Decimal-Integer-Coded Genetic Algorithm for Trimmed Estimator of the Multiple Linear Errors in Variables Model

  • Fuchang Wang
  • Huirong Cao
  • Xiaoshi Qian
Conference paper

DOI: 10.1007/978-3-642-25255-6_46

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7030)
Cite this paper as:
Wang F., Cao H., Qian X. (2011) Decimal-Integer-Coded Genetic Algorithm for Trimmed Estimator of the Multiple Linear Errors in Variables Model. In: Liu B., Chai C. (eds) Information Computing and Applications. ICICA 2011. Lecture Notes in Computer Science, vol 7030. Springer, Berlin, Heidelberg

Abstract

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.

Keywords

decimal-integer-coded genetic algorithm multiple linear error-in-variable model orthogonal least trimmed absolute deviation estimator robustness 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fuchang Wang
    • 1
  • Huirong Cao
    • 2
  • Xiaoshi Qian
    • 1
  1. 1.Department of Basic CoursesInstitute of Disaster Prevention of China Earthquake AdministrationSanheChina
  2. 2.College of Mathematics and Information ScienceLangfang Teachers CollegeLangfangChina

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