An integrated dominance analysis and dynamic programing approach for measuring predictor importance for customer satisfaction

作     者：Brusco, Michael J. Cradit, J. Dennis Brudvig, Susan 

作者机构：Florida State Univ Dept Business Analyt Informat Syst & Supply Chain Tallahassee FL 32306 USA Indiana Univ East Sch Business & Econ Richmond IN USA 

出 版 物：《COMMUNICATIONS IN STATISTICS-THEORY AND METHODS》 (统计学通讯：理论与方法)

年 卷 期：2019年第48卷第21期

页      面：5290-5307页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Multiple regression predictor importance dominance analysis dynamic programing subset selection customer satisfaction 

摘      要：Dominance analysis is a procedure for measuring the importance of predictors in multiple regression analysis. We show that dominance analysis can be enhanced using a dynamic programing approach for the rank-ordering of predictors. Using customer satisfaction data from a call center operation, we demonstrate how the integration of dominance analysis with dynamic programing can provide a better understanding of predictor importance. As a cautionary note, we recommend careful reflection on the relationship between predictor importance and variable subset selection. We observed that slight changes in the selected predictor subset can have an impact on the importance rankings produced by a dominance analysis.