{"title":"Distance-Based Analysis with Quantile Regression Models.","authors":"Shaoyu Li, Yanqing Sun, Liyang Diao, Xue Wang","doi":"10.1007/s12561-021-09306-6","DOIUrl":null,"url":null,"abstract":"<p><p>Non-standard structured, multivariate data are emerging in many research areas, including genetics and genomics, ecology, and social science. Suitably defined pairwise distance measures are commonly used in distance-based analysis to study the association between the variables. In this work, we consider a linear quantile regression model for pairwise distances. We investigate the large sample properties of an estimator of the unknown coefficients and propose statistical inference procedures correspondingly. Extensive simulations provide evidence of satisfactory finite sample properties of the proposed method. Finally, we applied the method to a microbiome association study to illustrate its utility.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":"291-312"},"PeriodicalIF":0.4000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-021-09306-6","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12561-021-09306-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/3/27 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Non-standard structured, multivariate data are emerging in many research areas, including genetics and genomics, ecology, and social science. Suitably defined pairwise distance measures are commonly used in distance-based analysis to study the association between the variables. In this work, we consider a linear quantile regression model for pairwise distances. We investigate the large sample properties of an estimator of the unknown coefficients and propose statistical inference procedures correspondingly. Extensive simulations provide evidence of satisfactory finite sample properties of the proposed method. Finally, we applied the method to a microbiome association study to illustrate its utility.
期刊介绍：
Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science.
SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
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