodd KK, Clayton MK. 2003. Factors that affect spread of Pseudomonas syrinage in the phyllosphere. Phytopathology. 93:1082-1092.

Gangnon RE, Clayton MK. 2004. Likelihood based tests for localized spatial clustering of disease. Environmetrics. 15:797-810.

Aukema BH, Clayton MK, Raffa KF. 2005. Modeling flight activity and population dynamics of the pine engraver, Ips pini, in the Great Lakes region: effects of weather and predators over short time scales. Population Ecology. 47:61-69.

Aukema BH, Werner RA, Haberkern KE, Illman BL, Clayton MK, Raffa KF. 2005. Quantifying sources of variation in the frequency of fungi associated with spruce beetles: Implications for hypothesis testing and sampling methodology in bark beetle-symbiont relationships. Forest Ecology and Management 217:187-202.

 Bennett EM, Carpenter SR, Clayton MK, 2005. Soil phosphorus variability: Scaledependence in an urbanizing agricultural landscape. Landscape Ecology. 20:389-400.

Cardille J, Turner M, Clayton M, Gergel S, Price S. 2005. METALAND: Characterizing spatial patterns and statistical context of landscape metrics. Bioscience 55:983-988.

Clayton MK, Petkau AJ. 2005. Evaluation of asymptotic approximations for a two-stage Bernoulli bandit. Journal of Statistical Planning and Inference. 130:133-148.

Hawbaker TJ, Radeloff VC, Hammer RB, Clayton MK. 2005. Road density and landscape pattern in relation to housing density, land ownership, land cover, and soils. Landscape Ecology. 20:609-625.

Kritsch KR, Murali S, Adamo ML, Clayton MK, Ney DM. 2005. Hypoenergetic highcarbohydrate or high-fat parenteral nutrition induces a similar metabolic response with differential effects on hepatic IGF-1 mRNA in dexamethasone-treated rats. Journal of Nutrition. 135:479-485.

Lin P-S, Clayton MK, 2005. Analysis of binary spatial data by quasi-likelihood estimating equations. Annals of Statistics. 33:542-555.

Yue JC, Clayton MK. 2005. A similarity measure based on species proportions. Communications in Statistics-Theory and Methods 34:2123-2131.

Hawbaker TJ, Radeloff VC, Clayton MK, Hammer RB, Gonzalez-Abraham CE. 2006. Road development, housing growth, and landscape fragmentation in northern Wisconsin: 1937-1999 Ecological Applications 16:1222-1237.

Yan P, Clayton MK, 2006. A cluster model for space-time disease counts. Statistics in Medicine. 25:867-881.

Clayton MK. 2007. How should we achieve high-quality reporting of statistics in scientific journals? A commentary on “Guidelines for reporting statistics in journals published by the American Physiological Society” Advances in Physiology Education. 31:302-304

Gangnon RE, Clayton MK. 2007. Cluster detection using Bayes factors from overparametrized cluster models. Environmental and Ecological Statistics. 14:69-82.

Sun L, Clayton MK. 2008. Bayesian analysis of cross-classified spatial data with autocorrelation.  Biometrics. 64:74-84.

Syphard, AD, Radeloff VC, Keuler NS, Taylor RS, Hawbaker TJ, Stewart SI, Clayton MK.  2008.  Predicting spatial patterns of fire on a  southern California landscape.  International Journal of Wildland Fire.  17: 602-613.