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Variable selection for functional density trees

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  • Shu-Fu Kuo
  • Yu-Shan Shih

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.

Suggested Citation

  • Shu-Fu Kuo & Yu-Shan Shih, 2012. "Variable selection for functional density trees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1387-1395, December.
  • Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1387-1395
    DOI: 10.1080/02664763.2011.649717
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    File URL: http://hdl.handle.net/10.1080/02664763.2011.649717
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    References listed on IDEAS

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    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. Nerini, David & Ghattas, Badih, 2007. "Classifying densities using functional regression trees: Applications in oceanology," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4984-4993, June.
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