Advertisement

Tree-Growing for the Multivariate Model: The RECPAM Approach

  • Antonio Ciampi
  • Lisa Hendricks
  • Zhiyi Lou

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.

Keywords

Regression Tree Nuisance Parameter Multivariate Normal Distribution RECursive Partition Admissibility Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Sonquist, J.A. and Morgan, J.N. (1964). The detection of interaction effects. Ann Arbor: Institute for Social Research, University of Michigan.Google Scholar
  2. [2]
    Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984). Classification and Regression Trees. The Waldsworth International Group, Belmont, California.Google Scholar
  3. [3]
    Ciampi, A., Chang, C.H., Hogg, S.A. and McKinney, S. (1987). Recursive Partition: A versatile method for exploratory data analysis in Biostatistics. Joshi Feistschrift, Vol. 5, Biostatistics, I.B. Mac Neil and G.J. Umphrey (eds), 23-50.Google Scholar
  4. [4]
    Ciampi, A. Thiffault, J. (1989). Pruning regression trees for survival data: the RECPAM approach. Communications in Statistics, Theory and Methods 18(9): 3373–3388.CrossRefGoogle Scholar
  5. [5]
    Ciampi, A. (1991). Generalized regression trees. Computational Statistics & Data Analysis, 12:57–78.CrossRefGoogle Scholar
  6. [6]
    Ciampi, A., Zhiyi Lou, Qian Lin and A. Negassa (1991) Recursive Partition and Amalgamation with the exponential family: theory and applications. Applied Stochastic Processes and Data Analysis, 7:121–137.Google Scholar
  7. [7]
    Ciampi, A., du Berger, R., Taylor, G. and Thiffault, J. (1991). Treestructured multi-variate regression. Symbolic-numeric data analysis and learning, New York, Nova Science Publ. Inc., 263-270Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Antonio Ciampi
    • 1
  • Lisa Hendricks
    • 1
  • Zhiyi Lou
    • 1
  1. 1.Department of Epidemiology and BiostatisticsMcGill UniversityMontrealCanada

Personalised recommendations