- AutorIn
- Samuel Kost
- Titel
- Logistic Regression for Prospectivity Modeling
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:105-qucosa2-728751
- Datum der Einreichung
- 07.08.2020
- Datum der Verteidigung
- 13.11.2020
- Abstract (EN)
- 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.
- Freie Schlagwörter (DE)
- Logistische Regression, Statistisches Lernen, Modellauswahl, Prospectivity Modeling
- Freie Schlagwörter (EN)
- Logistic Regression, Prospectivity Modeling, Statistical Learning, Model Selection
- Klassifikation (DDC)
- 510
- Normschlagwörter (GND)
- Logit-Modell
- Prospektion
- Maschinelles Lernen
- Statistik
- GutachterIn
- Prof. Dr. rer. nat. Oliver Rheinbach
- Prof. i. R. Dr. rer. nat. Helmut Schaeben
- BetreuerIn Hochschule / Universität
- Prof. Dr. rer. nat. Oliver Rheinbach
- Den akademischen Grad verleihende / prüfende Institution
- TU Bergakademe Freiberg, Freiberg
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:105-qucosa2-728751
- Veröffentlichungsdatum Qucosa
- 02.12.2020
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
- CC BY-SA 4.0