Developing seed zones and transfer guidelines with multivariate regression trees

Multivariate Regression Trees

Permutation Testing in Multivariate Regression Trees

Formal testing procedures are generally not available for determining which splits in a regression tree are significant. Such a procedure is presented using permutation testing applied to order statistics. Finally, traditional cross validation and the permutation testing procedure are compared in a specific example.

Multivariate Regression with Calibration

We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...

Multivariate regression trees for analysis of abundance data.

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 sever...

Illuminant Estimation using Ensembles of Multivariate Regression Trees

White balancing is a fundamental step in the image processing pipeline. The process involves estimating the chromaticity of the illuminant or light source and using the estimate to correct the image to remove any color cast. Given the importance of the problem, there has been much previous work on illuminant estimation. Recently, an approach based on ensembles of univariate regression trees tha...