Paper Info
Title
Improved Logistic Regression Approach to Predict the Potential Distribution of Invasive Species Using Information Theory and Frequency Statistics
Abstract
The predictive models of the potential distribution of invasive species are important for managing the growing invasive species crises. However, for most species absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using information theory and frequency statistics to produce a relative suitability map. Logistic regression model selection was based on Akaike's information criterion (AIC). Based on the weighted average model we provided the quantile statistics method to compartmentalize the relative habitat-suitability in native ranges. Finally, we used the model and the compartmentalize criterion developed in native ranges to "project" onto exotic ranges to predict the invasive species' potential distribution
Year
DOI
Venue
2006
10.1109/ICDMW.2006.96
ICDM Workshops
Keywords
Field
DocType
statistical distributions,weighted average model,logistic regression model selection,compartmentalize criterion,suitability map,invasive species,regression analysis,invasive species crisis,potential distribution,quantile statistics,improved logistic regression approach,information theory,exotic range,habitat suitability,species absence data,akaike information criterion,native range,information criterion,frequency statistics,predictive model,ecology,logistic regression,logistic regression model,prediction model
Econometrics,Bayesian information criterion,Akaike information criterion,Logistic distribution,Regression diagnostic,Computer science,Multinomial logistic regression,Nonparametric regression,Logistic model tree,Statistics,Logistic regression
Conference
ISBN
Citations 
PageRank 
0-7695-2702-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Hao Chen141.97
Lijun Chen200.34
Thomas P. Albright351.01
Qinfeng Guo400.34