ESTIMATING DIMENSION FROM SMALL SAMPLES

被引：57

作者：

JUDD, K

机构：

[1] Mathematics Department, The University of Western Australia, Nedlands

来源：

PHYSICA D | 1994年 / 71卷 / 04期

关键词：

D O I：

10.1016/0167-2789(94)90008-6

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Details of a new algorithm to estimate the dimension of a measure from point samples are described. The new algorithm emphasizes that dimension need not be the same at all scales, that is, there need not be a ''scaling region.'' The algorithm is verified with Gaussian measures and is found to be reliable and have less demanding data requirements than conventional estimators. Using certain convergence properties of Gaussian measures, it is shown that good estimates can be made from very. small samples.

引用

页码：421 / 429

页数：9

共 17 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].