LOJİSTİK REGRESYON MODELİNDE KÖTÜ KALDIRAÇ NOKTALARININ BELİRLENMESİ İÇİN YENİ SAĞLAM EŞİK DEĞERLER

Year 2021, Volume: 8 Issue: 2, 630 - 650, 27.07.2021

Ebru Gündoğan Aşık , Arzu Altin Yavuz , Zafer Küçük

https://doi.org/10.30798/makuiibf.691623

Abstract

Yüksek kaldıraç noktası, x uzayının merkezine uzak olan değer olarak adlandırılır. İyi ya da kötü kaldıraç noktaları yüksek kaldıraç noktası olabilir. Kötü kaldıraç noktaları, yanlış sınıflandırılmış gözlemler veya x uzayındaki diğer gözlem değerleri ile uyumsuzluk gösteren aykırı değerlerdir. Kötü kaldıraç noktalarının belirlenmesinde maskeleme ve süpürme problemini ortadan kaldırmak için kullanılan grup silme yöntemi lojistik regresyon modelinde de kullanılmaktadır. Bu çalışmada kötü kaldıraç noktalarının belirlenmesinde literatürde mevcut olan Sapma Bileşenleri (Deviance Component, DEVC) yöntemi için bazı sağlam eşik değerleri önerilmiştir. Yapılan simülasyon çalışması ile sapma bileşenleri yönteminde kullanılması için önerilen sağlam eşik değerlerin literatürde mevcut olan eşik değerden daha iyi sonuçlar verdiği ortaya konmuştur.

Keywords

Lojistik Regresyon, Yüksek Kaldıraç Noktası, Kötü Kaldıraç, Eşik Değeri

NEW ROBUST CUT-OFF VALUES IN DETERMINING BAD LEVERAGE POINTS IN THE LOGISTIC REGRESSION MODEL

Abstract

The high leverage point is called the value far from the center of the x space. Good or bad leverage points can be high leverage points. Bad leverage points misclassified observations and outliers that are incompatible with other observation values in the x space. The group deletion method used to eliminate the masking and swamping problem in determining bad leverage points is also used in the logistic regression model. In this study, some robust cut-off points are proposed for the deviation components method available in the literature for determining bad leverage points. In this study, some robust cut-off points are proposed for the deviation components method available in the literature for determining bad leverage points.

Keywords

Logistic regression, High Leverage Point, Bad Leverage, Cut-off Point

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