Sensitivity analysis in linear regression
Sensitivity analysis in linear regression
Practical genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The fuzzy approach to statistical analysis
Computational Statistics & Data Analysis
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Bias in regression estimates resulting from the omission of a correlated relevant variable is a well-known phenomenon. In this study, we apply a genetic algorithm to estimate the missing variable and, using that estimated variable, demonstrate that significant bias in regression estimates can be substantially corrected with relatively high confidence in effective models. Our interest is restricted to the case of a missing binary indicator variable and the analytical properties of bias and MSE dominance of the resulting dependent error generated vector process. These findings are compared to prior results for the independent error proxy process. Simulations are run for medium sample sizes and the method is shown to produce substantial reduction in estimation bias and often renders useful estimates of the missing vector. Limited simulations for the continuous variable case are reported and indicate some potential for the method and future research.