Bootstrap inference on the Behrens-Fisher-type problem for the skew-normal population under dependent samples

In this article, the inference on location parameter for the skew-normal population under dependent samples is considered. First, the Bootstrap test statistics and Bootstrap confidence intervals for the Behrens-Fisher-type problem are constructed, respectively, when the scale parameter or skewness p...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 52; no. 11; pp. 3751 - 3766
Main Authors Ye, Rendao, Fang, Bingni, Wang, Zhongchi, Luo, Kun, Ge, Wenting
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.06.2023
Taylor & Francis Ltd
Subjects
Behrens-Fisher-type problem
Bootstrap inference
Confidence intervals
dependent sample
Inference
location parameter
Parameters
Population (statistical)
Regression analysis
Skew-normal population
Statistical analysis
Statistical tests
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Summary:In this article, the inference on location parameter for the skew-normal population under dependent samples is considered. First, the Bootstrap test statistics and Bootstrap confidence intervals for the Behrens-Fisher-type problem are constructed, respectively, when the scale parameter or skewness parameter is known. Second, the Monte-Carlo simulation results indicate that the Bootstrap approach is better than the approximate approach in most cases. Finally, the above approaches are illustrated by using the real data examples of gross domestic product and stock closing price.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2021.1980045