Contact Info

Primary Title: 
Professor
Email Contact: 
Voice Contact: 
(541) 737-2028
Office: 
Kidder Hall, Room 82
Personal Page: 
Lisa Madsen

Education

  • Ph.D. Statistics, Cornell University, 2004
  • M.S. Statistics, Cornell University, 2000
  • M.S. Mathematics, University of Oregon, 1990
  • B.S. Mathematics and Computer Science, University of Oregon, 1986

Research Interests

  • Dependent discrete data
  • Abundance and occupancy models
  • Spatial statistics
  • Statistical computing and simulation
  • Ecological and environmental statistics

Selected Publications [for a full list, see link at personal webpage]

  • Dumelle, M., Migham, M., Ver Hoef, J.M., Olsen, A.R., and Madsen, L. (2022)  A comparison of design-based and model-based approaches for finite population spatial sampling and inference, Methods in Ecology and Evolution  https://doi.org/10.1111/2041-210X.13919
  • Brintz, Ben J., Madsen, L., and Fuentes, C. (2021)  A spatially explicit N-mixture model for the estimation of disease prevalence, Statistical Modelling https://journals.sagepub.com/doi/10.1177/1471082X211020872
  • Maurer, J.D., Huso, M., Dalthorp, D., Madsen, L., and Fuentes, C. (2020) Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol, Environmental and Ecological Statistics https://doi.org/10.1007/s10651-020-00466-0
  • Higham, M., Ver Hoef, J., Madsen, L., and Aderman, A. (2020) Adjusting a Finite Population Block Kriging Estimator for Imperfect Detection, Environmetrics  https://doi.org/10.1002/env.2654.
  • Madsen, L., Dalthorp, D., Huso, M., and Aderman, A. (2020). Estimating Population Size with Imperfect Detection Using a Parametric Bootstrap, Environmetrics  https://doi.org/10.1002/env.2603
  • Brintz, B., Fuentes, C., and Madsen, L. (2018). An Asymptotic Approximation to the N-mixture Model for the Estimation of Disease Prevalence, Biometrics https://doi.org/10.1111/biom.12913

 Instructional and Departmental Activities [contact the department office for syllabi]

  • ST 411/511   Methods of Data Analysis I (FAQ for prospective students)
  • ST 412/512   Methods of Data Analysis II
  • ST 415/515   Design and Analysis of Planned Experiments
  • ST 431/531   Sampling Methods
  • ST 441/541   Probability, Computing, and Simulation in Statistics
  • ST 517           Data Analytics I
  • ST 553           Statistical Methods III

 Professional Societies

  • American Statistical Association (ASA)
  • The International Environmetrics Society (TIES)