Robustness of the Joint Regression Analysis
Joint Regression Analysis is shown to be extremely robust to missing observations. Thus, using a series of "α-designs" of winter rye cultivars, it was shown that with up to 40% of missing observations the cultivars to be selected would be the same. In this study we considered missing obser...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2008
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10174/1207 |
| País: | Portugal |
| Oai: | oai:dspace.uevora.pt:10174/1207 |
| Resumo: | Joint Regression Analysis is shown to be extremely robust to missing observations. Thus, using a series of "α-designs" of winter rye cultivars, it was shown that with up to 40% of missing observations the cultivars to be selected would be the same. In this study we considered missing observations incidences varying from 5% to 75% with 5% differences between them. For each incidence the positions of missing observations were randomly generated in triplicate. |
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