Correcting regression dilution problem using direct adjustment method

This article presents a direct adjustment correction method to multiple regression dilution problem in the context of uncorrelated, zero mean, additive measurement errors with finite standard deviations. The proposed method gives accurate adjusted regression coefficients and standard errors to allow correct statistical inferences and outperforms the SIMEX algorithm in various simulation studies. The direct adjustment correction method is also more precise in its regression coefficient estimates compared to existing methods such as product moment correlation coefficient, intraclass coefficient, Rosner's regression and maximum likelihood estimation.