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On Estimation of Partially Linear Transformation Models.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lu, Wenbin Zhang, Hao Helen |
| Copyright Year | 2010 |
| Abstract | We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well. We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative algorithm is developed. Numerical examples are given to illustrate the finite-sample performance of the procedure. |
| Starting Page | 683 |
| Ending Page | 691 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www4.stat.ncsu.edu/~lu/research/jasa_plt_rev4.pdf |
| Alternate Webpage(s) | http://www4.stat.ncsu.edu/~hzhang/paper/transemi.pdf |
| Alternate Webpage(s) | http://www4.stat.ncsu.edu/~hzhang2/paper/transemi.pdf |
| PubMed reference number | 20802823v1 |
| Volume Number | 105 |
| Issue Number | 490 |
| Journal | Journal of the American Statistical Association |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Apricot kernel oil Estimated Population Parameter Sample Variance algorithm |
| Content Type | Text |
| Resource Type | Article |
