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Title: Robustness of the Joint Regression Analysis
Authors: Pereira, Dulce
Keywords: Joint Regressions Analysis
Missing observations
Linear regressions
L2 environmental indexes
Issue Date: 2007
Abstract: 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.
ISSN: 1896-3811
Type: article
Appears in Collections:CIMA - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
MAT - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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