Computational Statistics

, Volume 27, Issue 1, pp 103–125 | Cite as

A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches

  • Benoît Liquet
  • Jérôme SaraccoEmail author
Original Paper


Sliced inverse regression (SIR) and related methods were introduced in order to reduce the dimensionality of regression problems. In general semiparametric regression framework, these methods determine linear combinations of a set of explanatory variables X related to the response variable Y, without losing information on the conditional distribution of Y given X. They are based on a “slicing step” in the population and sample versions. They are sensitive to the choice of the number H of slices, and this is particularly true for SIR-II and SAVE methods. At the moment there are no theoretical results nor practical techniques which allows the user to choose an appropriate number of slices. In this paper, we propose an approach based on the quality of the estimation of the effective dimension reduction (EDR) space: the square trace correlation between the true EDR space and its estimate can be used as goodness of estimation. We introduce a naïve bootstrap estimation of the square trace correlation criterion to allow selection of an “optimal” number of slices. Moreover, this criterion can also simultaneously select the corresponding suitable dimension K (number of the linear combination of X). From a practical point of view, the choice of these two parameters H and K is essential. We propose a 3D-graphical tool, implemented in R, which can be useful to select the suitable couple (H, K). An R package named “edrGraphicalTools” has been developed. In this article, we focus on the SIR-I, SIR-II and SAVE methods. Moreover the proposed criterion can be use to determine which method seems to be efficient to recover the EDR space, that is the structure between Y and X. We indicate how the proposed criterion can be used in practice. A simulation study is performed to illustrate the behavior of this approach and the need for selecting properly the number H of slices and the dimension K. A short real-data example is also provided.


Bootstrap Dimension reduction Sliced inverse regression (SIR) Sliced average variance estimation (SAVE) Square trace correlation 


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.INSERM U897, ISPED, Université Victor Segalen Bordeaux 2Bordeaux CedexFrance
  2. 2.INRIA Bordeaux Sud-Ouest, CQFD TeamTalence CedexFrance
  3. 3.Institut de Mathématiques de Bordeaux, UMR CNRS 5251, Université Bordeaux 1Talence CedexFrance

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