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Tree-Growing for the Multivariate Model: The RECPAM Approach

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Computational Statistics

Abstract

The RECPAM approach is applied to the multivariate normal model. Tree-structured predictors are built for the multivariate mean, the correlation matrix and regression coefficients representing a treatment-outcome relationship. Several cases of special interest are discussed.

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References

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Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, McGill University, Montreal, P.Q., Canada

    Antonio Ciampi, Lisa Hendricks & Zhiyi Lou

Authors
  1. Antonio Ciampi
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  2. Lisa Hendricks
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  3. Zhiyi Lou
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Editor information

Editors and Affiliations

  1. Groupe de Statistique, University of Neuchâtel, Pierre-à-Mazel 7, CH-2000, Neuchâtel, Switzerland

    Yadolah Dodge (Professor of Statistics and Operations Research) (Professor of Statistics and Operations Research)

  2. Lancaster University, GB-Lancaster, LA1 4YE, Great Britain

    Joe Whittaker (Professor of Mathematics) (Professor of Mathematics)

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© 1992 Springer-Verlag Berlin Heidelberg

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Ciampi, A., Hendricks, L., Lou, Z. (1992). Tree-Growing for the Multivariate Model: The RECPAM Approach. In: Dodge, Y., Whittaker, J. (eds) Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-26811-7_19

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  • DOI: https://doi.org/10.1007/978-3-662-26811-7_19

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-26813-1

  • Online ISBN: 978-3-662-26811-7

  • eBook Packages: Springer Book Archive

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