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10.1111/j.1541-0420.2008.01116.x http://scihub22266oqcxt.onion/10.1111/j.1541-0420.2008.01116.x C4821886!4821886!18759851     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\18759851.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biometrics 2009 ; 65 (2): 554-63 Nephropedia Template TP {{tp|p=18759851|t=2009. Presence-Only Data and the EM Algorithm |pdf=|usr=}}{{18759851}} gab.com Text Presence-Only Data and the EM Algorithm JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=18759851 #FOAvip In ecological modeling of the habitat of a species, it can be prohibitively expensive to determine species absence. Presence-only data consist of a sample of locations with observed presences and a separate group of locations sampled from the full landscape, with unknown presences. We propose an expectation?maximization algorithm to estimate the underlying presence?absence logistic model for presence-only data. This algorithm can be used with any off-the-shelf logistic model. For models with stepwise fitting procedures, such as boosted trees, the fitting process can be accelerated by interleaving expectation steps within the procedure. Preliminary analyses based on sampling from presence?absence records of fish in New Zealand rivers illustrate that this new procedure can reduce both deviance and the shrinkage of marginal effect estimates that occur in the naive model often used in practice. Finally, it is shown that the population prevalence of a species is only identifiable when there is some unrealistic constraint on the structure of the logistic model. In practice, it is strongly recommended that an estimate of population prevalence be provided. Twit Text FOAVip Presence-Only Data and the EM Algorithm ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=18759851 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=18759851 #FOAvip English Wikipedia <ref>{{Cite pmid|18759851}}</ref> Presence-Only Data and the EM Algorithm #MMPMID18759851 Ward G; Hastie T; Barry S; Elith J; Leathwick JR Biometrics 2009[Jun]; 65 (2): 554-63 PMID18759851show ga In ecological modeling of the habitat of a species, it can be prohibitively expensive to determine species absence. Presence-only data consist of a sample of locations with observed presences and a separate group of locations sampled from the full landscape, with unknown presences. We propose an expectation?maximization algorithm to estimate the underlying presence?absence logistic model for presence-only data. This algorithm can be used with any off-the-shelf logistic model. For models with stepwise fitting procedures, such as boosted trees, the fitting process can be accelerated by interleaving expectation steps within the procedure. Preliminary analyses based on sampling from presence?absence records of fish in New Zealand rivers illustrate that this new procedure can reduce both deviance and the shrinkage of marginal effect estimates that occur in the naive model often used in practice. Finally, it is shown that the population prevalence of a species is only identifiable when there is some unrealistic constraint on the structure of the logistic model. In practice, it is strongly recommended that an estimate of population prevalence be provided. äPresence-Only Data and the EM Algorithm (epmc).pdf DeepDyve Pubget Overpricing