CONSTRUCTING A DATA-DRIVEN PIECEWISE LINEAR REGRESSION WITH INTERVAL UNCERTAINTY FOR THE DEPENDENT VARIABLE

Abstract

The article discusses a problem of constructing a data-driven piecewise linear regression model (also known as Leontief production function, zero elasticity of substitution production function, and fixed proportions production function) with interval uncertainty for the dependent variable. A brief review of application of traditional forms of such models constructed according to the classical point data is given for assessing air quality, analyzing public health’s relation to the agricultural activity, optimizing processes of antibodies’ fragments purification, studying airport capacity, and solving other problems. A sum of approximation errors mode is taken as a loss function. The formulated problem is reduced to the partially Boolean programming problem of acceptable dimension. There should not emerge any calculating difficul-ties when solving the problem due to the existing large amount of acceptable effective software tools. The results of the study can be applied in research using methods of mathematical simulation of complicated technical and socially economic objects with interval uncertainty in the initial data caused by failures in the operation of measuring devices, errors in the activities of statistical services and other reasons.