  In this paper the problem of retrospective change-point detection and
estimation in multivariate linear models is considered. The lower bounds for
the error of change-point estimation are proved in different cases (one
change-point: deterministic and stochastic predictors, multiple change-points).
A new method for retrospective change-point detection and estimation is
proposed and its main performance characteristics (type 1 and type 2 errors,
the error of estimation) are studied for dependent observations in situations
of deterministic and stochastic predictors and unknown change-points. We prove
that this method is asymptotically optimal by the order of convergence of
change-point estimates to their true values as the sample size tends to
infinity. Results of a simulation study of the main performance characteristics
of proposed method in comparison with other well known methods of retrospective
change-point detection and estimation are presented.
