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Current Faculty Profile

Clayton, Murray - Current Faculty Profile

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580 Russell Laboratories
1630 Linden Dr
Madison, WI 53706


B. Math. (Hons.): University of Waterloo in Pure Mathematics and Statistics

Ph.D.: University of Minnesota in Statistics

Expertise for Summary Page: 
The development of and use of statistical tools to address complex problems in the agricultural, environmental and biological sciences, focusing especially on spatial statistics.
Research Interests: 

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 non-Bayesian 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 non-Bayesian 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.

Courses Taught: 

Statistics 571-572
Statistics 998
Statistics 692/Plant Pathology 875 Seminar: Bayesian Statistics for Biology

(This is a sample of recent publications.)
Albright TP, Pidgeon AM, Rittenhouse CD, Clayton MK, Flather CH, Culbert PD, Radeloff, VC. 2011. Heat waves measured with MODIS land surface temperature data predict changes in avian community structure. Remote Sensing of Environment. 115:245-254.
Burton JI, Mladenoff DJ, Clayton MK, Forrester JA. 2011. The roles of environmental filtering and colonization in the fine-scale spatial patterning of ground-layer plant communities in north temperate deciduous forests. Journal of Ecology. 99:764-776.
Colombini S, Broderick GA, Clayton MK. 2011. Effect of quantifying peptide release on ruminal protein degradation determined using the inhibitor in vitro system. Journal of Dairy Science. 94:1967-1977. 
Shang ZF, Clayton MK. 2011. Consistency of Bayesian linear model selection with a growing number of parameters Journal of Statistical Planning and Inference 141:3463- 3474.
Solomon CT, Carpenter SR, Clayton MK, Cole JJ, Coloso JJ, Pace ML, Vander Zanden MJ, Weidel BC. 2011. Terrestrial, benthic, and pelagic resource use in lakes: results from a three-isotope Bayesian mixing model. Ecology. 92:1115-1125.
Yue CS, Clayton MK. 2011. The effect of migration on a similarity index. Communications in Statistics – Simulation and Computation. 40:412-423.
Zhang J, Clayton MK, Townsend PA. 2011. Functional concurrent linear regression model for spatial images. Journal of Agricultural, Biological and Environmental Statistics. 16:105-130.
Rittenhouse CD, Pidgeon AM, Albright TP, Culbert PD, Clayton MK, Flather CH, Masek JG, Radeloff VC. 2012. Land-cover change and avian diversity in the conterminous United States. Conservation Biology. 26: 821-829.
Solverson P, Murali SG, Brinkman AS, Nelson DW, Clayton MK, Yen CLE, Ney DM. 2012. Glycomacropeptide, a low-phenylalanine protein isolated from cheese whey, supports growth and attenuates metabolic stress in the murine model of phenylketonuria. American Journal of Physiology – Endocrinology and Metabolism. 302:885-895.
Shang ZF, Clayton MK. 2012. An application of Bayesian variable selection to spatial concurrent linear models. Environmental and Ecological Statistics. 19:521-544.
St-Louis V, Clayton MK, Pidgeon AM, Radeloff VC. 2012. An evaluation of prior influence on the predictive ability of Bayesian model averaging. Oecologia. 168:719-726.
Yue JC, Clayton MK. 2012. Sequential sampling in the search for new shared species. Journal of Statistical Planning and Inference. 142:1031-1039. 



Member for
6 years 48 weeks