An em estimation approach for analyzing bivariate skew normal data with non monotone missing values
T Baghfalaki, M Ganjali - Communications in Statistics-Theory and …, 2011 - Taylor & Francis
Communications in Statistics-Theory and Methods, 2011•Taylor & Francis
In this article, an EM algorithm approach to obtain the maximum likelihood estimates of
parameters for analyzing bivariate skew normal data with non monotone missing values is
presented. A simulation study is implemented to investigate the performance of the
presented algorithm. Results of an application are also reported where a Bootstrap
approach is used to find the variances of the parameter estimates.
parameters for analyzing bivariate skew normal data with non monotone missing values is
presented. A simulation study is implemented to investigate the performance of the
presented algorithm. Results of an application are also reported where a Bootstrap
approach is used to find the variances of the parameter estimates.
In this article, an EM algorithm approach to obtain the maximum likelihood estimates of parameters for analyzing bivariate skew normal data with non monotone missing values is presented. A simulation study is implemented to investigate the performance of the presented algorithm. Results of an application are also reported where a Bootstrap approach is used to find the variances of the parameter estimates.
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