ON THE PREDICTIVE PROPERTIES OF BINARY LINK FUNCTIONS

Yıl 2017, Cilt: 66 Sayı: 1, 1 - 18, 01.02.2017

Necla Gündüz Ernest Fokoué

https://doi.org/10.1501/Commua1_0000000770

Cited By: 3

Öz

This paper provides a theoretical and computational justification
of the long held claim of the similarity of the probit and logit link functions often used in binary classification. Despite this widespread recognition
of the strong similarities between these two link functions, very few (if any)
researchers have dedicated time to carry out a formal study aimed at establishing and characterizing Örmly all the aspects of the similarities and diffierences.
This paper proposes a definition of both structural and predictive equivalence
of link functions-based binary regression models, and explores the various ways
in which they are either similar or dissimilar. From a predictive analytics perspective, it turns out that not only are probit and logit perfectly predictively
concordant, but the other link functions like cauchit and complementary log
log enjoy very high percentage of predictive equivalence. Throughout this paper, simulated and real life examples demonstrate all the equivalence results
that we prove theoretically

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