Logistic Regression for Prospectivity Modeling

DOI10.1007/978-3-030-55874-1_81zbMath1475.62215OpenAlexW3158872869MaRDI QIDQ5152880

Oliver Rheinbach, S. Kost, Helmut Schaeben

Publication date: 27 September 2021

Published in: Lecture Notes in Computational Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://tubaf.qucosa.de/api/qucosa%3A72875/attachment/ATT-0/

zbMATH Keywords

neural networksmodel selectionrare eventslogistic regressionmachine learningprospectivity modeling

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Unnamed Item
• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• A mathematical view of weights-of-evidence, conditional independence, and logistic regression in terms of Markov random fields
• Popular raster-based methods of prospectivity modeling and their relationships
• The Estimation of Choice Probabilities from Choice Based Samples
• Wald's Test as Applied to Hypotheses in Logit Analysis
• Applied Logistic Regression

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