Dependence Structure of some Bivariate Distributions

Dimitrov, Boyan. "Dependence Structure of some Bivariate Distributions." Serdica Journal of Computing 8.3 (2014): 233-254. <http://eudml.org/doc/270076>.

@article{Dimitrov2014,
abstract = {Dependence in the world of uncertainty is a complex concept. However, it exists, is asymmetric, has magnitude and direction, and can be measured. We use some measures of dependence between random events to illustrate how to apply it in the study of dependence between non-numeric bivariate variables and numeric random variables. Graphics show what is the inner dependence structure in the Clayton Archimedean copula and the Bivariate Poisson distribution. We know this approach is valid for studying the local dependence structure for any pair of random variables determined by its empirical or theoretical distribution. And it can be used also to simulate dependent events and dependent r/v/’s, but some restrictions apply. ACM Computing Classification System (1998): G.3, J.2.},
author = {Dimitrov, Boyan},
journal = {Serdica Journal of Computing},
keywords = {Bivariate Poisson; Clayton Copula; Local Dependence; Measures of Dependence; Regression Coefficient},
language = {eng},
number = {3},
pages = {233-254},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Dependence Structure of some Bivariate Distributions},
url = {http://eudml.org/doc/270076},
volume = {8},
year = {2014},
}

TY - JOUR
AU - Dimitrov, Boyan
TI - Dependence Structure of some Bivariate Distributions
JO - Serdica Journal of Computing
PY - 2014
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 8
IS - 3
SP - 233
EP - 254
AB - Dependence in the world of uncertainty is a complex concept. However, it exists, is asymmetric, has magnitude and direction, and can be measured. We use some measures of dependence between random events to illustrate how to apply it in the study of dependence between non-numeric bivariate variables and numeric random variables. Graphics show what is the inner dependence structure in the Clayton Archimedean copula and the Bivariate Poisson distribution. We know this approach is valid for studying the local dependence structure for any pair of random variables determined by its empirical or theoretical distribution. And it can be used also to simulate dependent events and dependent r/v/’s, but some restrictions apply. ACM Computing Classification System (1998): G.3, J.2.
LA - eng
KW - Bivariate Poisson; Clayton Copula; Local Dependence; Measures of Dependence; Regression Coefficient
UR - http://eudml.org/doc/270076
ER -