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Nonparametric Estimation of Additive Separable Regression Models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chen, Richel Härdle, Wolfgang K. Linton, Oliver Severance-Lossin, Eric K. |
| Copyright Year | 1996 |
| Abstract | Additive regression models have been shown to be useful in many situations. Numerical estimation of these models is usually done using the iterative back-fitting technique. This paper proposes an estimator for additive models with an explicit ‘hat matrix’ which does not use iteration. The asymptotic normality of the estimator is proved. We also investigate a variable selection procedure using the proposed estimator and prove that asymptotically the procedure finds the correct variable set with probability 1. A simulation study is presented investigating the practical performance of the procedure. |
| Starting Page | 247 |
| Ending Page | 265 |
| Page Count | 19 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/978-3-642-48425-4_18 |
| Alternate Webpage(s) | https://doi.org/10.1007/978-3-642-48425-4_18 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
