Abstract:
The classification of the natural and anthropogenic destabilizing factors of a telecommunications network as a complex system is presented herein. This research shows that to evaluate the
parameters of a telecommunications network in the presence of destabilizing factors, it is necessary
to modify classical linear methods to reduce their sensitivity to the incompleteness of a priori information. Using generalized linear models of multiple regression, a combined method was developed
for assessing and predicting the survivability of a telecommunications network under conditions of
uncertainty regarding the influence of destabilizing factors. The method consists of accumulating current information about the parameters and state of the network, the statistical analysis and processing
of information, and the extraction of sufficient sample statistics. The basis of the developed method
was balancing multiple correlation–regression analysis with the number of regression equations and
the observed results. Various methods of estimating the mathematical expectation and correlation
matrix of the observed results under the conditions of random loss of part of the observed data (for
example, removing incomplete sample elements, substituting the average, pairwise crossing out, and
substituting the regression) were analyzed. It was established that a shift in the obtained estimates
takes place under the conditions of a priori uncertainty of the statistics of the observed data. Given
these circumstances, recommendations are given for the correct removal of sample elements and
variables with missing values. It is shown that with significant unsteadiness of the parameters and
state of the network under study and a noticeable imbalance in the number of regression equations
and observed results, it is advisable to use stepwise regression methods.
