MULTIVARIATE REGRESSION TREES: A NEW TECHNIQUE FOR MODELING SPECIES-ENVIRONMENT RELATIONSHIPS
By G. D. ath. 2002
259 citations recorded by the source corpus.
Subfields: Ecological Modeling, Ecology, Genetics
View Paper: https://www.semanticscholar.org/paper/146b8f9ef6df7e36394a78c736d1db0bcc69e534
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Citations in the corpus
- Forecasting precious metal returns with multivariate random forests (2018, unknown): “The specific multivariate modeling framework that we use is known in the literature as multivariate random forests (on multivariate trees, see Segal 1992; De’ath 2002; on multivariate random forests, see Segal and Xiao 2011; Behrens et al. 2018).”
- A new method for detecting and interpreting biodiversity and ecological community thresholds (2010, methodology): “In both nCPA and MRT, optimal partitioning is obtained by maximizing a deviance reduction statistic that compares within-group vs. between-group dissimilarity determined by a userselected ecological distance metric.”
- Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “Alternative approaches include Multivariate Adaptive Regression Splines (MARS, Friedman, 1991), 395 Artificial Neural Networks (ANN, Maravelias et al., 2003, Joy and Death, 2004, Wieland and JarreTeichmann, 1997), regression trees (De'ath and Fabricius, 2000, De'ath, 2002, Sutton et al., 2005, Strat”
- A review on longitudinal data analysis with random forest (2022, unknown): “Ahlem Hajjem) F R No [19] RE-EM R package REEMtree (CRAN) T R No [42, 47] (S)MERT, (S)MERF R package LongituRF (CRAN) T, F R No [7, 8] (S)REEMforest GMERT R code (supplement to original paper) T C No [20] BiMM forest R code (supplement to original paper) F C No [51, 52] Multivariate RF R package Mul”
- Regression trees for longitudinal and multiresponse data (2012, methodology): “De’ath (2002) avoids the covariance computations by following the CART algorithm exactly except for two simple modifications: the sample mean is replaced by the d-dimensional sample mean and the node impurity is replaced by i(t) = ∑d k=1 ik(t), where ik(t) is the sum of squared deviations about the…”
- Models and indicators for assessing conservation and fisheries-related effects of marine protected areas (2008, unknown): “Edgar et al. (2004) and Kendall et al. (2004) provide recent examples of baseline assessment before MPA establishment or enlargement.”
- Developing seed zones and transfer guidelines with multivariate regression trees (2011, methodology): “Homogeneity is evaluated as 169 sums of squares of traits around the multivariate mean of observations in a cluster 170 (De'Ath 2002).”
- Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage (2008, methodology): “These two analytical strands converge in a third routine, a counterpart to the BEST procedure of matching environmental information to species patterns, which adapts the Multivariate Regression Trees of De'ath (2002) to the non-parametric framework in PRIMER.”
- Microhabitat associations of vascular epiphytes in a wet tropical forest canopy (2015, methodology): “MRT is a multivariate discrimination technique that forms clusters of sites based on environmental variables that minimize the dissimilarity of groups within clusters (De’Ath 2002).”