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Quantile regression for longitudinal data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Koenker, Roger W. |
| Copyright Year | 2004 |
| Abstract | 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 l 1 regularization methods. Sparse linear algebra and interior point methods for solving large linear programs are essential computational tools. |
| Starting Page | 74 |
| Ending Page | 89 |
| Page Count | 16 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.jmva.2004.05.006 |
| Volume Number | 91 |
| Alternate Webpage(s) | http://www.econ.uiuc.edu/~roger/courses/Copenhagen/lectures/L7.pdf |
| Alternate Webpage(s) | http://www.econ.uiuc.edu/~roger/research/panel/long.pdf |
| Alternate Webpage(s) | http://www.econ.uiuc.edu/~roger/courses/LSE/lectures/L7.pdf |
| Alternate Webpage(s) | https://doi.org/10.1016/j.jmva.2004.05.006 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
