國家衛生研究院 NHRI:Item 3990099045/6544
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/6544


    Title: An optimal k-nearest neighbor for density estimation
    Authors: Kung, YH;Lin, PS;Kao, CH
    Contributors: Division of Biostatistics and Bioinformatics
    Abstract: A k-nearest neighbor method, which has been widely applied in machine learning, is a useful tool to obtain statistical inference for an underlying distribution of multi-dimensional data. However, the knowledge on choosing an optimal order for the k-nearest neighbor is relatively little. This paper proposes an asymptotic distribution for the nearest neighbor statistic. Under some conditions, we find an optimal unbiased density estimate based on a linear combination of nearest neighbors, and it leads to an optimal choice for the order of the k-nearest neighbor.
    Date: 2012-10
    Relation: Statistics and Probability Letters. 2012 Oct;82(10):1786-1791.
    Link to: http://dx.doi.org/10.1016/j.spl.2012.05.017
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0167-7152&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000307682400005
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84863088046
    Appears in Collections:[Pei-Sheng Lin] Periodical Articles

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