{"title":"数据库系统的模糊插补方法","authors":"J. I. Peláez, J. Doña, D. Red","doi":"10.4018/978-1-59904-853-6.CH033","DOIUrl":null,"url":null,"abstract":"The missing data and nonresponse problem is a usual difficulty of particular concern in medical and social science databases. Dealing with nonresponse can be a difficult matter and it is important to apply adequate missing data methods to obtain valid inference. Missing data is a very common problem in real data sets, and different methods to solve this problem have been developed. A simple and common strategy is to ignore missing values, thus reducing the size of the useful data set. The experience in databases has demonstrated the dangers of simply removing cases (listwise deletion) from the original data set, and deletion can introduce AbstrAct","PeriodicalId":118992,"journal":{"name":"Handbook of Research on Fuzzy Information Processing in Databases","volume":"497 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Fuzzy Imputation Method for Database Systems\",\"authors\":\"J. I. Peláez, J. Doña, D. Red\",\"doi\":\"10.4018/978-1-59904-853-6.CH033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The missing data and nonresponse problem is a usual difficulty of particular concern in medical and social science databases. Dealing with nonresponse can be a difficult matter and it is important to apply adequate missing data methods to obtain valid inference. Missing data is a very common problem in real data sets, and different methods to solve this problem have been developed. A simple and common strategy is to ignore missing values, thus reducing the size of the useful data set. The experience in databases has demonstrated the dangers of simply removing cases (listwise deletion) from the original data set, and deletion can introduce AbstrAct\",\"PeriodicalId\":118992,\"journal\":{\"name\":\"Handbook of Research on Fuzzy Information Processing in Databases\",\"volume\":\"497 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Handbook of Research on Fuzzy Information Processing in Databases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-59904-853-6.CH033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Handbook of Research on Fuzzy Information Processing in Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-59904-853-6.CH033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The missing data and nonresponse problem is a usual difficulty of particular concern in medical and social science databases. Dealing with nonresponse can be a difficult matter and it is important to apply adequate missing data methods to obtain valid inference. Missing data is a very common problem in real data sets, and different methods to solve this problem have been developed. A simple and common strategy is to ignore missing values, thus reducing the size of the useful data set. The experience in databases has demonstrated the dangers of simply removing cases (listwise deletion) from the original data set, and deletion can introduce AbstrAct