Name
Abstract | ||
|---|---|---|
In this article, we consider variable selection in robust regression models for longitudinal data. We propose a penalized robust estimating equation to estimate the regression parameters and to select the important covariate variables simultaneously. Under some regularity conditions, we show the oracle properties of the proposed robust variable selection methods. A simulation study shows the robustness of the proposed methods against outliers. Moreover, it is found by the simulation study that incorporating the correlation structure into the procedure of variable selection will lead to better performance than ignoring the correlation structure for longitudinal data. In the end, the proposed methods are illustrated in the analysis of a real data set. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.jmva.2012.03.007 | J. Multivariate Analysis |
Keywords | Field | DocType |
robust regression model,simulation study,correlation structure,penalized robust estimating equation,important covariate variable,longitudinal data,proposed robust variable selection,variable selection,estimating equation,robust estimator,robust regression | Econometrics,Covariate,Feature selection,Regression,Oracle,Outlier,Robustness (computer science),Robust regression,Statistics,Mathematics,Estimating equations | Journal |
Volume | ISSN | Citations |
109, | 0047-259X | 6 |
PageRank | References | Authors |
0.87 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yali Fan | 1 | 6 | 0.87 |
| Guoyou Qin | 2 | 12 | 3.82 |
| Zhong Yi Zhu | 3 | 34 | 10.77 |