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Research Article | Open Access

Uncertain logistic regression models

Jinling Gao1, Zengtai Gong2( )
School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730010, China
College of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, China
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Abstract

Logistic regression is a generalized nonlinear regression analysis model and is often used for data mining, automatic disease diagnosis, economic prediction, and other fields. In this paper, we first aimed to introduce the concept of uncertain logistic regression based on the uncertainty theory, as well as investigating the likelihood function in the sense of uncertain measure to represent the likelihood of unknown parameters.

Keywords

uncertain logistic regressionmaximum likelihood estimationuncertainty theory
CLC number: 03E72, 08A72, 26E50

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AIMS Mathematics
Volume 9 Issue 5,
May 2024
Pages 10478-10493
DOI: 10.3934/math.2024512

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Cite this article:
Gao J, Gong Z. Uncertain logistic regression models. AIMS Mathematics, 2024, 9(5): 10478-10493. https://doi.org/10.3934/math.2024512

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Received: 19 January 2024
Revised: 27 February 2024
Accepted: 01 March 2024
Published: 15 May 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)