Case Studies in Bayesian Statistics
Volume IV
Ed. by Constantine Gatsonis, Robert E. Kass, Bradley Carlin et al.
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While most of the case studies in this volume come from biomedical research, the reader will also find studies in environmental science and marketing research. The 4th Workshop on Case Studies in Bayesian Statistics was held at Carnegie-Mellon University September 27-28, 1997. As in the past, the workshop featured both invited and contributed case studies. The former were presented in detail while the latter were presented in poster format. This volume contains the four invited case studies with the accompanying discussion as well as nine contributed papers selected by a refereeing process.
The 4th Workshop on Case Studies in Bayesian Statistics was held at the Car negie Mellon University campus on September 27-28, 1997. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the four invited case studies with the accompanying discus sion as well as nine contributed papers selected by a refereeing process. While most of the case studies in the volume come from biomedical research the reader will also find studies in environmental science and marketing research. INVITED PAPERS In Modeling Customer Survey Data, Linda A. Clark, William S. Cleveland, Lorraine Denby, and Chuanhai LiD use hierarchical modeling with time series components in for customer value analysis (CVA) data from Lucent Technologies. The data were derived from surveys of customers of the company and its competi tors, designed to assess relative performance on a spectrum of issues including product and service quality and pricing. The model provides a full description of the CVA data, with random location and scale effects for survey respondents and longitudinal company effects for each attribute. In addition to assessing the performance of specific companies, the model allows the empirical exploration of the conceptual basis of consumer value analysis. The authors place special em phasis on graphical displays for this complex, multivariate set of data and include a wealth of such plots in the paper.
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