Name
Abstract | ||
|---|---|---|
This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate. The authors show that under some appropriate conditions, the SCAD-penalized least squares estimator has the so called "oracle property". In addition, the authors also suggest a BIC criterion to select the tuning parameter, and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models. Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11424-014-2276-9 | J. Systems Science & Complexity |
Keywords | Field | DocType |
BIC,covariate adjusted regression model,oracle property,variable selection | Least squares,Covariate,Observable,Multiplicative function,Feature selection,Regression analysis,Oracle,Statistics,Mathematics,Linear regression | Journal |
Volume | Issue | ISSN |
27 | 6 | 1009-6124 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuejing Li | 1 | 0 | 0.34 |
| Jiang Du | 2 | 0 | 0.68 |
| Gaorong Li | 3 | 64 | 14.58 |
| Mingzhi Fan | 4 | 0 | 0.34 |