{"title":"基于神经网络的可解释非比例赔率序数回归模型","authors":"Akifumi Okuno, Kazuharu Harada","doi":"10.1080/10618600.2024.2321208","DOIUrl":null,"url":null,"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-...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"47 18 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An interpretable neural network-based non-proportional odds model for ordinal regression\",\"authors\":\"Akifumi Okuno, Kazuharu Harada\",\"doi\":\"10.1080/10618600.2024.2321208\",\"DOIUrl\":null,\"url\":null,\"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-...\",\"PeriodicalId\":15422,\"journal\":{\"name\":\"Journal of Computational and Graphical Statistics\",\"volume\":\"47 18 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational and Graphical Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1080/10618600.2024.2321208\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational and Graphical Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/10618600.2024.2321208","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
An interpretable neural network-based non-proportional odds model for ordinal regression
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-...
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.