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A New Algorithm for the Least Absolute Deviation Regression Based on the Simulated Annealing Algorithm

Fuchang Wang

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Abstract

The least absolute deviation criteria is widely used in engineering because of its robustness, but the algorithms for solving the least absolute deviation estimate of the regression coefficient are too explicated or only efficient for small sample points and variables. In this paper, a new method based on simulated annealing algorithm to solve the least absolute deviation estimates of regression coefficient is presented by changing the problem to the combinatorial optimization based on it's properties. At last the numerical experimentations verified the validity of the new method.

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What this paper is about

The least absolute deviation criteria is widely used in engineering because of its robustness, but the algorithms for solving the least absolute deviation estimate of the regression coefficient are too explicated or only efficient for small sample points and variables. In this paper, a new method based on simulated annealing algorithm to solve the least absolute deviation estimates of regression coefficient is presented by changing the problem to the combinatorial optimization based on it's properties. At last the numerical experimentations verified the validity of the new method.

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Available abstract

The least absolute deviation criteria is widely used in engineering because of its robustness, but the algorithms for solving the least absolute deviation estimate of the regression coefficient are too explicated or only efficient for small sample points and variables. In this paper, a new method based on simulated annealing algorithm to solve the least absolute deviation estimates of regression coefficient is presented by changing the problem to the combinatorial optimization based on it's properties. At last the numerical experimentations verified the validity of the new method.

Key concepts: Least absolute deviations, Absolute deviation, Simulated annealing, Algorithm, Standard deviation, Robustness (evolution), Relative standard deviation, Mean absolute error

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