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EM Algorithm for Estimating the Parameters of Weibull Competing Risk Model.
1Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia.
Applied Bionics and Biomechanics
|November 1, 2021
Summary
The expectation-maximization (EM) algorithm improves parameter estimation for additive Weibull models, outperforming traditional methods in survival analysis, especially with complex datasets. This approach offers more accurate results for bathtub-shaped hazard rates.
Area of Science:
- Statistics
- Survival Analysis
- Reliability Engineering
Background:
- The additive Weibull model is widely used in survival analysis for its ability to model bathtub-shaped hazard rates.
- Traditional estimation methods like maximum likelihood and least squares can be biased and perform poorly with numerous parameters.
Purpose of the Study:
- To evaluate the expectation-maximization (EM) algorithm as an alternative for parameter estimation in additive Weibull models.
- To address the limitations of conventional estimators in complex survival data scenarios.
Main Methods:
- Application of the expectation-maximization (EM) algorithm for parameter estimation.
- Conducting simulation studies to assess the accuracy and performance of the EM algorithm.
- Comparison with maximum likelihood and least square estimators.
Main Results:
- The EM algorithm demonstrated superior accuracy in parameter estimation for the additive Weibull model.
- Simulation results confirmed the advantages of the EM algorithm over traditional methods, particularly with large parameter sets.
- The EM algorithm provides reliable estimates for bathtub-shaped hazard rates.
Conclusions:
- The expectation-maximization (EM) algorithm is a robust and accurate method for estimating parameters in additive Weibull models.
- This method offers a significant improvement for survival analysis involving complex datasets and bathtub-shaped hazard rates.
- The EM algorithm provides a valuable alternative to biased traditional estimators.
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