F Jay Breidt Professor & Undergraduate Director

Office: Statistics Building 201

Phone: (970) 491-6786

Google Scholar: https://scholar.google.com/citations?user=i-PzBId6al0C&hl=en&oi=ao

Education

  • 1991: PhD, Statistics, Colorado State University
  • 1989: MS, Statistics, Colorado State University
  • 1987: BA, Mathematics and English Literature, The College of Idaho

About

Jay Breidt, Professor and past Chair of the Department of Statistics at Colorado State University, has expertise in survey sampling, time series, nonparametric regression, and uncertainty quantification for complex scientific models. He received his PhD at Colorado State University in 1991 and spent the first nine years of his career at Iowa State University as an assistant professor and tenured associate professor, before returning to Colorado State in 2000. Breidt has an extensive record of refereed publications. He has presented over 130 invited short courses, conference talks, and academic seminars. Since 1991, his research has been supported continuously by a variety of agencies including the National Science Foundation, National Institutes of Health, Department of Homeland Security, Environmental Protection Agency, US Forest Service, and NASA.  Breidt is the Reviews Editor for the Journal of the American Statistical Association and The American Statistician, and has been an associate editor for seven different journals.  He serves on the US Bureau of the Census Scientific Advisory Committee and has served on six review committees for the National Academy of Sciences.  He is past Chair of the American Statistical Association National Committee on Energy Statistics (an advisory panel for the Energy Information Administration, US Department of Energy) and has served two terms on the Federal Economic Statistics Advisory Committee.  Breidt has received numerous honors, including recognition with a national prize in environmental statistics, elected membership in the International Statistical Institute, and elected fellowship in the American Statistical Association and the Institute of Mathematical Statistics.  Breidt teaches courses at all levels in statistical theory and methods and currently serves as Undergraduate Director for the Department of Statistics.

Publications

climate and Soil characteristics Determine Where no-till Management can Store carbon in Soils and Mitigate Greenhouse Gas emissionsStephen M. Ogle, Cody Andrew Alsaker, Jeff Baldock, Martial Bernoux, F Jay Breidt, Brian McConkey, Kristiina Regina, Gabriel G. Vazquez-Amabile Scientific reports, 1, 2019.
Estimation of fish consumption rates based on a creel angler survey of an urban river in New Jersey, USABetsy Ruffle, Suzanne Baird, Gemma Kirkwood, F Jay Breidt Human and Ecological Risk Assessment: An International Journal, 2019.
Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysisMeng Cao, Wen Zhou, F Jay Breidt, Graham Peers Biometrics, 2019.
Minimum Mean Squared Error Estimation of the Radius of Gyration in Small-Angle X-Ray Scattering ExperimentsCody Andrew Alsaker, F Jay Breidt, van der Woerd, Mark J Journal of the American Statistical Association, 525, 2019.
Large scale maximum average power multiple inference on time course data of counts and applications to RNA-seq analysisMeng Cao, Wen Zhou, F Jay Breidt, Graham Peers Biometrics.
Model-assisted survey estimation with imperfectly matched auxiliary dataInternational Conference of the Thailand Econometrics SocietyF Jay Breidt, Jean D. Opsomer, Chien-Min Huang Springer, 2018.
Pilot surveys to improve monitoring of marine recreational fisheries in HawaiʻiHongguang Ma, Tom K. Ogawa, Thomas R. Sminkey, F Jay Breidt, Virginia M. Lesser, Jean D. Opsomer, John R. Foster, David A. Van Voorhees Fisheries research, 2018.
Understanding the drivers of sensitive behavior using Poisson regression from quantitative randomized response technique dataMeng Cao, F Jay Breidt, Jennifer N. Solomon, Abu Conteh, Michael Gavin PloS one, 9, 2018.
Asymptotics for the maximum sample likelihood estimator under informative selection from a finite populationDaniel Bonnery, F Jay Breidt, Francois Coquet Bernoulli, 2, 2017.
Improved Bayesian Inference in the General Projected Normal Distribution of Arbitrary Dimension: Modeling and Bayesian InferenceDaniel Hernandez-Stumpfhauser, F Jay Breidt, van der Woerd, Mark J, others Bayesian Analysis, 1, 2017.