Computational Statistics

, Volume 18, Issue 3, pp 585–603 | Cite as

Two Cross Validation Criteria for SIRα and PSIRα methods in view of prediction

  • Ali GannounEmail author
  • Jérôme Saracco


In this paper, we will consider the semiparametric regression model introduced by Duan and Li (1991). The response variable y will be linked to an index x′β (i.e. a linear combination of the explanatory variables x) through an unknown function. In order to estimate the direction of the unknown slope parameter β, Slicing and Pooled Slicing methods have been developed (see Duan and Li (1991), Li (1991), Aragon and Saracco (1997), Saracco (2001)). All the methods are computationally simple and fast. Among these methods, we focus on SIRα and PSIRα. We propose two cross validation criteria to select the parameter α. The evaluation of these criteria requires the kernel smoothing estimation of the link function. The choice of α is illustrated with simulations.

Key words

Cross Validation Dimension Reduction Kernel Smoothing Pooled Slicing (PSIR) Semiparametric Regression model Sliced Inverse Regression (SIR) 


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Copyright information

© Physica-Verlag 2003

Authors and Affiliations

  1. 1.Laboratoire de Probabilités et Statistique, Département des Sciences MathématiquesCC 051 Université Montpellier IIMontpellier Cedex 5France

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