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The series of workshops Case Studies in Bayesian Statistics at Carnegie Mellon University is unique in devoting an entire meeting to extended presentation and discussion of scientific investigations in which statisticians play central roles within integrated, cross- disciplinary teams. The goal has been to elucidate the interplay between Bayesian theory and practice, and thereby identify successful methods and indicate important directions for future research. This volume contains the four invited case studies, with accompanying discussion, and nine contributed papers, from the 4th workshop, which was held September 27-28, 1997. 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. Students and teachers of statistics, research statisticians, and investigators from other fields should find a wealth of ideas and methods in this series of case studies.
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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