Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors
In this paper we consider a nonparametric regression model that admits a mix of continuous and discrete regressors, some of which may in fact be redundant (that is, irrelevant). We show that, asymptotically, a data-driven least squares cross-validation method can remove irrelevant regressors. Simula...
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| Veröffentlicht in: | The review of economics and statistics 2007-11, Vol.89 (4), p.784-789 |
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| container_end_page | 789 |
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| container_issue | 4 |
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| container_title | The review of economics and statistics |
| container_volume | 89 |
| creator | Hall, Peter Li, Qi Racine, Jeffrey S. |
| description | In this paper we consider a nonparametric regression model that admits a mix of continuous and discrete regressors, some of which may in fact be redundant (that is, irrelevant). We show that, asymptotically, a data-driven least squares cross-validation method can remove irrelevant regressors. Simulations reveal that this "automatic dimensionality reduction" feature is very effective in finite-sample settings. |
| doi_str_mv | 10.1162/rest.89.4.784 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0034-6535 |
| ispartof | The review of economics and statistics, 2007-11, Vol.89 (4), p.784-789 |
| issn | 0034-6535 1530-9142 |
| language | eng |
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| source | Business Source Complete; JSTOR Mathematics & Statistics; Jstor Complete Legacy; MIT Press Journals |
| subjects | Economic theory Kernel functions Markovs inequality Nonparametric models Parametric models Regression analysis Signal bandwidth |
| title | Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors |
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