  The bivariate Gaussian distribution has been a key model for many
developments in statistics. However, many real-world phenomena generate data
that follow asymmetric distributions, and consequently bivariate normal model
is inappropriate in such situations. Bidimensional log-symmetric models have
attractive properties and can be considered as good alternatives in these
cases. In this paper, we discuss bivariate log-symmetric distributions and
their characterizations. We establish several distributional properties and
obtain the maximum likelihood estimators of the model parameters. A Monte Carlo
simulation study is performed for examining the performance of the developed
parameter estimation method. A real data set is finally analyzed to illustrate
the proposed model and the associated inferential method.
