Medical Malpractice Litigation Under National Health Insurance: Essential or Expendable?

Identification of time-continuous models from sampled data is a long standing topic of discussion, and many approaches have been suggested. The Maximum Likelihood method is asymptotically and theoretically superior to other methods. However, it may suffer from numerical inaccuracies at fast sampling and it also requires reliable initial parameter values. A number of efficient and useful alternatives to the maximum-likelihood method have been developed over the years. The most important of these are State-Variable filters, combined with Instrumental Variable methods, including the simplified refined IV method. In this contribution we perform unpretentious numerical experiments to comment on these methods, and their mutual benefits.CADIC