Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152277 | Statistics & Probability Letters | 2012 | 6 Pages |
Abstract
A kk-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 kk-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 kk-nearest neighbor.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Yi-Hung Kung, Pei-Sheng Lin, Cheng-Hsiung Kao,