Table 13 Numerical values for \(\pmb K{_2}\) of SRS divided by \(\pmb K{_2}\) of RSS for all estimators.

From: Optimal estimation of power Chris-Jerry distribution parameters using ranked set sampling design with application

n

ESt.

MLE

ADE

CVME

MPSE

OLSE

RTADE

WLSE

LTADE

\(\alpha =1.5,~\beta =2.0\)

30

\(\hat{\alpha }\)

1.94788

1.41574

1.45599

1.21072

1.50387

1.20332

1.59456

1.54325

 

\(\hat{\beta }\)

3.40136

2.97115

2.97434

2.93861

3.06759

2.81424

3.02705

3.22909

80

\(\hat{\alpha }\)

1.72122

1.57278

1.39295

1.16454

1.64097

1.26055

1.71713

1.55103

 

\(\hat{\beta }\)

2.85263

3.04251

3.12267

2.82564

3.10359

2.92126

2.81938

2.86938

150

\(\hat{\alpha }\)

1.51839

1.42995

1.62539

1.14360

1.38797

1.27018

1.39548

1.54274

 

\(\hat{\beta }\)

2.61738

3.17670

2.48558

2.93802

3.06804

3.01271

2.89879

3.06963

300

\(\hat{\alpha }\)

1.88168

1.36565

1.35417

1.31781

1.52655

1.20635

1.50000

1.61207

 

\(\hat{\beta }\)

3.00376

2.84980

2.68852

2.70677

3.24803

2.98361

2.93050

3.12598

400

\(\hat{\alpha }\)

1.71354

1.52734

1.48378

1.29297

1.56119

1.24601

1.43689

1.42007

 

\(\hat{\beta }\)

3.02083

3.07143

2.70370

2.92473

3.22680

2.93370

2.69683

2.91905

500

\(\hat{\alpha }\)

1.63190

1.62264

1.47331

1.44500

1.48936

1.28625

1.38521

1.60680

 

\(\hat{\beta }\)

3.11940

2.52353

2.94083

2.67500

3.00641

2.88667

2.85185

3.23333

\(\alpha =0.8,~\beta =0.2\)

30

\(\hat{\alpha }\)

1.75194

1.46359

1.51740

1.16357

1.52696

1.04202

1.62540

1.57386

 

\(\hat{\beta }\)

1.84034

1.57143

1.54972

1.16957

1.37387

1.14333

1.51275

1.45609

80

\(\hat{\alpha }\)

1.52294

1.48638

1.55357

1.25253

1.45143

1.03605

1.43620

1.43793

 

\(\hat{\beta }\)

1.76923

1.85455

1.70896

1.52740

1.62329

1.09333

1.74825

1.64754

150

\(\hat{\alpha }\)

1.79048

1.27778

1.54545

1.26490

1.47222

1.19198

1.43017

1.44056

 

\(\hat{\beta }\)

2.02083

1.47619

1.70769

1.48611

1.63636

1.39791

1.55696

1.61290

300

\(\hat{\alpha }\)

1.50877

1.42857

1.58025

1.24675

1.54118

1.08122

1.38636

1.48529

 

\(\hat{\beta }\)

1.76000

1.60000

1.87879

1.48571

1.70270

1.16364

1.58974

1.65517

400

\(\hat{\alpha }\)

1.68293

1.46000

1.53125

1.32143

1.34783

1.02878

1.40299

1.57143

 

\(\hat{\beta }\)

1.94444

1.63636

1.73077

1.64000

1.51724

1.13514

1.65517

1.72727

500

\(\hat{\alpha }\)

1.65625

1.28889

1.42593

1.37209

1.46154

1.14912

1.44898

1.53846

 

\(\hat{\beta }\)

1.92857

1.45000

1.63636

1.55000

1.68182

1.21311

1.68182

1.76471

\(\alpha =1.0,~\beta =1.5\)

30

\(\hat{\alpha }\)

1.87862

1.68271

1.80688

1.22892

1.56077

1.17000

1.52626

1.54524

 

\(\hat{\beta }\)

3.12962

3.29243

2.83143

2.82643

3.14588

2.23471

2.78080

3.12941

80

\(\hat{\alpha }\)

1.61620

1.43863

1.52493

1.31751

1.44444

1.07143

1.41343

1.68339

 

\(\hat{\beta }\)

3.13978

2.91189

2.90986

2.76529

2.93714

2.05980

2.79719

3.08230

150

\(\hat{\alpha }\)

1.71362

1.41118

1.71654

1.30255

1.53125

1.21887

1.55140

1.55435

 

\(\hat{\beta }\)

2.98580

2.72093

2.97105

3.00567

2.69042

2.54512

3.07672

2.89891

300

\(\hat{\alpha }\)

1.60360

1.59398

1.65608

1.29730

1.45361

1.11864

1.41011

1.38509

 

\(\hat{\beta }\)

2.90751

2.89444

3.28729

2.90230

2.79679

2.19408

2.61353

2.77473

400

\(\hat{\alpha }\)

1.94595

1.47706

1.40000

1.47706

1.60584

1.08780

1.48780

1.63303

 

\(\hat{\beta }\)

2.82308

2.82836

2.81757

3.00752

2.75839

2.19048

2.67974

2.92029

500

\(\hat{\alpha }\)

1.67164

1.58621

1.40000

1.35714

1.55455

1.31646

1.52525

1.69412

 

\(\hat{\beta }\)

2.74545

2.83810

3.05505

2.72566

2.68103

2.38824

2.85714

2.90826