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.
Date:
2012-01
Relation:
Journal of Applied Statistics. 2012 Jan;39(7):1387-1395.
