Robustness of joint regression analysis

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)105-128
Number of pages24
JournalBiometrical Letters
Volume44
Issue number2
Publication statusPublished - 1 Jan 2007

Keywords

  • Joint Regressions Analysis
  • Robustness
  • Missing observations
  • Linear regressions
  • L2 environmental indexes

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