Nonparametric Additive Models for Billion Observations

IF 1.4 2区 数学 Q2 STATISTICS & PROBABILITY Journal of Computational and Graphical Statistics Pub Date : 2024-02-15 DOI:10.1080/10618600.2024.2319684
Mengyu Li, Jingyi Zhang, Cheng Meng
{"title":"Nonparametric Additive Models for Billion Observations","authors":"Mengyu Li, Jingyi Zhang, Cheng Meng","doi":"10.1080/10618600.2024.2319684","DOIUrl":null,"url":null,"abstract":"The nonparametric additive model (NAM) is a widely used nonparametric regression method. Nevertheless, due to the high computational burden, classic statistical techniques for fitting NAMs are not ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"23 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-02-15","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/10.1080/10618600.2024.2319684","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0

Abstract

The nonparametric additive model (NAM) is a widely used nonparametric regression method. Nevertheless, due to the high computational burden, classic statistical techniques for fitting NAMs are not ...
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
十亿观测数据的非参数加法模型
非参数加法模型(NAM)是一种广泛使用的非参数回归方法。然而,由于计算负担较重,用于拟合 NAM 的经典统计技术并不 ...
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
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.
期刊最新文献
High-Dimensional Block Diagonal Covariance Structure Detection Using Singular Vectors Optimal Subsampling for Data Streams with Measurement Constrained Categorical Responses Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates Latent Markov time-interaction processes Multi-label Random Subspace Ensemble Classification1
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1