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Table 3 MLE estimates for the parameters of \(\mathcal {EW}\) distribution with simulated data from \(\mathcal {EW}\) distribution via BFGS, SANN, and Nelder–Mead algorithms

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

n

Method

Inference results

b

c

\(\beta\)

50

BFGS

Estimates

4.402925

4.102611

5.949618

SE

5.697425

0.751590

2.601159

MSE

22.795855

0.402740

9.699106

SANN

Estimates

4.449711

4.106828

5.920183

SE

6.085270

0.789521

2.686872

MSE

24.098193

0.413645

8.739624

Nelder–Mead

Estimates

4.472318

4.107355

5.951902

SE

5.962889

0.764355

2.603384

MSE

25.399142

0.420800

10.046063

Time

0d:8h:50m:45s (31845 s)

100

BFGS

Estimates

4.213534

4.113357

5.299846

SE

3.566710

0.509471

1.548855

MSE

18.221747

0.290249

2.756462

SANN

Estimates

4.207505

4.114013

5.297310

SE

3.650426

0.521174

1.571037

MSE

17.379728

0.288609

2.753479

Nelder–Mead

Estimates

4.249701

4.115646

5.298766

SE

3.666593

0.513693

1.549940

MSE

19.982132

0.299304

2.763964

Time

0d:15h:18m:26s (55106 s)

500

BFGS

Estimates

3.155945

4.017774

5.055031

SE

0.906044

0.185317

0.627086

MSE

0.906766

0.035019

0.396619

SANN

Estimates

3.157440

4.017996

5.054715

SE

0.911430

0.186253

0.629546

MSE

0.911503

0.035196

0.398176

Nelder–Mead

Estimates

3.156035

4.017759

5.055316

SE

0.906550

0.185396

0.627336

MSE

0.908748

0.035088

0.397327

Time

2d:16h:15m:17s (231317 s)

1000

BFGS

Estimates

3.078456

4.009790

5.023386

SE

0.609515

0.128449

0.437921

MSE

0.390373

0.016572

0.189574

SANN

Estimates

3.078632

4.009775

5.023852

SE

0.611345

0.128798

0.439047

MSE

0.393664

0.016703

0.190998

Nelder–Mead

Estimates

3.077648

4.009598

5.024238

SE

0.609420

0.128441

0.438062

MSE

0.391118

0.016610

0.190232

Time

4d:12h:27m:34s (390454 s)