Sudheesh K. Kattumannil, Deepesh Bhati, Isha Dewan
{"title":"Tests for independence against regression and expectation dependence","authors":"Sudheesh K. Kattumannil, Deepesh Bhati, Isha Dewan","doi":"10.1007/s10463-025-00949-6","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we propose nonparametric tests based on <i>U</i>-statistics for testing independence against two different classes of alternatives: positive regression dependence and positive expectation dependence. We obtain the asymptotic distribution of the test statistics both under the null and the alternative hypothesis. An extensive Monte Carlo simulation study is done to assess the finite sample performance of the proposed tests. The test procedures are illustrated using two data sets.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"78 2","pages":"327 - 349"},"PeriodicalIF":1.4000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of the Institute of Statistical Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10463-025-00949-6","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose nonparametric tests based on U-statistics for testing independence against two different classes of alternatives: positive regression dependence and positive expectation dependence. We obtain the asymptotic distribution of the test statistics both under the null and the alternative hypothesis. An extensive Monte Carlo simulation study is done to assess the finite sample performance of the proposed tests. The test procedures are illustrated using two data sets.
期刊介绍:
Annals of the Institute of Statistical Mathematics (AISM) aims to provide a forum for open communication among statisticians, and to contribute to the advancement of statistics as a science to enable humans to handle information in order to cope with uncertainties. It publishes high-quality papers that shed new light on the theoretical, computational and/or methodological aspects of statistical science. Emphasis is placed on (a) development of new methodologies motivated by real data, (b) development of unifying theories, and (c) analysis and improvement of existing methodologies and theories.