Table 3 MSE of memory-type exponential ratio and conventional exponential ratio estimators.

From: Variance estimation using memory type estimators based on EWMA statistic for time scaled surveys in stratified sampling

\(\:{\varvec{\uprho\:}}_{\mathbf{Y}\mathbf{X}}\)

n

\(\:\varvec{\lambda\:}=\:0.1\)

\(\:\varvec{\lambda\:}=\:0.2\)

\(\:\varvec{\lambda\:}=\:0.3\)

\(\:\varvec{\lambda\:}=\:0.5\)

\(\:\varvec{\lambda\:}=\:0.75\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}^{\varvec{M}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}^{\varvec{M}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}^{\varvec{M}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}^{\varvec{M}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}\)

\(\:{\varvec{t}}_{\varvec{e}\varvec{r}\varvec{s}\varvec{t}}^{\varvec{M}}\)

0.75

50

287.204

15.025

287.204

30.622

287.204

49.801

287.204

94.347

287.204

169.915

100

137.540

7.522

137.540

15.083

137.540

24.845

137.540

48.176

137.540

80.284

200

66.382

3.823

66.382

7.542

66.382

12.318

66.382

24.384

66.382

41.518

300

45.932

2.820

45.932

4.953

45.932

7.625

45.932

15.232

45.932

26.176

500

27.052

1.837

27.052

2.972

27.052

4.946

27.052

8.109

27.052

14.679

0.80

50

255.414

13.429

255.414

29.969

255.414

45.223

255.414

83.560

255.414

147.409

100

124.594

6.858

124.594

13.343

124.594

22.232

124.594

40.725

124.594

78.522

200

65.596

3.968

65.596

7.017

65.596

10.631

65.596

19.167

65.596

35.686

300

38.434

2.506

38.434

4.892

38.434

6.530

38.434

13.099

38.434

23.081

500

22.654

1.631

22.654

2.687

22.654

4.334

22.654

7.331

22.654

13.830

0.85

50

218.007

11.628

218.007

24.288

218.007

40.116

218.007

79.100

218.007

121.875

100

108.828

6.216

108.828

12.334

108.828

18.258

108.828

37.720

108.828

64.205

200

50.623

3.114

50.623

6.133

50.623

9.417

50.623

17.803

50.623

31.901

300

33.970

2.276

33.970

3.956

33.970

6.261

33.970

11.363

33.970

20.344

500

19.378

1.447

19.378

2.435

19.378

3.583

19.378

6.654

19.378

12.734

0.90

50

188.256

10.158

188.256

18.942

188.256

32.542

188.256

60.608

188.256

111.353

100

85.711

4.887

85.711

9.628

85.711

15.201

85.711

30.890

85.711

55.922

200

42.142

2.654

42.142

4.534

42.142

7.802

42.142

14.811

42.142

26.205

300

28.955

1.963

28.955

3.321

28.955

5.070

28.955

9.717

28.955

17.081

500

17.722

1.351

17.722

2.056

17.722

3.202

17.722

6.011

17.722

10.408

0.95

50

145.571

8.072

145.571

16.923

145.571

25.886

145.571

49.223

145.571

84.633

100

69.035

4.035

69.035

8.260

69.035

12.683

69.035

23.019

69.035

42.619

200

34.492

2.226

34.492

3.933

34.492

6.163

34.492

11.225

34.492

19.472

300

23.946

1.719

23.946

2.534

23.946

4.165

23.946

7.849

23.946

13.446

500

13.747

1.164

13.747

1.714

13.747

2.336

13.747

4.344

13.747

7.702