Parametric Statistical Change Point Analysis

With Applications to Genetics, Medicine, and Finance

  • Jie Chen
  • Arjun K. Gupta

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Jie Chen, Arjun K. Gupta
    Pages 1-5
  3. Jie Chen, Arjun K. Gupta
    Pages 7-88
  4. Jie Chen, Arjun K. Gupta
    Pages 89-138
  5. Jie Chen, Arjun K. Gupta
    Pages 139-154
  6. Jie Chen, Arjun K. Gupta
    Pages 155-172
  7. Jie Chen, Arjun K. Gupta
    Pages 173-187
  8. Jie Chen, Arjun K. Gupta
    Pages 189-197
  9. Jie Chen, Arjun K. Gupta
    Pages 199-214
  10. Jie Chen, Arjun K. Gupta
    Pages 215-255
  11. Back Matter
    Pages 257-273

About this book


Overall, the book gives a clear and systematic presentation of models and methods. It will be an excellent source for theoretical and applied statisticians who are interested in research on change-point analysis and its applications to many areas.   —Mathematical Reviews (Review of the First Edition)

Revised and expanded, Parametric Statistical Change Point Analysis, Second Edition is an in-depth study of the change point problem from a general point of view, and a deeper look at change point analysis of the most commonly used statistical models. For some time, change point problems have appeared throughout the sciences in such disciplines as economics, medicine, psychology, signal processing, and geology; more recently, they have also been found extensively in applications related to biomedical imaging data, array Comparative Genomic Hybridization (aCGH) data, and gene expression data. These areas of interest—new and old—have motivated substantial research on change point problems and led to a significant body of literature in the field. The present monograph stands as a valuable contribution to this literature.

Key features and topics: 

* Clear and systematic exposition with a great deal of introductory material included; 

* Different models in each chapter, including gamma and exponential models, rarely examined thus far in the literature; 

* Extensive examples to emphasize key concepts and different methodologies used, namely the likelihood ratio criterion as well as the Bayesian and information criterion approaches; 

* An up-to-date comprehensive bibliography and two indices.

New to the Second Edition: 

* New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control; 

* Two new sections of applications of the underlying change point models in analyzing the array Comparative Genomic Hybridization (aCGH) data for DNA copy number changes; 

* A new chapter on change points in the hazard function; 

* A new chapter on other practical change point models, such as the epidemic change point model and a smooth-and-abrupt change point model.

This monograph will be a highly useful resource for an impressively broad range of researchers in statistics, as well as a useful supplement for graduate courses in the field.


Bayesian and information criterion approaches aCGH data change point models discrete models epidemic change point models gene expression data hazard function model likelihood ratio criterion multivariate normal model regression smooth-and-abrupt change point model univariate normal model

Authors and affiliations

  • Jie Chen
    • 1
  • Arjun K. Gupta
    • 2
  1. 1., Department of Mathematics and StatisticsUniversity of Missouri-Kansas CityKansas CityUSA
  2. 2., Department of Mathematics and StatisticsBowling Green State UniversityBowling GreenUSA

Bibliographic information