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Interval quantile regression models based on swarm intelligence
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DOI：10.1016/j.asoc.2018.04.061.png)
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This paper presents quantile regression models for classical data and interval symbolic data using algorithms based on swarm intelligence to estimate the parameters aiming to improve the model performance. Also, these methods are compared with methods of estimation based on linear programming widely used in the literature. Applications using real and simulated data are considered. The prediction quality is assessed by the mean magnitude of relative error calculated from test data. The growth of symbolic data nature of alerts to the need of the new statistical methodologies development for the treatment of this type of information. The results show that the proposed models are effective alternatives for optimizing the choice of parameters of the quantile regression, providing greater precision and robustness than models based on linear programming. (C) 2018 Published by Elsevier B.V.
Keyword:
Quantile regression
Swarm intelligence
Symbolic data analysis
Particle swarm optimization
Artificial bee colony algorithm
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6.6
论文数:
1.4W
被引数:
4.8W
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