Induced OWA operators in linear regression

Abstract

The induced ordered weighted average (IOWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This work presents a new application that uses the simple linear regression (LR) and the IOWA operator in the same formulation. We study some of its main properties and particular cases. The main advantage of the linear regression IOWA operator is that it unifies the IOWA operator with the linear regression in the same formulation considering the degree of optimism and pessimism of the decision maker. Thus, we can under- or overestimate the regression according to complex attitudes that the decision may have in the analysis. The work ends analyzing the applicability of this new approach in a problem regarding exchange rate forecasting. The objective of the new approach is to analyze the information in a more complete way.