{"title":"分类数据分析中的诊断","authors":"E. Andersen","doi":"10.1111/J.2517-6161.1992.TB01451.X","DOIUrl":null,"url":null,"abstract":"Diagnostics as measures of model deviations and of the influence of particular data sets are used extensively in modern regression analysis. For contingency tables, and more generally for the parametric multinomial distribution, it is not the influence of individual observations which is of interest, but rather the contribution to a lack of model fit or to the values of the parameter estimates from a single cell in the table, which must be evaluated. Hence diagnostics for contingency tables take somewhat different forms","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1992-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":"{\"title\":\"Diagnostics in categorical data analysis\",\"authors\":\"E. Andersen\",\"doi\":\"10.1111/J.2517-6161.1992.TB01451.X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diagnostics as measures of model deviations and of the influence of particular data sets are used extensively in modern regression analysis. For contingency tables, and more generally for the parametric multinomial distribution, it is not the influence of individual observations which is of interest, but rather the contribution to a lack of model fit or to the values of the parameter estimates from a single cell in the table, which must be evaluated. Hence diagnostics for contingency tables take somewhat different forms\",\"PeriodicalId\":17425,\"journal\":{\"name\":\"Journal of the royal statistical society series b-methodological\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the royal statistical society series b-methodological\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/J.2517-6161.1992.TB01451.X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the royal statistical society series b-methodological","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/J.2517-6161.1992.TB01451.X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Diagnostics as measures of model deviations and of the influence of particular data sets are used extensively in modern regression analysis. For contingency tables, and more generally for the parametric multinomial distribution, it is not the influence of individual observations which is of interest, but rather the contribution to a lack of model fit or to the values of the parameter estimates from a single cell in the table, which must be evaluated. Hence diagnostics for contingency tables take somewhat different forms
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