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Table 4 MLE estimates for the parameters of \(\mathcal {EGW}\) distribution with Nelore data via BFGS, SANN, and Nelder–Mead algorithms

From: Analyzing and solving the identifiability problem in the exponentiated generalized Weibull distribution

Methods Parameters Estimates SE Confidence Interval (0.95)
BFGS a 0.01631688 0.004142414 \(\left[ 0.01625805 ; 0.01637572 \right]\)
b 58.74132600 27.050302156 \(\left[ 58.35713331 ; 59.12551870 \right]\)
\(\alpha\) 0.03633065 0.004862259 \(\left[ 0.03626159 ; 0.03639970 \right]\)
\(\beta\) 3.17753052 0.241423958 \(\left[ 3.17410160 ; 3.18095944 \right]\)
SANN a 0.801092593 0.495883447 \(\left[ 0.794049611 ; 0.808135576 \right]\)
b 4.477365906 0.822148967 \(\left[ 4.465689008 ; 4.489042804 \right]\)
\(\alpha\) 0.009091301 0.002221628 \(\left[ 0.009059748 ; 0.009122855 \right]\)
\(\beta\) 2.454931581 0.286086581 \(\left[ 2.450868322 ; 2.458994840 \right]\)
Nelder–Mead a 1.27972e−10
b 13.08793619 4.6261741 \(\left[ 13.0808279; 13.0950445\right]\)
\(\alpha\) 0.69883693 0.2502418 \(\left[ 0.6884190; 0.7092548\right]\)
\(\beta\) 5.052449497 0.3667520 \(\left[ 4.9210393; 5.1838597\right]\)