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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