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Multivariate Regression Trees for Analysis of Abundance Data
Author(s) -
Larsen David R.,
Speckman Paul L.
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00202.x
Subject(s) - multivariate statistics , statistics , regression analysis , multivariate analysis , regression , abundance (ecology) , cluster (spacecraft) , multivariate adaptive regression splines , tree (set theory) , set (abstract data type) , homogeneous , variables , cross sectional regression , mathematics , econometrics , bayesian multivariate linear regression , computer science , ecology , biology , mathematical analysis , programming language , combinatorics
Summary . Multivariate regression tree methodology is developed and illustrated in a study predicting the abundance of several cooccurring plant species in Missouri Ozark forests. The technique is a variation of the approach of Segal (1992) for longitudinal data. It has the potential to be applied to many different types of problems in which analysts want to predict the simultaneous cooccurrence of several dependent variables. Multivariate regression trees can also be used as an alternative to cluster analysis in situations where clusters are defined by a set of independent variables and the researcher wants clusters as homogeneous as possible with respect to a group of dependent variables.