Порівняльний аналіз методів пошуку кращої структури лінійної регресії

Abstract

Most computationally critical part of linear regression algorithms is selection of structures that should be checked. One way to reduce the number of such structures is to make some primary estimations of structures quality and divide the structures into groups according to this estimation. One technique that uses this approach is La Motte-Hocking algorithm. This paper represents research results of La Motte-Hocking method efficiency in comparison with the combinatorial algorithm. Results of research and some possible ways to improve results are presented in this paper. The outcomes are planned to be used for updating the combinatorial GMDH algorithm.