EconPapers    
Economics at your fingertips  
 

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

Rendao Ye, Bingni Fang, Zhongchi Wang, Kun Luo and Wenting Ge

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 11, 3751-3766

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1980045 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:52:y:2023:i:11:p:3751-3766

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2021.1980045

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:lstaxx:v:52:y:2023:i:11:p:3751-3766