An interpretable neural network-based non-proportional odds model for ordinal regression

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-02-22 DOI:10.1080/10618600.2024.2321208

Akifumi Okuno, Kazuharu Harada

引用次数: 0

Abstract

This study proposes an interpretable neural network-based non-proportional odds model (N3POM) for ordinal regression. N3POM is different from conventional approaches to ordinal regression with non-...

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基于神经网络的可解释非比例赔率序数回归模型

本研究针对序数回归提出了一种可解释的基于神经网络的非比例几率模型（N3POM）。N3POM 有别于传统的序数回归方法，它采用非...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.