{"title":"创新研究中的情境独立性","authors":"F. Nicolussi, M. Cazzaro","doi":"10.1002/9781119579465.CH1","DOIUrl":null,"url":null,"abstract":"The study of (in)dependence relationships among a set of categorical variables collected in a contingency table is an amply topic. In this work we want to focus on the so called context-specific independence where the conditional independence holds only in a subspace of the outcome space. The main aspects that we introduce concern the definition in the same model of marginal, conditional and context-specific independencies, through the marginal models. Furthermore, we investigate how it is possible to test these context-specific independencies when there are ordinal variables. Finally, we propose a graphical representation of all the considered independencies taking advantages from the chain graph model. We show the results on an application on ”The Italian Innovation Survey” of Istat (2012).","PeriodicalId":175515,"journal":{"name":"Data Analysis and Applications 2","volume":"170 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Context-specific Independence in Innovation Study\",\"authors\":\"F. Nicolussi, M. Cazzaro\",\"doi\":\"10.1002/9781119579465.CH1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study of (in)dependence relationships among a set of categorical variables collected in a contingency table is an amply topic. In this work we want to focus on the so called context-specific independence where the conditional independence holds only in a subspace of the outcome space. The main aspects that we introduce concern the definition in the same model of marginal, conditional and context-specific independencies, through the marginal models. Furthermore, we investigate how it is possible to test these context-specific independencies when there are ordinal variables. Finally, we propose a graphical representation of all the considered independencies taking advantages from the chain graph model. We show the results on an application on ”The Italian Innovation Survey” of Istat (2012).\",\"PeriodicalId\":175515,\"journal\":{\"name\":\"Data Analysis and Applications 2\",\"volume\":\"170 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Analysis and Applications 2\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119579465.CH1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Analysis and Applications 2","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119579465.CH1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The study of (in)dependence relationships among a set of categorical variables collected in a contingency table is an amply topic. In this work we want to focus on the so called context-specific independence where the conditional independence holds only in a subspace of the outcome space. The main aspects that we introduce concern the definition in the same model of marginal, conditional and context-specific independencies, through the marginal models. Furthermore, we investigate how it is possible to test these context-specific independencies when there are ordinal variables. Finally, we propose a graphical representation of all the considered independencies taking advantages from the chain graph model. We show the results on an application on ”The Italian Innovation Survey” of Istat (2012).