S. Noskov

DIMENSIONAL REDUCTION OF LINEAR REGRESSION TAKING INTO ACCOUNT A GIVEN LEVEL OF APPROXIMATION ERRORS

Background. Quite often, when constructing regression models for real complex objects, it becomes necessary to reduce their dimension in order to eliminate the multicollinearity of variables or exclude statistically insignificant factors. Usually, the main component methods are used, as well as various step procedures...