Abstract
In this paper, the exhaustive search principle used in functional trees for classifying densities is shown to select variables with more split points. A new variable selection scheme is proposed to correct this bias. The Pearson chi-squared tests for associated two-way contingency tables are used to select the variables. Through simulation, we show that the new method can control bias and is more powerful in selecting split variable.
Acknowledgements
The authors are grateful to the reviewer for the valuable comments and suggestions. This research was supported in part by a grant from NSC, Taiwan.