{"title":"分析有系统偏差的数据","authors":"M. Zampetakis","doi":"10.1145/3572885.3572890","DOIUrl":null,"url":null,"abstract":"In many data analysis problems, we only have access to biased data due to some systematic bias of the data collection procedure. In this letter, we present a general formulation of systematic bias in data as well as our recent results on how to handle two very fundamental types of systematic bias that arise frequently in econometric studies: truncation bias and self-selection bias.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"20 1","pages":"55 - 63"},"PeriodicalIF":0.6000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing data with systematic bias\",\"authors\":\"M. Zampetakis\",\"doi\":\"10.1145/3572885.3572890\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many data analysis problems, we only have access to biased data due to some systematic bias of the data collection procedure. In this letter, we present a general formulation of systematic bias in data as well as our recent results on how to handle two very fundamental types of systematic bias that arise frequently in econometric studies: truncation bias and self-selection bias.\",\"PeriodicalId\":56237,\"journal\":{\"name\":\"ACM SIGecom Exchanges\",\"volume\":\"20 1\",\"pages\":\"55 - 63\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGecom Exchanges\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3572885.3572890\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGecom Exchanges","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3572885.3572890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
In many data analysis problems, we only have access to biased data due to some systematic bias of the data collection procedure. In this letter, we present a general formulation of systematic bias in data as well as our recent results on how to handle two very fundamental types of systematic bias that arise frequently in econometric studies: truncation bias and self-selection bias.
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