Robust regression and outlier detection
Robust regression and outlier detection
Genetic algorithms and their statistical applications: an introduction
Computational Statistics & Data Analysis
Robust weighted orthogonal regression in the errors-in-variables model
Journal of Multivariate Analysis
Computing LTS Regression for Large Data Sets
Data Mining and Knowledge Discovery
An evolutionary algorithm for robust regression
Computational Statistics & Data Analysis
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The multiple linear errors-in-variables model is frequently used in science and engineering for model fitting tasks. When sample data is contaminated by outliers, the orthogonal least squares estimator isn't robust. To obtain robust estimators, orthogonal least trimmed absolute deviation (OLTAD) estimator based on the subset of h cases(out of n) is proposed. However, the OLTAD estimator is NP-hard to compute. So, an new decimal-decimal-integer-coded genetic algorithm(DICGA) for OLTAD estimator is presented. We show that the OLTAD estimator has the high breakdown point and appropriate properties. Computational experiments of the OLTAD estimator of multiple linear EIV model on synthetic data is provided and the results indicate that the DICGA performs well in identifying groups of high leverage outliers in reasonable computational time and can obtain smaller objective function fitness.