Inference about the shape parameters of several inverse Gaussian distributions: testing equality and confidence interval for a common value

Mohammad Reza Kazemi () and Ali Akbar Jafari ()
Additional contact information
Mohammad Reza Kazemi: Fasa University
Ali Akbar Jafari: Yazd University

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 5, No 1, 529-545

Abstract: Abstract In this paper, we consider inference about the shape parameters of several inverse Gaussian distributions. At first, an approach is given to test the equality of these parameters based on modified likelihood ratio test. Then, five approaches are presented to construct confidence intervals for the common shape parameter. The performance of these approaches is studied using Monte Carlo simulation, and illustrated using a real data set.

Keywords: Confidence distribution; Maximum likelihood estimation; Modified signed log-likelihood ratio (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00184-018-0693-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:metrik:v:82:y:2019:i:5:d:10.1007_s00184-018-0693-9

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-018-0693-9

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().