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Quantile Regression
Abstract
Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight.
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Montoro, D. T., Haber, A. L., Biton, M., Vinarsky, V., Lin, B., Birket, S. E., Yuan, F., Chen, S., Leung, H. M., Villoria, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0393-7 · Cited by 1,206
Economic and social consequences of human mobility restrictions under COVID-19
Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A., Porcelli, F., Schmidt, A. L., Valensise, C. M., Scala, A., Quattrociocchi, W., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007658117 · Cited by 1,004
Deep Reinforcement Learning: A Survey
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis
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Metadata
- Authors
- Koenker, R., & Hallock, K. F.
- Venue
- Journal of Economic Perspectives
- Publisher
- American Economic Association
- Published
- 2001-11-01
- Volume
- 15
- Issue
- 4
- Pages
- 143-156
- ISSN
- 0895-3309 (ISSN-L)
- OpenAlex
- W4241996101
- Cited by
- 4,413
- References
- 1
- FWCI
- 15.97
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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