摘要
Mixture models have become more popular in modelling compared to standard distributions. The mixing distributions play a role in capturing the variability of the random variable in the conditional distribution. Studies have lately focused on finite mixture models as mixing distributions in the mixing mechanism. In the present work, we consider a Normal Variance Mean mix<span>ture model. The mixing distribution is a finite mixture of two special cases of</span><span> Generalised Inverse Gaussian distribution with indexes <span style="white-space:nowrap;">-1/2 and -3/2</span>. The </span><span>parameters of the mixed model are obtained via the Expectation-Maximization</span><span> (EM) algorithm. The iterative scheme is based on a presentation of the normal equations. An application to some financial data has been done.
Mixture models have become more popular in modelling compared to standard distributions. The mixing distributions play a role in capturing the variability of the random variable in the conditional distribution. Studies have lately focused on finite mixture models as mixing distributions in the mixing mechanism. In the present work, we consider a Normal Variance Mean mix<span>ture model. The mixing distribution is a finite mixture of two special cases of</span><span> Generalised Inverse Gaussian distribution with indexes <span style="white-space:nowrap;">-1/2 and -3/2</span>. The </span><span>parameters of the mixed model are obtained via the Expectation-Maximization</span><span> (EM) algorithm. The iterative scheme is based on a presentation of the normal equations. An application to some financial data has been done.
