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Lisa Madsen

Department of Statistics

Lisa Madsen

Department of Statistics


Research Interests

  • Dependent Discrete Data
  • Abundance and Occupancy Models
  • Spatial Statistics
  • Statistical Computing and Simulation
  • Ecological and Environmental Statistics


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


  • Madsen, L., & Royle, J. A. (2023) A review of N‐mixture models. Wiley Interdisciplinary Reviews: Computational Statistics, e1625.
  • Dumelle, M., Higham, 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, 13(9), 2018-2029.
  • Brintz, Ben J., Madsen, L., and Fuentes, C. (2021) A spatially explicit N-mixture model for the estimation of disease prevalence, Statistical Modelling, 23(1), 31-52.
  • 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
  • Higham, M., Ver Hoef, J., Madsen, L., and Aderman, A. (2020) Adjusting a Finite Population Block Kriging Estimator for Imperfect Detection, Environmetrics
  • Madsen, L., Dalthorp, D., Huso, M., and Aderman, A. (2020). Estimating Population Size with Imperfect Detection Using a Parametric Bootstrap, Environmetrics
  • Brintz, B., Fuentes, C., and Madsen, L. (2018). An Asymptotic Approximation to the N-mixture Model for the Estimation of Disease Prevalence, Biometrics
  • Ossiander, M., Peszynska, M., Madsen, L. et al. (2017), Estimation and simulation for geospatial porosity and permeability data, Environmental and Ecological Statistics, 24(1), 109-130.
  • Huso, M.M.P., Dalthorp, D., Dail D., and Madsen, L. (2015). Estimating turbine-caused bird and bat fatality when zero carcasses are observed, Ecological Applications 25(5), 1213-1225
  • Fang, Y., Madsen, L., and Liu, L. (2014) Comparison of Two Methods to Check Copula Fitting, International Journal of Applied Mathematics 44(1), 53-61.
  • Fang, Y. and Madsen, L. (2013) Modifed Gaussian pseudo-copula: Applications in insurance and finance, Insurance: Mathematics and Economics 53, 292-301
  • Dail, D. and Madsen, L. (2013) Estimating open population site occupancy from presence-absence data lacking the robust design, Biometrics 69(1), 146-156
  • Madsen, L. and Birkes, D. (2013) Simulating dependent discrete data, Journal of Statistical Computation and Simulation 83(4), 677-691.
  • Groom, J., Dent, L., Madsen, L., and Fleuret, J. (2011) Response of western Oregon (USA) stream temperatures to contemporary forest management, Forest Ecology and Management 262(8), 1618-1629
  • Madsen, L. and Fang, Y. (2011) Joint Regression Analysis for Discrete Longitudinal Data, Biometrics 67(3), 1171-1175
  • Dail, D. and Madsen, L. (2011) Models for Estimating Abundance from Repeated Counts of an Open Metapopulation, Biometrics 67(2), 577-587
  • Eskelson, Bianca N.I., Madsen, L., Hagar, J., and Temesgen, H. (2011) Estimating riparian understory vegetation cover with beta regression and copula models, Forest Science 57(3), 212-221
  • Groom, J., Dent, L., and Madsen, L. (2011) Stream temperature change detection for state and private forests in the Oregon Coast Range, Water Resources Research 47(1)
  • Madsen, L. (2009), Maximum Likelihood Estimation of Regression Parameters with Spatially Dependent Discrete Data, Journal of Agricultural, Biological, and Environmental Statistics 14(4), 375-391.
  • Madsen, L., Ruppert, D., and Altman, N.S. (2008), Regression with Spatially Misaligned Data, Environmetrics 19(5), 453-467
  • Madsen, L. and Dalthorp, D. (2007), Simulating Correlated Count Data, Environmental and Ecological Statistics 14(2), 129-148.