  The skewing mechanism of Azzalini for continuous distributions is used for
the first time to derive a new generalization of the geometric distribution.
Various structural properties of the proposed distribution are investigated.
Characterizations, including a new result for the geometric distribution, in
terms of the proposed model are established. Extensive simulation experiment is
done to evaluate performance of the maximum likelihood estimation method.
Likelihood ratio test for the necessity of additional skewing parameter is
derived and corresponding simulation based power study is also reported. Two
real life count datasets are analyzed with the proposed model and compared with
some recently introduced two-parameter count models. The findings clearly
indicate the superiority of the proposed model over the existing ones in
modelling real life count data.
