Kent M. Eskridge

Portrait of Kent 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:

Eskridge, K. M., Gilmour, S. G., & Posadas, L. G. (2019). Group screening for rare events based on incomplete block designs. Biotechnology progress, 35(2). https://www.ncbi.nlm.nih.gov/pubmed/30592187

Li, M., Eskridge, K., Liu, E., & Wilkins, M. (2019). Enhancement of polyhydroxybutyrate (PHB) production by 10-fold from alkaline pretreatment liquor with an oxidative enzyme-mediator-surfactant system under Plackett-Burman and central composite designs. Bioresource technology, 281, 99-106. https://www.ncbi.nlm.nih.gov/pubmed/30807996

Li, M., Eskridge, K. M., & Wilkins, M. R. (2019). Optimization of polyhydroxybutyrate production by experimental design of combined ternary mixture (glucose, xylose and arabinose) and process variables (sugar concentration, molar C: N ratio). Bioprocess and biosystems engineering, 1-12. https://europepmc.org/abstract/med/31111213

Miller, J. J., Schepers, J. S., Shapiro, C. A., Arneson, N. J., Eskridge, K. M., Oliveira, M. C., & Giesler, L. J. (2018). Characterizing soybean vigor and productivity using multiple crop canopy  sensor readings. Field crops research, 216, 22-31. https://maxweeds.rbind.io/publication/2018-miller-fieldcropsresearch/

Jurado, N. V., Eskridge, K. M., Kachman, S. D., & Lewis, R. M. (2018). Using a Bayesian Hierarchical  Linear Mixing Model to Estimate Botanical Mixtures. Journal of Agricultural, Biological and  Environmental Statistics, 23(2), 190-207. https://link-springer-com.libproxy.unl.edu/article/10.1007%2Fs13253-018-0318-9

Yuan, B., Lu, M., Eskridge, K. M., & Hanna, M. A. (2018). Valorization of hazelnut shells into natural antioxidants by ultrasound‐assisted extraction: Process optimization and phenolic composition identification. Journal of food process engineering, 41(5), e12692. https://onlinelibrary-wiley-com.libproxy.unl.edu/doi/full/10.1111/jfpe.12692

Rafsanjani, H. N., Ahn, C. R., & Eskridge, K. M. (2018). Understanding the recurring patterns of  occupants' energy-use behaviors at entry and departure events in office buildings. Building and environment, 136, 77-87. https://www-sciencedirect-com.libproxy.unl.edu/science/article/pii/S0360132318301720?via%3Dihub

Verma, T., Wei, X., Lau, S. K., Bianchini, A., Eskridge, K. M., Stratton, J., ... & Subbiah, J.  (2018). Response surface methodology for Salmonella inactivation during extrusion processing of oat flour. Journal of food protection, 81(5), 815-826. https://www.ncbi.nlm.nih.gov/pubmed/29648932

Mugabi, R., Eskridge, K. M., & Weller, C. L. (2017). Comparison of experimental designs used to study variables during hammer milling of corn bran. Transactions of the ASABE, 60(2), 537-544.

Montesinos-López, O. A., Montesinos-López, A., Eskridge, K., & Crossa, J. (2017). Inverse sampling  regression for pooled data. Statistical methods in medical research, 26(3), 1093-1109. https://journals.sagepub.com/doi/abs/10.1177/0962280214568047

Montesinos-López, O. A., Montesinos-López, A., Crossa, J., Toledo, F. H., Pérez-Hernández, O., Eskridge, K. M., & Rutkoski, J. (2016). A genomic Bayesian multi-trait and multi-environment model. G3: Genes, Genomes, Genetics, 6(9), 2725-2744. https://www.g3journal.org/content/ggg/6/9/2725.full.pdf

Zhang, Y., Sallach, J. B., Hodges, L., Snow, D. D., Bartelt-Hunt, S. L., Eskridge, K. M., & Li, X.  (2016). Effects of soil texture and drought stress on the uptake of antibiotics and the internalization of Salmonella in lettuce following wastewater irrigation. Environmental pollution,  208, 523-531. https://www.ncbi.nlm.nih.gov/pubmed/26552531

Farmaha, B. S., Eskridge, K. M., Cassman, K. G., Specht, J. E., Yang, H., & Grassini, P. (2016).  Rotation impact on on-farm yield and input-use efficiency in high-yield irrigated maize–soybean  systems. Agronomy Journal, 108(6), 2313-2321.

Stephenson, M. B., Schacht, W. H., Volesky, J. D., Eskridge, K. M., & Bauer, D. (2015). Time of  grazing effect on subsequent-year standing crop in the Eastern Nebraska Sandhills. Rangeland ecology  & management, 68(2), 150-157. https://bioone.org/journals/rangeland-ecology-and-management/volume-68/issue-2/j.rama.2015.01.010/Time-of-Grazing-Effect-on-Subsequent-Year-Standing-Crop-in/10.1016/j.rama.2015.01.010.pdf

Grassini, P., Eskridge, K. M., & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature communications, 4, 2918. https://www.nature.com/articles/ncomms3918

Kumar, A., Eskridge, K., Jones, D. D., & Hanna, M. A. (2009). Steam–air fluidized bed gasification of distillers grains: Effects of steam to biomass ratio, equivalence ratio and gasification temperature. Bioresource Technology, 100(6), 2062-2068. https://www.ncbi.nlm.nih.gov/pubmed/19028089

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.   https://doi.org/10.1007/s11135-011-9575-1

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