Simultaneous Variable Selection

نویسندگان

  • Berwin A. Turlach
  • William N. Venables
  • Stephen J. Wright
چکیده

We propose a new method for selecting a common subset of explanatory variables where the aim is to explain or predict several response variables. The basic idea is a natural extension of the LASSO technique proposed by Tibshirani (1996) based on minimising the (joint) residual sum of squares while constraining the parameter estimates to lie within a suitable polyhedral region. This leads to a convex programming problem for which we develop an efficient interior point algorithm. The method is illustrated on a data set with infra-red spectrometry measurements on 14 qualitatively different but correlated responses using 770 wavelengths. The aim is to select a subset of the wavelengths suitable to use as predictors for as many as possible of the responses.

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عنوان ژورنال:
  • Technometrics

دوره 47  شماره 

صفحات  -

تاریخ انتشار 2005