{"title":"利用贝叶斯优势层次来确定服务研究预测因子的重要性","authors":"Xiaoyin Wang, P. Duverger, H. Bansal","doi":"10.1109/IJCSS.2012.68","DOIUrl":null,"url":null,"abstract":"Empirical results in business research derived from multiple linear regression models are often susceptible to issues of dimensionality and multicollinearity. We extend the current research practices for addressing multicollinearity by introducing an original method, Bayesian Dominance Hierarchy (BDH) to determine the relative importance of predictors in a multiple regression context.","PeriodicalId":147619,"journal":{"name":"2012 International Joint Conference on Service Sciences","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Bayesian Dominance Hierarchies to Determine Predictor Importance in Service Research Predictive Studies\",\"authors\":\"Xiaoyin Wang, P. Duverger, H. Bansal\",\"doi\":\"10.1109/IJCSS.2012.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Empirical results in business research derived from multiple linear regression models are often susceptible to issues of dimensionality and multicollinearity. We extend the current research practices for addressing multicollinearity by introducing an original method, Bayesian Dominance Hierarchy (BDH) to determine the relative importance of predictors in a multiple regression context.\",\"PeriodicalId\":147619,\"journal\":{\"name\":\"2012 International Joint Conference on Service Sciences\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Joint Conference on Service Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCSS.2012.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Joint Conference on Service Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCSS.2012.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Bayesian Dominance Hierarchies to Determine Predictor Importance in Service Research Predictive Studies
Empirical results in business research derived from multiple linear regression models are often susceptible to issues of dimensionality and multicollinearity. We extend the current research practices for addressing multicollinearity by introducing an original method, Bayesian Dominance Hierarchy (BDH) to determine the relative importance of predictors in a multiple regression context.