Abstract
We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametric component and a profiling estimator of the parametric component of the model and derive their asymptotic properties. Specifically, we show the consistency of the nonparametric functional estimates and derive the asymptotic expansion of the estimates of the parametric component. We illustrate the performance of our methodology using a simulation study and a real data application.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1807-1814 |
| Number of pages | 8 |
| Journal | Statistics & Probability Letters |
| Volume | 82 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2012 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Maity’s work was partly supported by Award Number R00ES017744 from the National Institute of Environmental Health Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute Of Environmental Health Sciences or the National Institutes of Health. Huang’s work was partly supported by grants from NSF (DMS-09-07170 and DMS-10-07618) and King Abdullah University of Science and Technology (KUS-CI-016-04). We are also grateful to two anonymous reviewers for their careful evaluation of the paper and constructive comments that led to a significantly improved version of the paper.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.