Name
Abstract | ||
|---|---|---|
We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE. The proposed method shares some of the desired features of existing variable selection methods: the resulting estimator enjoys the oracle property; the proposed procedure avoids the convex optimization problem and is flexible and easy to implement. Moreover, we propose a penalized weighted deviance criterion for a data-driven choice of the tuning parameters. Simulation studies are carried out to assess the performance of SGEE, and a real dataset is analyzed for further illustration. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.csda.2012.12.015 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
variable selection method,data-driven choice,corresponding parameter,automatic variable selection procedure,convex optimization problem,generalized linear model,inactive predictor,longitudinal generalized linear model,proposed method share,proposed procedure,variable selection,generalized estimating equations | Econometrics,Mathematical optimization,Feature selection,Generalized linear array model,Generalized linear model,Statistics,Generalized linear mixed model,Convex optimization,Generalized estimating equation,Mathematics,Linear regression,Estimator | Journal |
Volume | Issue | ISSN |
61 | C | 0167-9473 |
Citations | PageRank | References |
7 | 0.91 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaorong Li | 1 | 64 | 14.58 |
| Heng Lian | 2 | 106 | 27.59 |
| Sanying Feng | 3 | 29 | 6.30 |
| Lixing Zhu | 4 | 116 | 34.41 |