After increasing the window size and the EM iterations, the EM algorithm achieves the best accuracies at the three missing data rates across the four LD levels.

A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and ...

We consider novel methods for the computation of model selection criteria in missing-data problems based on the output of the EM algorithm. The methodology is very general and can be applied to ...

This example estimates the normal SSM of the mink-muskrat data using the EM algorithm. The mink-muskrat series are detrended. Refer to Harvey (1989) for details of this data set. Since this EM ...

Testing the robustness of the data is part of the bias analysis. This process is important as new data sources are added, which can occur when a practice joins The US Oncology Network, for example.