Abstract
The theory of multilevel hierarchical data Expectation Maximization (EM)-algorithm is introduced via discrete time Markov chain (DTMC) epidemic models. A general model for a multilevel hierarchical discrete data is derived. The observed sample Y in the system is a stochastic incomplete data, and the missing data Z exhibits a multilevel hierarchical data structure. The EM-algorithm to find ML-estimates for parameters in the stochastic system is derived. Applications of the EM-algorithm are exhibited in the two DTMC models, to find ML-estimates of the system parameters. Numerical results are given for influenza epidemics in the state of Georgia (GA), USA.
| Original language | English |
|---|---|
| Article number | e12622 |
| Journal | Heliyon |
| Volume | 8 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2022 |
Scopus Subject Areas
- General
Keywords
- Chain binomial models
- EM-algorithm
- Hierarchical data estimation
- Maximum likelihood estimator
- SEIR epidemic models
- Statistical inference