What are the Challenges of Using MLE in Epidemiology?
While MLE is a powerful tool, it comes with certain challenges:
1. Computational Complexity: Maximizing the likelihood function, especially for complex models, can be computationally intensive. 2. Data Quality: The accuracy of MLE depends heavily on the quality and completeness of the data. 3. Model Assumptions: The validity of MLE estimates relies on the correctness of the model assumptions. Incorrect assumptions can lead to biased estimates. 4. Convergence Issues: In some cases, the numerical optimization algorithms used to maximize the likelihood function may not converge or may converge to local maxima instead of the global maximum.
