Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel method to consistently estimate, at the root-n rate, the coefficient parameters in a biased partial linear single-index model whose error term does not have zero conditional expectation. To achieve this purpose, we first transfer the model to a pro forma linear model and then introduce an artificial variable into a linear bias correction model. Based on the bias correction model, the parameters can then be consistently estimated by the linear least squares method. Both numerical studies and real data analyses are conducted to show the effectiveness of the proposed estimation procedure. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.csda.2019.03.006 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
Partial linear single index model,Artificial variable construction,Bias-corrected model,Estimation consistency | Linear model,Conditional expectation,Bias correction,Statistics,Mathematics,Linear least squares method | Journal |
Volume | ISSN | Citations |
139 | 0167-9473 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jun lu | 1 | 9 | 5.04 |
| Xuehu Zhu | 2 | 3 | 2.28 |
| Lu Lin | 3 | 27 | 8.56 |
| Lixing Zhu | 4 | 116 | 34.41 |