Robust Logistic Modelling for Datasets with Unusual Points
| dc.contributor.author | Tekin, Kumru Urgancı | |
| dc.contributor.author | Mestav, Burcu | |
| dc.contributor.author | İyit, Neslihan | |
| dc.date.accessioned | 2025-01-27T19:37:16Z | |
| dc.date.available | 2025-01-27T19:37:16Z | |
| dc.date.issued | 2021 | |
| dc.department | Çanakkale Onsekiz Mart Üniversitesi | |
| dc.description.abstract | Unusual Points (UPs) occur for different reasons, such as an observational error or the\rpresence of a phenomenon with unknown cause. Influential Points (IPs), one of the UPs, have a\rnegative effect on parameter estimation in the Logistic Regression model. Many researchers in fisheries\rsciences face this problem and have recourse to some manipulations to overcome this problem. The\rlimitations of these manipulations have prompted researchers to use more suitable and innovative\restimation techniques to deal with the problem. In this study, we examine the classification accuracies\rand parameter estimation performances of the Maximum Likelihood (ML) estimator and robust\restimators through modified real datasets and simulation experiments. Besides, we discuss the potential\rapplicability of the assessed robust estimators to the estimation models when the IPs are kept in the\rdataset. The obtained results show that the Weighted Maximum Likelihood (WML) and Weighted\rBianco-Yohai (WBY) estimators of robust estimators outperform the others. | |
| dc.identifier.doi | 10.53570/jnt.971062 | |
| dc.identifier.endpage | 63 | |
| dc.identifier.issn | 2149-1402 | |
| dc.identifier.issue | 36 | |
| dc.identifier.startpage | 49 | |
| dc.identifier.trdizinid | 535314 | |
| dc.identifier.uri | https://doi.org/10.53570/jnt.971062 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/535314 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12428/17174 | |
| dc.identifier.volume | 2021 | |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.language.iso | en | |
| dc.relation.ispartof | Journal of New Theory | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_TRD_20250125 | |
| dc.subject | İstatistik ve Olasılık | |
| dc.title | Robust Logistic Modelling for Datasets with Unusual Points | |
| dc.type | Article |
