Statistical Analysis of Observations of Increasing Dimension is
devoted to the investigation of the limit distribution of the empirical
generalized variance, covariance matrices, their eigenvalues and
solutions of the system of linear algebraic equations with random
coefficients, which are an important function of observations in
multidimensional statistical analysis. A general statistical analysis is
developed in which observed random vectors may not have density and
their components have an arbitrary dependence structure. The methods of
this theory have very important advantages in comparison with existing
methods of statistical processing. The results have applications in
nuclear and statistical physics, multivariate statistical analysis in
the theory of the stability of solutions of stochastic differential
equations, in control theory of linear stochastic systems, in linear
stochastic programming, in the theory of experiment planning.