Kent M. Eskridge

Portrait of Kent Eskridge

Kent M. Eskridge

Professor
Areas of Expertise: Design of Experiments, Biological modeling, Statistical consulting

Research Interests:
Design of Experiments:  I am interested in the design and analysis of experiments. Recently I have been most interested in the properties and applications of (1) supersaturated split-plot designs and (2) confounded factorial conjoint choice experiments in consumer preference. I am also quite interested in working with researchers in other disciplines in developing and applying new ideas of design and analysis to their research. My most recent teaching duties in this area are (1) Advanced Design of Experiments (Stat 902) and (2) Theory of Design of Experiments (Stat 904).

Biological modeling:  I am interested in development of statistical and mathematical approaches in modeling complex biological systems. Recently I have been most interested in the properties and applications of (1) spline-enhanced differential equations as applied to pharmacokinetics and pharmacokinetics and (2)  structural equation modeling as applied to composite interval gene mapping and in the analysis of gene-environment interaction. My most recent teaching duties in this area are (1) Applied Multivariate (Stat 873) Statistics and (2) Theory of Multivariate Statistics (Stat 973).

Statistical consulting:  I aid researchers from a wide range of fields with the design and analysis of their experiments. The majority of work is in the biological sciences.


Selected Publications:
W. Koh,  K. M. Eskridge  and M. A. Hanna. 2013. Supersaturated Split-plot Designs. Journal of Quality Techonlogy 45(1):61-73.

W. Koh,  K. M. Eskridge  and D. Wang.  2013. The effects of nonnormality on the analysis of supersaturated designs: a comparison of stepwise, SCAD and permutation test methods.  Journal of Statistical Computation and Simulation. 83(1):158-166 2013.

Michel Kanmogne, Kent M. Eskridge. 2013. Identifying some major determinants of entrepreneurial partnership, using a confounded factorial conjoint choice experiment. Quality and Quantity. 47(2):943-960.

http://www.springerlink.com/content/187g962n4688714j/fulltext.pdf



Wang, Yi,  Eskridge, Kent M. and Nadarajah, Saralees.  2012.  Optimal Design of Mixed-Effects PK/PD Models Based on Differential Equations.  Journal of Biopharmaceutical Statistics.  22(1):180-205.
  http://www.tandfonline.com/doi/pdf/10.1080/10543406.2010.513465 

X. Mi,  K. M. Eskridge, V. George and D. Wang. 2011.  Structural Equation Modeling of Gene-Environment Interactions in CHD .  Annals of Human Genetics. 75:255-265.
http://www.ncbi.nlm.nih.gov/pubmed/21241273 

C. K. Yong, Kent M. Eskridge, Chris R. Calkins and Wendy J. Umberger. 2010. Assessing consumer preferences for rib-eye steak characteristics using confounded factorial conjoint choice experiments. J. of Muscle Foods. 21(2):224-242.
http://onlinelibrary.wiley.com/doi/10.1111/j.1745-4573.2009.00178.x/pdf 

X. Mi,  K. M. Eskridge,  D. Wang,  P. S. Baenziger, B. T. Campbell,   K. S. Gill,   I. Dweikat. 2010. Bayesian mixture structural equation modelling in multiple-trait QTL mapping.  Genetics Research Cambridge. 92:239-250.
http://naldc.nal.usda.gov/download/47710/PDF

X. Mi,  K. M. Eskridge,  D. Wang,  P. S. Baenzige, B. T. Campbell,   K. S. Gill,   I. Dweikat and J. Bovaird. 2010. Regression based Multi-trait QTL mapping using a structural equation model. 2010. Statistical Applications in Genetics and Molecular Biology.  9(1):article 38:1-21. DOI: 10.2202/1544-6115.1552 .  
http://www.bepress.com/sagmb/vol9/iss1/art38

Yi Wang, Kent M. Eskridge and Shunpu Zhang. 2008. Semiparametric Mixed-Effects Analysis on PK/PD Models Using Differential Equations. J. of Pharmacokinetics and Pharmacodymamics 35(4):443-463.

H. Guo, K.M. Eskridge, D. Christensen, Q. Ming and T. Safranek. 2007. Statistical adjustment for misclassification of seat belt and alcohol use in the analysis of motor vehicle accident data. Accident Analysis and Prevention. 39:117-124.

P. Dhungana, K. M. Eskridge, P. S. Baenziger, B. T. Campbell, K. S. Gill, I. Dweikat. 2007. Analysis of genotype-by-environment interaction in wheat using chromosome substitution lines and a structural equation model. Crop Science. 47(2):477-484. https://www.crops.org/publications/cs/abstracts/47/2/477 

Mehmet Nuri Nas, Kent M. Eskridge and Paul E. Read. 2005. Experimental designs suitable for testing many factors with a limited number of explants in tissue culture. Plant Cell, Tissue and Organ Culture. 81:213-220.
http://link.springer.com/article/10.1007%2Fs11240-004-5114-2#page-1 

K. M. Eskridge, S. Gilmour, R. Mead, N.A. Butler and D. A. Travnicek. 2004. Large Supersaturated Designs. Journal of Statistical Computation and Simulation. 74(7):525-542.
 http://www.ingentaconnect.com/content/tandf/gscs/2004/00000074/00000007/art00006 

N.A. Butler, R. Mead, K.M.Eskridge, and S.G. Gilmour. 2001. A general way of constructing E(S2)optimal supersaturated designs. 2001. J Royal Stat. Society (B). 63(3):621-632.  
http://onlinelibrary.wiley.com/doi/10.1111/1467-9868.00303/pdf
 

K. M. Eskridge, M. M. Shah, P.S. Baenziger and D.A. Travnicek. 2000. Correcting for Classification Errors when Estimating the Number of Genes Using Recombinant Inbred Chromosome Lines. Crop Science. 40:398-403. 
https://www.crops.org/publications/cs/abstracts/40/2/398