A new approach to finding relevant components in linear regression

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Abstract

This paper contains some remarks on the so-called “relevant subspaces” useful when data reduction, near-collinearity of prediction problems are to be dealt with. The presentation is mainly based on a geometrical point of view. The consequences of relevant subspaces on the most common linear regression methods are analysed and a new method is developed to extract relevant subspaces. An experiment based on simulations is proposed to verify if the forecasting ability of some regression methods is influenced by relevant components.
Lingua originaleEnglish
pagine (da-a)213-224
Numero di pagine12
RivistaJOURNAL OF THE ITALIAN STATISTICAL SOCIETY
Stato di pubblicazionePubblicato - 1999

Keywords

  • principal factor analysis
  • relevant components

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