The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.

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."

The U.S. Department of Homeland Security (DHS) is responsible for securing and managing the nation's borders. Over the past decade, DHS has dramatically stepped up its enforcement efforts at the U.S.-Mexico border, increasing the number of U.S. Border patrol (USBP) agents, expanding the deployment of technological assets, and implementing a variety of "consequence programs" intended to deter illegal immigration. During this same period, there has also been a sharp decline in the number of unauthorized migrants apprehended at the border. Trends in total apprehensions do not, however, by themselves speak to the effectiveness of DHS's investments in immigration enforcement. In particular, to evaluate whether heightened enforcement efforts have contributed to reducing the flow of undocumented migrants, it is critical to estimate the number of border-crossing attempts during the same period for which apprehensions data are available. With these issues in mind, DHS charged the National Research Council (NRC) with providing guidance on the use of surveys and other methodologies to estimate the number of unauthorized crossings at the U.S.-Mexico border, preferably by geographic region and on a quarterly basis. Options for Estimating Illegal Entries at the U.S.-Mexico Border focuses on Mexican migrants since Mexican nationals account for the vast majority (around 90 percent) of attempted unauthorized border crossings across the U.S.-Mexico border. Table of Contents Front Matter Summary 1 Introduction 2 The Process of Unauthorized Crossing at the U.S.Mexico Border 3 Migration-Relevant Surveys in the United States and Mexico: Background 4 Migration-Relevant Surveys in the United States and Mexico: Usefulness and Limitations 5 Administrative Data on Undocumented Migration Across U.S. Borders 6 Model-Based Approaches to Estimating Migration Flows References Appendix A: Survey Questions About Migration and Border Crossing Appendix B: Review of Capture-Recapture Ideas for Measuring the Flow of Unauthorized Crossings at the U.S.Mexico Border Appendix C: Biographical Sketches of Panel Members Committee on National Statistics