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Estimation and Variable Selection in Additive Nonparametric Regression Models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Härdle, Wolfgang K. Chen, Richel |
| Copyright Year | 1995 |
| Abstract | Additive regression models have been shown to be useful in many situations. Numerical estimation of these models is usually done using the back-tting technique. This iterative numerical procedure converges very fast but has the disadvantage of a complicated`hat matrix.' This paper proposes an estimator with an explicit`hat matrix' which does not use backktting. 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 nds the correct variable set with probability 1. A simulation study is presented investigating the practical performance of the procedure. |
| File Format | PDF HTM / HTML |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
