How Many Variables Should Be Entered in a Regression Equation?
The optimal number of regressors is determined to minimize mean squared prediction error and is shown to be a small fraction of the number of data points. As the number of regressors grows large, the S p criterion provides an asymptotically optimal rule for the number of variables to enter.
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| Veröffentlicht in: | Journal of the American Statistical Association 1983-03, Vol.78 (381), p.131-136 |
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| container_end_page | 136 |
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| container_issue | 381 |
| container_start_page | 131 |
| container_title | Journal of the American Statistical Association |
| container_volume | 78 |
| creator | Breiman, L. Freedman, D. |
| description | The optimal number of regressors is determined to minimize mean squared prediction error and is shown to be a small fraction of the number of data points. As the number of regressors grows large, the S
p
criterion provides an asymptotically optimal rule for the number of variables to enter. |
| doi_str_mv | 10.1080/01621459.1983.10477941 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0162-1459 |
| ispartof | Journal of the American Statistical Association, 1983-03, Vol.78 (381), p.131-136 |
| issn | 0162-1459 1537-274X |
| language | eng |
| recordid | cdi_jstor_primary_10_2307_2287119 |
| source | Periodicals Index Online; JSTOR Mathematics & Statistics; Jstor Complete Legacy |
| subjects | Best subsets regression Error rates Linear regression Mathematical vectors Matrices Prediction error Probabilities Random errors Regression Statistical theories Statistical variance Statistics Stepwise regression Theory and Methods |
| title | How Many Variables Should Be Entered in a Regression Equation? |
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