Publications

• Madsen, L., & Royle, J. A. (2023) A review of N‐mixture models. Wiley Interdisciplinary Reviews: Computational Statistics, e1625. https://doi.org/10.1002/wics.1625
• 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 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
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 https://doi.org/10.1890/14-0764.1.
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 https://doi.org/10.1016/j.insmatheco.2013.05.009.
Dail, D. and Madsen, L. (2013) Estimating open population site occupancy from presence-absence data lacking the robust design, Biometrics 69(1), 146-156 https://doi.org/10.1111/j.1541-0420.2012.01796.x.
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 https://doi.org/10.1016/j.foreco.2011.07.012.
Madsen, L. and Fang, Y. (2011) Joint Regression Analysis for Discrete Longitudinal Data, Biometrics 67(3), 1171-1175 https://doi.org/10.1111/j.1541-0420.2010.01494.x.
Dail, D. and Madsen, L. (2011) Models for Estimating Abundance from Repeated Counts of an Open Metapopulation, Biometrics 67(2), 577-587 https://doi.org/10.1111/j.1541-0420.2010.01465.x.
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 https://doi.org/10.1093/forestscience/57.3.212.
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) https://doi.org/10.1029/2009WR009061.
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 https://doi.org/10.1002/env.888.
Madsen, L. and Dalthorp, D. (2007), Simulating Correlated Count Data, Environmental and Ecological Statistics 14(2), 129-148.