{"title":"单调回归:一种简单快速的O(n) PAVA实现","authors":"F. Busing","doi":"10.18637/jss.v102.c01","DOIUrl":null,"url":null,"abstract":"Efficient coding and improvements in the execution order of the up-and-down-blocks algorithm for monotone or isotonic regression leads to a significant increase in speed as well as a short and simple O ( n ) implementation. Algorithms that use monotone regression as a subroutine, e.g., unimodal or bivariate monotone regression, also benefit from the acceleration. A substantive comparison with and characterization of currently available implementations provides an extensive overview of up-and-down-blocks implementations for the pool-adjacent-violators algorithm for simple linear ordered monotone regression.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"1 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Monotone Regression: A Simple and Fast O(n) PAVA Implementation\",\"authors\":\"F. Busing\",\"doi\":\"10.18637/jss.v102.c01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Efficient coding and improvements in the execution order of the up-and-down-blocks algorithm for monotone or isotonic regression leads to a significant increase in speed as well as a short and simple O ( n ) implementation. Algorithms that use monotone regression as a subroutine, e.g., unimodal or bivariate monotone regression, also benefit from the acceleration. A substantive comparison with and characterization of currently available implementations provides an extensive overview of up-and-down-blocks implementations for the pool-adjacent-violators algorithm for simple linear ordered monotone regression.\",\"PeriodicalId\":17237,\"journal\":{\"name\":\"Journal of Statistical Software\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.18637/jss.v102.c01\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18637/jss.v102.c01","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Monotone Regression: A Simple and Fast O(n) PAVA Implementation
Efficient coding and improvements in the execution order of the up-and-down-blocks algorithm for monotone or isotonic regression leads to a significant increase in speed as well as a short and simple O ( n ) implementation. Algorithms that use monotone regression as a subroutine, e.g., unimodal or bivariate monotone regression, also benefit from the acceleration. A substantive comparison with and characterization of currently available implementations provides an extensive overview of up-and-down-blocks implementations for the pool-adjacent-violators algorithm for simple linear ordered monotone regression.
期刊介绍:
The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.