Change-point problems arise in a variety of experimental and
mathematical sciences, as well as in engineering and health
sciences. This rigorously researched text provides a comprehensive
review of recent probabilistic methods for detecting various types
of possible changes in the distribution of chronologically ordered
observations. Further developing the already well-established
theory of weighted approximations and weak convergence, the authors
provide a thorough survey of parametric and non-parametric methods,
regression and time series models together with sequential methods.
All but the most basic models are carefully developed with detailed
proofs, and illustrated by using a number of data sets. Contains a
thorough survey of:
- The Likelihood Approach
- Non-Parametric Methods
- Linear Models
- Dependent Observations
This book is undoubtedly of interest to all probabilists and
statisticians, experimental and health scientists, engineers, and
essential for those working on quality control and surveillance
problems. Foreword by David Kendall
General
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