Arbeitspapier

Variables selection in observational and experimental studies

This paper discusses whether differences in the data structure of observational and experimental studies should lead to different strategies for variable selection. On the one hand, it is argued that outliers in the predictor variables have to be treated differently in the two kinds of studies. In experimental studies this results in philosophical problems with the applicability of cross validation. On the other hand, it is shown, however, that a well designed experiment might lead to a factor structure very appropriate for cross validation, namely a certain balance in the observations together with orthogonality of the factors. This might be the reason why in practice cross validation has proven to be a valuable tool for variable selection also in experimental studies. In contrast, however, it is shown that variables selection based on cross validation is not appropriate for saturated orthogonal designs. After this fundamental argumentation, we illustrate by a number of examples that the same methods for variable selection can be successfully applied in observational as well as experimental studies.

Sprache
Englisch

Erschienen in
Series: Technical Report ; No. 2000,22

Thema
variables selection
stepwise regression
cross validation
principal components
screening
optimization

Ereignis
Geistige Schöpfung
(wer)
Kunert, Joachim
Weihs, Claus
Ereignis
Veröffentlichung
(wer)
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
(wo)
Dortmund
(wann)
2000

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Kunert, Joachim
  • Weihs, Claus
  • Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen

Entstanden

  • 2000

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