Publications:

Madsen, Lisa and Royle, J. Andrew (2023) A review of N‐mixture models, Wiley Interdisciplinary Reviews: Computational Statistics: e1625. https://doi.org/10.1002/wics.1625.

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., 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.M.P., 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.

Groom, J.D., Madsen, L.J, Jones, J.E., and Giovanini, J.N. (2018) Informing changes to riparian forestry rules with a Bayesian hierarchical model, Forest Ecology and Management 419, 17-30 https://doi.org/10.1016/j.foreco.2018.03.014.

Ossiander, M., Peszynska, M., Madsen, L., Muir, A., and Harbert, W. (2017) Estimation and simulation for geospatial porosity and permeability data, Environmental and Ecological Statistics 24(1), 109-130 https://link.springer.com/article/10.1007/s10651-016-0362-y.

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 (Example R code).

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. (Supplemental materials)

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 (Correction) 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.