The explosive development of information science and technology puts in
new problems involving statistical data analysis. These problems result
from higher re- quirements concerning the reliability of statistical
decisions, the accuracy of math- ematical models and the quality of
control in complex systems. A new aspect of statistical analysis has
emerged, closely connected with one of the basic questions of
cynergetics: how to "compress" large volumes of experimental data in
order to extract the most valuable information from data observed. De-
tection of large "homogeneous" segments of data enables one to identify
"hidden" regularities in an object's behavior, to create mathematical
models for each seg- ment of homogeneity, to choose an appropriate
control, etc. Statistical methods dealing with the detection of changes
in the characteristics of random processes can be of great use in all
these problems. These methods have accompanied the rapid growth in data
beginning from the middle of our century. According to a tradition of
more than thirty years, we call this sphere of statistical analysis the
"theory of change-point detection. " During the last fifteen years, we
have witnessed many exciting developments in the theory of change-point
detection. New promising directions of research have emerged, and
traditional trends have flourished anew. Despite this, most of the
results are widely scattered in the literature and few monographs exist.
A real need has arisen for up-to-date books which present an account of
important current research trends, one of which is the theory of non
parametric change--point detection.
