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Quantile regression for longitudinal data
Open accessAbstract
The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of “fixed effects”. The introduction of a large number of individual fixed effects can significantly inflate the variability of estimates of other covariate effects. Regularization, or shrinkage of these individual effects toward a common value can help to modify this inflation effect. A general approach to estimating quantile regression models for longitudinal data is proposed employing ℓ1 regularization methods. Sparse linear algebra and interior point methods for solving large linear programs are essential computational tools.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Cited by
Nguyen, T. T., Pham, T. A. T., & Tram, H. T. X. (2020) · Journal of Environmental Management · doi:10.1016/j.jenvman.2020.110162 · Cited by 494
Cheng, C., Ren, X., Wang, Z., & Yan, C. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.02.063 · Cited by 426
Awan, A., Abbasi, K. R., Rej, S., Bandyopadhyay, A., & Lv, K. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.03.017 · Cited by 284
Doğan, B., Saboori, B., & Can, M. (2019) · Environmental Science and Pollution Research · doi:10.1007/s11356-019-06333-1 · Cited by 268
Wage Dispersion and Decentralization of Wage Bargaining
Dahl, C. M., le Maire, D., & Munch, J. R. (2013) · Journal of Labor Economics · doi:10.1086/669339 · Cited by 126
Metadata
- Authors
- Koenker, R.
- Venue
- Journal of Multivariate Analysis
- Publisher
- Elsevier BV
- Published
- 2004-10
- Volume
- 91
- Issue
- 1
- Pages
- 74-89
- ISSN
- 0047-259X (ISSN-L)
- OpenAlex
- W1981463702
- Cited by
- 2,177
- References
- 2
- FWCI
- 6.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
