Significance in scale space for bivariate density estimation

被引:65
作者
Godtliebsen, F [1 ]
Marron, JS
Chaudhuri, P
机构
[1] Univ Tromso, Dept Math & Stat, N-9037 Tromso, Norway
[2] Univ N Carolina, Dept Stat, Chapel Hill, NC 27599 USA
[3] Indian Stat Inst, Kolkata 700035, W Bengal, India
关键词
kernel estimates; nonparametric smoothing; statistically significant features;
D O I
10.1198/106186002317375596
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
An important problem in the use of density estimation for data analysis is whether or not observed features, such as bumps, are "really there" as opposed to being artifacts of the natural sampling variability. Here we propose a solution to this problem, in the challenging two-dimensional case, using the graphical technique of significance in scale space. Color and dynamic graphics form an important part of the visualization method.
引用
收藏
页码:1 / 21
页数:21
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