A measure of dependence between two compositions
Bergman, Jakob LU
and Holmquist, Björn
LU
(2012)
In Australian & New Zealand Journal of Statistics
54(4).
p.451-461
- Abstract
- We consider the problem of describing the correlation between two compositions. Using a bicompositional Dirichlet distribution, we calculate a joint correlation coefficient, based on the concept of information gain, between two compositions. Numerical values of the joint correlation coefficient are calculated for compositions of two and three components. We also present an estimator of the joint correlation coefficient for a sample from a bicompositional Dirichlet distribution. Two confidence intervals are also presented and we examine their empirical confidence coefficient using Monte Carlo study. Finally we apply the estimator to a data set analysing the correlation between the 1967 and 1997, and the 1977 and 1997 compositions of the... (More)
- We consider the problem of describing the correlation between two compositions. Using a bicompositional Dirichlet distribution, we calculate a joint correlation coefficient, based on the concept of information gain, between two compositions. Numerical values of the joint correlation coefficient are calculated for compositions of two and three components. We also present an estimator of the joint correlation coefficient for a sample from a bicompositional Dirichlet distribution. Two confidence intervals are also presented and we examine their empirical confidence coefficient using Monte Carlo study. Finally we apply the estimator to a data set analysing the correlation between the 1967 and 1997, and the 1977 and 1997 compositions of the government gross domestic product for the 50 U.S. states and District of Columbia. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1971432
- author
- Bergman, Jakob
LU
and Holmquist, Björn
LU
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- correlation, Dirichlet distribution, empirical confidence coefficient, Fraser information, joint correlation coefficient, simplex
- in
- Australian & New Zealand Journal of Statistics
- volume
- 54
- issue
- 4
- pages
- 451 - 461
- publisher
- Wiley-Blackwell
- external identifiers
-
- wos:000313888400004
- scopus:84872818420
- ISSN
- 1467-842X
- DOI
- 10.1111/j.1467-842X.2012.00688.x
- language
- English
- LU publication?
- yes
- id
- d48774fb-e9ce-4745-bb2d-ab127732a475 (old id 1971432)
- date added to LUP
- 2016-04-01 14:22:39
- date last changed
- 2022-01-28 00:18:41
@article{d48774fb-e9ce-4745-bb2d-ab127732a475,
abstract = {{We consider the problem of describing the correlation between two compositions. Using a bicompositional Dirichlet distribution, we calculate a joint correlation coefficient, based on the concept of information gain, between two compositions. Numerical values of the joint correlation coefficient are calculated for compositions of two and three components. We also present an estimator of the joint correlation coefficient for a sample from a bicompositional Dirichlet distribution. Two confidence intervals are also presented and we examine their empirical confidence coefficient using Monte Carlo study. Finally we apply the estimator to a data set analysing the correlation between the 1967 and 1997, and the 1977 and 1997 compositions of the government gross domestic product for the 50 U.S. states and District of Columbia.}},
author = {{Bergman, Jakob and Holmquist, Björn}},
issn = {{1467-842X}},
keywords = {{correlation; Dirichlet distribution; empirical confidence coefficient; Fraser information; joint correlation coefficient; simplex}},
language = {{eng}},
number = {{4}},
pages = {{451--461}},
publisher = {{Wiley-Blackwell}},
series = {{Australian & New Zealand Journal of Statistics}},
title = {{A measure of dependence between two compositions}},
url = {{https://lup.lub.lu.se/search/files/51894285/application_anzjs_rev2.pdf}},
doi = {{10.1111/j.1467-842X.2012.00688.x}},
volume = {{54}},
year = {{2012}},
}