Fast Regression Estimates of Missing Data
A recent comparison of methods for estimating missing data concluded that when there is sufficient redundancy to justify using a more elaborate method than the mean of each variable, the principal components and regression methods are equally good and superior to the other methods investigated. Prin...
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| Veröffentlicht in: | Psychometrika 1976-06, Vol.41 (2), p.277-277 |
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| Format: | Artikel |
| Sprache: | eng |
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| container_end_page | 277 |
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| container_issue | 2 |
| container_start_page | 277 |
| container_title | Psychometrika |
| container_volume | 41 |
| creator | Koopman, Raymond F. |
| description | A recent comparison of methods for estimating missing data concluded that when there is sufficient redundancy to justify using a more elaborate method than the mean of each variable, the principal components and regression methods are equally good and superior to the other methods investigated. Principal components was preferred because of its “tremendous computational savings over the regression method.” This note proposes an alternate implementation of the regression method which should be slightly faster than the principal components method. |
| doi_str_mv | 10.1007/BF02291846 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0033-3123 |
| ispartof | Psychometrika, 1976-06, Vol.41 (2), p.277-277 |
| issn | 0033-3123 1860-0980 |
| language | eng |
| recordid | cdi_crossref_primary_10_1007_BF02291846 |
| source | Springer Nature - Complete Springer Journals |
| title | Fast Regression Estimates of Missing Data |
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