Multivariate statistical analysis: a high-dimensional approach

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ dations except to ignore a part of the data. The probability of data degeneration increases with the dimension n, and for n > N, where N is the sample size, the sample covariance matrix has no inverse. Thus nearly all conventional linear methods of multivariate statis­ tics prove to be unreliable or even not applicable to high-dimensional data

Unimprovable solution to systems of empirical linear algebraic equations

An optimum solution, free from degeneration, is found for a system of linear algebraic equations with empirical coefficients and right-hand sides. The quadratic risk of estimators of the unknown solution vector is minimized over a class of linear systems with given square norm of the coefficient matrix and length of the vector on the right-hand side. Empirical coefficients and the right-hand sides are assumed to be independent and normal with known variance. It is found that the optimal estimator has the form of a regularized minimum square solution with an extension multiple. A simple formula is derived showing explicitly the dependence of the minimal risk on parameters.Empirical systems of equations Empirical linear models Systems of random linear algebraic equations

Normal model for distribution-free multivariate analysis

It is shown that standard quality functions of n-dimensional regularized linear multivariate procedures can be accurately estimated under the assumption that of populations normality if n is so large that it is comparable in magnitude with sample size. The correction terms are estimated from above and their upper estimates are obtained with accuracy up to absolute constants.Multivariate analysis Large dimension Normal model