Variable selection for functional density trees

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.

Keywords:

• chi-squared tests
• contingency tables
• selection bias

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.