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Quantile Regression
Abstract
Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
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Cited by
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Hutchins, B. I., Yuan, X., Anderson, J. M., & Santangelo, G. M. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002541 · Cited by 500
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382
Do Highly Paid, Highly Skilled Women Experience the Largest Motherhood Penalty?
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Metadata
- Authors
- Koenker, R.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2005
- OpenAlex
- W2096904991
- Cited by
- 4,551
- References
- 0
- FWCI
- 15.01
- Type
- book
- Language
- en
- Open access
- closed
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