Isabella Verdinelli – författare

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 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.

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.

This comprehensive modern look at regression covers a wide range of topics and relevant contemporary applications, going well beyond the topics covered in most introductory books. With concision and clarity, the authors present linear regression, nonparametric regression, classification, logistic and Poisson regression, high-dimensional regression, quantile regression, conformal prediction and causal inference. There are also brief introductions to neural nets, deep learning, random effects, survival analysis, graphical models and time series. Suitable for advanced undergraduate and beginning graduate students, the book will also serve as a useful reference for researchers and practitioners in data science, machine learning, and artificial intelligence who want to understand modern methods for data analysis.

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 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.