$\left(m,n\right)=\left(5,5\right)$

$\left(m,n\right)=\left(15,10\right)$

$\left(m,n\right)=\left(20,15\right)$

$\left(m,n\right)=\left(30,30\right)$

$\left({\lambda }_1\mathrm{,}{\lambda }_2\right)$

方法

CP

IL

CP

IL

CP

IL

CP

IL

(1,4)

GV

0.959

0.4101

0.948

0.2619

0.951

0.2200

0.952

0.1631

LS

0.916

0.4129

0.944

0.2696

0.952

0.2150

0.956

0.1684

Bayes

0.945

0.4108

0.946

0.2647

0.953

0.2202

0.950

0.1632

Bootstrap-t

0.908

0.4900

0.928

0.2638

0.946

0.2225

0.947

0.1684

(3,4)

GV

0.947

0.5307

0.952

0.3710

0.957

0.3147

0.95

0.2414

LS

0.968

0.4613

0.965

0.3390

0.954

0.2942

0.948

0.2320

Bayes

0.951

0.5315

0.956

0.3704

0.946

0.3143

0.952

0.2418

Bootstrap-t

0.918

0.6899

0.937

0.3778

0.956

0.3327

0.945

0.2336

(5,4)

GV

0.942

0.5334

0.941

0.3702

0.943

0.3156

0.950

0.2432

LS

0.977

0.4625

0.946

0.3405

0.952

0.2962

0.948

0.2333

Bayes

0.952

0.5322

0.951

0.3710

0.955

0.3162

0.952

0.2432

Bootstrap-t

0.913

0.6955

0.925

0.3667

0.933

0.3316

0.937

0.2495

(7,4)

GV

0.944

0.5124

0.948

0.3511

0.947

0.2992

0.945

0.2294

LS

0.956

0.4520

0.955

0.3303

0.946

0.2848

0.947

0.2226

Bayes

0.952

0.5151

0.948

0.3510

0.953

0.2982

0.950

0.2295

Bootstrap-t

0.933

0.7028

0.942

0.3738

0.945

0.3143

0.951

0.2209