Evaluating Classical, Robust and Non-parametric Binary Classification in Contaminated Datasets  A Comparative Simulation Study

Additional Metadata
Keywords	Robust Classification; M-Estimator; Mallows Quasi-Likelihood; Cubif; Naïve Bayes; Support Vector Classifier
Thesis Advisor	Zhelonkin, M.
Persistent URL	hdl.handle.net/2105/60891
Series	Econometrie
Organisation	Erasmus School of Economics
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Ferreira da Silva, G.V. (2021, November 26). Evaluating Classical, Robust and Non-parametric Binary Classification in Contaminated Datasets A Comparative Simulation Study. Econometrie. Retrieved from http://hdl.handle.net/2105/60891

Evaluating Classical, Robust and Non-parametric Binary Classification in Contaminated Datasets A Comparative Simulation Study