Table 2 The ML estimates, log-likelihood, AIC, CAIC and BIC for data set
From: The beta Burr type X distribution properties with application
Model | ML estim. | LL | AIC | CAIC | BIC |
|---|---|---|---|---|---|
Beta Burr type X | \(\hat{\alpha }= -0.7249\) | 16.0016 | 40.003 | 40.691 | 48.578 |
\(\hat{\beta }= 13.3691\) | |||||
\(\hat{\lambda }= 0.28056\) | |||||
\(\hat{\theta }=7.81788\) | |||||
Burr type X | \(\hat{\lambda }= 0.9869\) | 23.9287 | 51.8575 | 52.0575 | 56.1437 |
\(\hat{\theta }= 5.4860\) | |||||
Burr type X one parameter | \(\hat{\lambda }= 5.7249\) | 23.9584 | 49.9167 | 49.9823 | 56.0599 |
G Exponential | \(\hat{\theta }= 2.6115\) | 31.3834 | 66.7669 | 66.9669 | 71.0532 |
\(\hat{\lambda }= 31.3489\) | |||||
Rayleigh | \(\hat{\theta }= 0.6490\) | 49.7909 | 101.5818 | 101.6474 | 103.7249 |