Optimal Model Selection for Truncated Data among Non-Nested Competitive Models

被引：0

作者：

Torkaman, Parisa ^{[1
]}

机构：

[1] Malayer Univ, Malayer, Iran

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2018年 / 17卷 / 01期

关键词：

Kullback-Leibler information criteria; non-nested competitive model; truncated data;

D O I：

10.22237/jmasm/1525132980

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：

Selecting a model for incomplete data is an important issue. Truncated data is an example of incomplete data, which sometimes occurs due to inherent limitations. The maximum likelihood estimator features and its asymptotic distribution are studied, and a test statistic among non-nested competitive model of incomplete data is presented, which can select an appropriate model close to the true model. This close-to-true model under the null hypothesis of the equivalency of two competitive models against alternative hypothesis is selected.

引用

页数：10

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