Nonparametric Predictive Inference. Wiley Series in Probability and Statistics

This book will be the first on NPI and will provide an introduction to and overview of, the approach′s current state of the art. It will be a self–contained treatment of the subject, introducing it to readers, and leading them on to a more advanced and specialist understanding. The Author compares and contrasts NPI theory with classical statistical theory, pointing out the ways in which NPI can enhance current research in areas ranging from operations research to engineering and artificial intelligence.

After the initial introductory chapter, the book provides a series of chapters outlining the use of NPI in specific settings, e.g. for real–valued random quantities or for multinomial data. This will be followed by chapters detailing further applications in statistics, providing examples such as NPI for statistical quality and process control, reliability and operations research, with a variety of examples such as maintenance and replacement problems, queuing situations and risk reliability inferences.

The foundations and ideas behind NPI will be presented along with an examination and comparison of more traditional approaches of classical and Bayesian statistics, providing further insights into the advantages of NPI. Future directions and the accommodation of multivariate data will also be discussed.