國家衛生研究院 NHRI:Item 3990099045/6484
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    题名: Variable selection for functional density trees
    作者: Kuo, SF;Shih, YS
    贡献者: Division of Biostatistics and Bioinformatics
    摘要: 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.
    日期: 2012-01
    關聯: Journal of Applied Statistics. 2012 Jan;39(7):1387-1395.
    Link to: http://dx.doi.org/10.1080/02664763.2011.649717
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0266-4763&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000304484800001
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84861864582
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