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Predicting the Geographic Distribution of a Species from Presence-Only Data Subject to Detection Errors

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  • Robert M. Dorazio

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  • Robert M. Dorazio, 2012. "Predicting the Geographic Distribution of a Species from Presence-Only Data Subject to Detection Errors," Biometrics, The International Biometric Society, vol. 68(4), pages 1303-1312, December.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:4:p:1303-1312
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01779.x
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    References listed on IDEAS

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    1. Avishek Chakraborty & Alan E. Gelfand & Adam M. Wilson & Andrew M. Latimer & John A. Silander, 2011. "Point pattern modelling for degraded presence‐only data over large regions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(5), pages 757-776, November.
    2. Gill Ward & Trevor Hastie & Simon Barry & Jane Elith & John R. Leathwick, 2009. "Presence-Only Data and the EM Algorithm," Biometrics, The International Biometric Society, vol. 65(2), pages 554-563, June.
    3. A. J. Lee & A. J. Scott & C. J. Wild, 2006. "Fitting binary regression models with case-augmented samples," Biometrika, Biometrika Trust, vol. 93(2), pages 385-397, June.
    4. Lancaster, Tony & Imbens, Guido, 1996. "Case-control studies with contaminated controls," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 145-160.
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    Cited by:

    1. Kosicki, Jakub Z., 2017. "Should topographic metrics be considered when predicting species density of birds on a large geographical scale? A case of Random Forest approach," Ecological Modelling, Elsevier, vol. 349(C), pages 76-85.
    2. Fernández, Daniel & Nakamura, Miguel, 2015. "Estimation of spatial sampling effort based on presence-only data and accessibility," Ecological Modelling, Elsevier, vol. 299(C), pages 147-155.
    3. Kenneth F Kellner & Robert K Swihart, 2014. "Accounting for Imperfect Detection in Ecology: A Quantitative Review," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-8, October.

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