{"title":"以空气质量监测为例的功能异常值检测","authors":"D. Kosiorowski, J. Rydlewski, Z. Zawadzki","doi":"10.5604/01.3001.0014.0528","DOIUrl":null,"url":null,"abstract":"Methods of functional outliers detection in functional setting have been discussed, i.e. shape outliers and magnitude outliers. Outliergram has been discussed, a tool for functional shape outliers detection. Robust adjusted functional boxplot has been discussed as well, a tool for functional magnitude outliers detection. „The elements of functional outliers analysis have been applied to air pollution data for Katowice and Kraków.”\n\n","PeriodicalId":357447,"journal":{"name":"Przegląd Statystyczny","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Functional Outliers Detection by the Example of Air Quality Monitoring\",\"authors\":\"D. Kosiorowski, J. Rydlewski, Z. Zawadzki\",\"doi\":\"10.5604/01.3001.0014.0528\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Methods of functional outliers detection in functional setting have been discussed, i.e. shape outliers and magnitude outliers. Outliergram has been discussed, a tool for functional shape outliers detection. Robust adjusted functional boxplot has been discussed as well, a tool for functional magnitude outliers detection. „The elements of functional outliers analysis have been applied to air pollution data for Katowice and Kraków.”\\n\\n\",\"PeriodicalId\":357447,\"journal\":{\"name\":\"Przegląd Statystyczny\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Przegląd Statystyczny\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5604/01.3001.0014.0528\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Przegląd Statystyczny","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5604/01.3001.0014.0528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Functional Outliers Detection by the Example of Air Quality Monitoring
Methods of functional outliers detection in functional setting have been discussed, i.e. shape outliers and magnitude outliers. Outliergram has been discussed, a tool for functional shape outliers detection. Robust adjusted functional boxplot has been discussed as well, a tool for functional magnitude outliers detection. „The elements of functional outliers analysis have been applied to air pollution data for Katowice and Kraków.”