Information Complexity Based Modeling in the Presence of Length-Biased Sampling

被引：0

作者：

Akman, Olcay ^{[1
]}

机构：

[1] Illinois State Univ, Dept Math, Normal, IL 61761 USA

来源：

JOURNAL OF STATISTICAL THEORY AND PRACTICE | 2010年 / 4卷 / 01期

关键词：

Mixture Models; Inverse Gaussian; Sampling distribution;

D O I：

10.1080/15598608.2010.10411972

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：

We utilize an Information Complexity Measure (ICOMP) based modeling approach to determine possible contamination due to length-biased sampling. The ICOMP approach considers both the lack of fit (goodness of fit) and the inherent information complexity of each of the distributions with respect to the real distribution of the data.

引用

页码：45 / 55

页数：11

共 27 条

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