New method boosts accuracy of weather predictions, impacting disaster preparedness.
The article explores a method called two-stage maximum likelihood estimation for analyzing copula models. By dropping the assumption that the chosen model is the true one, researchers found that the estimator is consistent for a specific parameter value. They also discovered that the variance of the copula parameter estimate can change when the true model assumption is dropped. Surprisingly, using highly misspecified models can sometimes lead to more accurate estimates. This approach provides a robust way to make inferences in copula models, even when the true model is not known.
