非参数回归模型的线性检验

IF 0.6 Q4 STATISTICS & PROBABILITY
Khedidja Djaballah-Djeddour, Moussa Tazerouti
{"title":"非参数回归模型的线性检验","authors":"Khedidja Djaballah-Djeddour, Moussa Tazerouti","doi":"10.17713/ajs.v51i1.1047","DOIUrl":null,"url":null,"abstract":"The problem of checking the linearity of a regression relationship is addressed. The test uses nonparametric estimation techniques. The null hypothesis is that the regression function is linear; it is tested against the non-specic alternatives hypotheses. This test is based on a Hermite transform characterization of conditional expectations. A statistical test is derived, the distribution of this statisticunder the null hypothesis of linearity is determined. A power study using simulation shows the new statistic to be more sensitive to non-linearity.","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"54 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Test for Linearity in Non-Parametric Regression Models\",\"authors\":\"Khedidja Djaballah-Djeddour, Moussa Tazerouti\",\"doi\":\"10.17713/ajs.v51i1.1047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of checking the linearity of a regression relationship is addressed. The test uses nonparametric estimation techniques. The null hypothesis is that the regression function is linear; it is tested against the non-specic alternatives hypotheses. This test is based on a Hermite transform characterization of conditional expectations. A statistical test is derived, the distribution of this statisticunder the null hypothesis of linearity is determined. A power study using simulation shows the new statistic to be more sensitive to non-linearity.\",\"PeriodicalId\":51761,\"journal\":{\"name\":\"Austrian Journal of Statistics\",\"volume\":\"54 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Austrian Journal of Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17713/ajs.v51i1.1047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Austrian Journal of Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17713/ajs.v51i1.1047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0

摘要

解决了检验回归关系线性的问题。该测试使用非参数估计技术。零假设是回归函数是线性的;对非特异性替代假设进行了检验。这个测试是基于条件期望的赫米特变换特征。导出了一个统计检验,确定了该统计量在线性零假设下的分布。仿真结果表明,新统计量对非线性更敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Test for Linearity in Non-Parametric Regression Models
The problem of checking the linearity of a regression relationship is addressed. The test uses nonparametric estimation techniques. The null hypothesis is that the regression function is linear; it is tested against the non-specic alternatives hypotheses. This test is based on a Hermite transform characterization of conditional expectations. A statistical test is derived, the distribution of this statisticunder the null hypothesis of linearity is determined. A power study using simulation shows the new statistic to be more sensitive to non-linearity.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Austrian Journal of Statistics
Austrian Journal of Statistics STATISTICS & PROBABILITY-
CiteScore
1.10
自引率
0.00%
发文量
30
审稿时长
24 weeks
期刊介绍: The Austrian Journal of Statistics is an open-access journal (without any fees) with a long history and is published approximately quarterly by the Austrian Statistical Society. Its general objective is to promote and extend the use of statistical methods in all kind of theoretical and applied disciplines. The Austrian Journal of Statistics is indexed in many data bases, such as Scopus (by Elsevier), Web of Science - ESCI by Clarivate Analytics (formely Thompson & Reuters), DOAJ, Scimago, and many more. The current estimated impact factor (via Publish or Perish) is 0.775, see HERE, or even more indices HERE. Austrian Journal of Statistics ISNN number is 1026597X Original papers and review articles in English will be published in the Austrian Journal of Statistics if judged consistently with these general aims. All papers will be refereed. Special topics sections will appear from time to time. Each section will have as a theme a specialized area of statistical application, theory, or methodology. Technical notes or problems for considerations under Shorter Communications are also invited. A special section is reserved for book reviews.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信