Ebook: Case Studies in Bayesian Statistics: Volume IV

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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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.

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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.
Content:
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Modeling Customer Survey Data....Pages 3-57
Functional Connectivity in the Cortical Circuits Subserving Eye Movements....Pages 59-132
Modeling Risk of Breast Cancer and Decisions about Genetic Testing....Pages 133-203
The Bayesian approach to Population pharmacokinetic/pharmacodynamic modeling....Pages 205-265
Front Matter....Pages 267-267
Longitudinal Modeling of the Side Effects of Radiation Therapy....Pages 269-285
Analysis of hospital quality monitors using hierarchical time series models....Pages 287-302
Spatio-Temporal Hierarchical Models for Analyzing Atlanta Pediatric Asthma ER Visit Rates....Pages 303-320
Validating Bayesian Prediction Models: a Case Study in Genetic Susceptibility to Breast Cancer....Pages 321-338
Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design....Pages 339-353
Population Models for Hematologic Data....Pages 355-366
A Hierarchical Spatial Model for Constructing Wind Fields from Scatterometer Data in the Labrador Sea....Pages 367-382
Redesigning a Network of Rainfall Stations....Pages 383-393
Using PSA to detect prostate cancer onset: An application of Bayesian retrospective and prospective changepoint identification....Pages 395-412
Erratum to: Analysis of hospital quality monitors using hierarchical time series models....Pages 431-431
Back Matter....Pages 413-429

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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.
Content:
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Modeling Customer Survey Data....Pages 3-57
Functional Connectivity in the Cortical Circuits Subserving Eye Movements....Pages 59-132
Modeling Risk of Breast Cancer and Decisions about Genetic Testing....Pages 133-203
The Bayesian approach to Population pharmacokinetic/pharmacodynamic modeling....Pages 205-265
Front Matter....Pages 267-267
Longitudinal Modeling of the Side Effects of Radiation Therapy....Pages 269-285
Analysis of hospital quality monitors using hierarchical time series models....Pages 287-302
Spatio-Temporal Hierarchical Models for Analyzing Atlanta Pediatric Asthma ER Visit Rates....Pages 303-320
Validating Bayesian Prediction Models: a Case Study in Genetic Susceptibility to Breast Cancer....Pages 321-338
Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design....Pages 339-353
Population Models for Hematologic Data....Pages 355-366
A Hierarchical Spatial Model for Constructing Wind Fields from Scatterometer Data in the Labrador Sea....Pages 367-382
Redesigning a Network of Rainfall Stations....Pages 383-393
Using PSA to detect prostate cancer onset: An application of Bayesian retrospective and prospective changepoint identification....Pages 395-412
Erratum to: Analysis of hospital quality monitors using hierarchical time series models....Pages 431-431
Back Matter....Pages 413-429
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