Logistic Regression for Prospectivity Modeling

The thesis proposes a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald statistic and Bayes' information criterion making it suitable for the processing of large data and a high number of variables that emerge in the nonlinear setting of logistic regression. The obtained models of our suggested method are parsimonious allowing for an interpretation and information gain. The advantages of our method are shown by comparing it to another model selection method and to arti cial neural networks on several datasets. Furthermore we introduced a possibility to induce spatial dependencies which are important in such geological settings.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

TU Bergakademe Freiberg

Year

2020

Author dc:creator

• Kost, Samuel

Contributors dc:contributor

• Rheinbach, Oliver
• Schaeben, Helmut

• Logistische Regression
• Statistisches Lernen
• Modellauswahl
• Prospectivity Modeling
• Logistic Regression
• Statistical Learning
• Model Selection