The OWA operator in multiple linear regression

Multiple linear regression (MLR) is one of the most widely used statistical procedures for scholarly and research. The main limitation of MLR is that when being estimated with linear methodologies as ordinary least squares (OLS) becomes not functional with complex data. The ordered weighted average (OWA) is an aggregation operator that provides means that collect complex information. This work presents a new application that uses MLR and OWA operators in the same formulation. We developed two applications called MLR-OWA operator and MLR-GOWA operator. The main advantage of the MLR with OWA operators is that we can consider the degree of optimism and pessimism of the environment. We study some of its main properties and particular cases. Finally, an application is tested for a volatility exchange rate estimation problem.