METHOD FOR ELIMINATING ANOMALOUS MEASUREMENTS IN ANALYSIS OF THE MULTI-DIMENSIONAL DATABASE IN SOLVING THE DECISION-MAKING PROBLEM

Abstract

The paper proposes a method for eliminating abnormal measurements (outliers) to improve the quality of multivariate data in statistical studies. Such a problem arises, for example, in the theory of managerial decision-making, since when calculating estimates of the parameters of probability distributions, the presence of anomalous (that is, those that significantly increase the confidence interval) measurements in the sample can distort the results of a statistical study, and, consequently, the main problem. The peculiarity of the proposed method is a combination of statistical and geometric methods, namely: the Gestwirt estimation method, the Tukey procedure, and a modification of the method for constructing the convex hull of a finite set of points in a multidimensional space. A set of multidimensional data is associated with a set of points of a multidimensional space. To find and eliminate outliers, a sequence of nested convex hulls, polytopes, is constructed, each of which is described by the intersection of half-spaces (support facets). A detailed algorithm for finding anomalous measurements is given. Their elimination corresponds to the successive elimination of the boundary points of nested convex hulls. The Gestwirt estimate gives the condition for stopping the operation of the algorithm. The proposed method does not require large computational costs and can be widely used in solving both theoretical and practical problems related to the processing of multidimensional data. The numerical results of the method with the number of data components 4 and 5 are presented.