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Open AccessArticle

Binomial Regression Models with a Flexible Generalized Logit Link Function

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Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Jl. Arif Rahman Hakim Surabaya 60111, Indonesia
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BPS-Statistics Indonesia, Jl. Dr. Sutomo 6-8, Jakarta 10710, Indonesia
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Department of Business Statistics, Faculty of Vocational, Institut Teknologi Sepuluh Nopember, Jl. Arif Rahman Hakim Surabaya 60111, Indonesia
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Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 221; https://doi.org/10.3390/sym12020221
Received: 21 December 2019 / Revised: 19 January 2020 / Accepted: 27 January 2020 / Published: 2 February 2020
In binomial regression, a link function is used to join the linear predictor variables and the expectation of the response variable. This paper proposes a flexible link function from a new class of generalized logistic distribution, namely a flexible generalized logit (glogit) link. This approach considers both symmetric and asymmetric models, including the cases of lighter and heavier tails, as compared to standard logistic. The glogit is created from the inverse cumulative distribution function of the exponentiated-exponential logistic (EEL) distribution. Using a Bayesian framework, we conduct a simulation study to investigate the model performance compared to the most commonly used link functions, e.g., logit, probit, and complementary log–log. Furthermore, we compared the proposed model with several other asymmetric models using two previously published datasets. The results show that the proposed model outperforms the existing ones and provides flexibility fitting the experimental dataset. Another attractive aspect of the model are analytically tractable and can be easily implemented under a Bayesian approach. View Full-Text
Keywords: binomial regression; generalized linear model; symmetric and asymmetric link functions; flexible generalized logit link; Bayesian estimation binomial regression; generalized linear model; symmetric and asymmetric link functions; flexible generalized logit link; Bayesian estimation
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Prasetyo, R.B.; Kuswanto, H.; Iriawan, N.; Ulama, B.S.S. Binomial Regression Models with a Flexible Generalized Logit Link Function. Symmetry 2020, 12, 221.

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