Parametric Statistical Change Point Analysis (eBook) von Jie Chen

This book offers in-depth study of the change point problem, and an examination of change point analysis of common statistical models. Change point problems are encountered in economics, finance, medicine, signal processing, and geology, to mention a few.

Preface.- Preliminaries.- Introduction.- Univariate Normal Model.- Multivariate Normal Model.- Regression Model.- Gamma Model.- Exponential Model.- Change Point Model for the Hazard Function.- Discrete Models.- Other Models.- Bibliography.- Author Index.- Subject Index.

This revised and expanded second edition is an in-depth study of the change point problem from a general point of view, as well as a further examination of change point analysis of the most commonly used statistical models. Change point problems are encountered in such disciplines as economics, finance, medicine, psychology, signal processing, and geology, to mention only several. More recently, change point analysis has been found in extensive applications related to analyzing biomedical imaging data, array Comparative Genomic Hybridization (aCGH) data, and gene expression data.

The exposition throughout the work is clear and systematic, with a great deal of introductory material included. Different models are presented in each chapter, including gamma and exponential models, rarely examined thus far in the literature. Extensive examples throughout the text emphasize key concepts and different methodologies used, namely the likelihood ratio criterion as well as the Bayesian and information criterion approaches. New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control are added in this second edition. Also included are two new chapters on change points in the hazard function and other practical change point models such as the epidemic change point model and a smooth-and-abrupt change point model. An up-to-date comprehensive bibliography and two indices round out the work.