Reducing errors-in-variables bias in linear regression using compact genetic algorithms

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Authors M. Hakan Satman, Erkin Diyarbakirlioglu
Journal/Conference Name Journal of Statistical Computation and Simulation
Paper Category
Paper Abstract A new technique is devised to mitigate the errors-in-variables bias in linear regression. The procedure mimics a 2-stage least squares procedure where an auxiliary regression which generates a better behaved predictor variable is derived. The generated variable is then used as a substitute for the error-prone variable in the first-stage model. The performance of the algorithm is tested by simulation and regression analyses. Simulations suggest the algorithm efficiently captures the additive error term used to contaminate the artificial variables. Regressions provide further credit to the simulations as they clearly show that the compact genetic algorithm-based estimate of the true but unobserved regressor yields considerably better results. These conclusions are robust across different sample sizes and different variance structures imposed on both the measurement error and regression disturbances.
Date of publication 2015
Code Programming Language R

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