Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/1630
Title: Baker-Lin-Huang Type Bivariate Distributions Based on Order Statistics
Authors: Bayramoglu, K.
Bayramoglu (Bairamov), I.
Keywords: Bivariate distribution function
FGM distributions
Copula
Positive quadrant dependent
Negative quadrant dependent
Order statistics
Pearson's correlation coefficient
62H20
62G30
Gumbel-Morgenstern Distributions
Dependence Structure
Fixed Marginals
Copulas
Symmetry
Family
Publisher: Taylor & Francis Inc
Abstract: Baker (2008) introduced a new class of bivariate distributions based on distributions of order statistics from two independent samples of size n. Lin and Huang (2010) discovered an important property of Baker's distribution and showed that the Pearson's correlation coefficient for this distribution converges to maximum attainable value, i.e., the correlation coefficient of the Frechet upper bound, as n increases to infinity. Bairamov and Bayramoglu (2013) investigated a new class of bivariate distributions constructed by using Baker's model and distributions of order statistics from dependent random variables, allowing higher correlation than that of Baker's distribution. In this article, a new class of Baker's type bivariate distributions with high correlation are constructed based on distributions of order statistics by using an arbitrary continuous copula instead of the product copula.
URI: https://doi.org/10.1080/03610926.2013.775301
https://hdl.handle.net/20.500.14365/1630
ISSN: 0361-0926
1532-415X
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
1630.pdf394.96 kBAdobe PDFView/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

10
checked on Nov 20, 2024

WEB OF SCIENCETM
Citations

9
checked on Nov 20, 2024

Page view(s)

58
checked on Nov 18, 2024

Download(s)

30
checked on Nov 18, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.