The Analysis of Stochastic Properties of the SVD Decomposition at Approximation of the Experimental Data

The stochastic method is offered for the analysis of the SVD decomposition of experimental data matrices for complex non-Gaussian random variables generating independent variables of regression models (RM). To analyse the stability of RM desired solutions, the distortion of data matrix elements is simulated. There are considered therein both common-type classical matrices and binary normal matrices suggested by the author. Within the stochastic analysis of the distribution of singular values σi for matrices an algorithm is applied to identify the probable density functions σi on limited-scope samples. The research results are described and recommendations are given for the correct application of singular decomposition.