Identification of linear regression models in the presence of errors in input and output data

Abstract

The problem of constructing linear regression models in the presence of errors in input and output data is considered. A statistical test for detecting measurement errors in input data is proposed that does not require a preliminary consistent estimation of the coefficients under the assumption of the presence of errors. The test is validated by Monte-Carlo statistical simulation.