Robust Logistic Modelling for Datasets with Unusual Points
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Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
İstatistik ve Olasılık
Kaynak
Journal of New Theory
WoS Q Değeri
Scopus Q Değeri
Cilt
2021
Sayı
36
