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• W4384345338 abstract "The mean regression methodology may not be successful in developing precise mathematical models when working with data that has a significant number of outliers. The reason for this is that the outliers have a considerable impact on the mean value, making it untrustworthy for the intended usage. A different model termed Quantile Regression (QR) has become more popular as a solution to this problem. Quantile regression is more resistant to outliers and can handle data with a wide range of distributions. It has emerged as a promising approach for practical applications, offering a more comprehensive view than mean regression. In this paper, we delve deeper into this strategy, with a particular focus on its application in various fields, including the prediction of wind power as well as its usage in finance and economics research. Rather than just providing a technical explanation of the theory or a comprehensive analysis of recent developments, we begin by examining important applications to illustrate the usefulness of the approach. We then discuss these topics in further detail with a brief introduction to their mathematics. Finally, we provide an overview of current research topics that make substantial use of this method, later concluding why we need it." @default.
• W4384345338 created "2023-07-15" @default.
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• W4384345338 date "2023-05-26" @default.
• W4384345338 modified "2023-09-27" @default.
• W4384345338 title "Quantile Regression in Machine Learning: A Survey" @default.
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• W4384345338 doi "https://doi.org/10.1109/icsccc58608.2023.10176807" @default.
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