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Estimation and testing for partially linear single-index models,” Annals of statistics (2010)
| Content Provider | CiteSeerX |
|---|---|
| Author | Liang, Hua Liu, Xiang Li, Runze Tsai, Chih-Ling |
| Abstract | In partially linear single-index models, we obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. We also employ the smoothly clipped absolute deviation penalty (SCAD) approach to simultaneously select variables and estimate regression coefficients. We show that the resulting SCAD estimators are consistent and possess the ora-cle property. Subsequently, we demonstrate that a proposed tuning parameter selector, BIC, identifies the true model consistently. Finally, we develop a lin-ear hypothesis test for the parametric coefficients and a goodness-of-fit test for the nonparametric component, respectively. Monte Carlo studies are also presented. 1. Introduction. Regression |
| File Format | |
| Publisher Date | 2010-01-01 |
| Access Restriction | Open |
| Content Type | Text |
