Table 1 MSE of memory-type ratio and conventional 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

0.75

50

1684.564

57.705

1684.564

169.128

1684.564

268.443

1684.564

497.696

1684.564

912.845

100

772.964

37.647

772.964

83.219

772.964

126.667

772.964

255.592

772.964

443.241

200

357.593

17.621

357.593

38.195

357.593

66.390

357.593

110.321

357.593

211.715

300

232.755

12.042

232.755

24.902

232.755

38.812

232.755

73.966

232.755

135.976

500

130.052

6.842

130.052

15.572

130.052

23.312

130.052

44.901

130.052

87.315

0.80

50

1386.321

61.730

1386.321

135.572

1386.321

214.098

1386.321

408.530

1386.321

779.512

100

601.559

29.283

601.559

63.306

601.559

103.500

601.559

192.825

601.559

361.707

200

295.843

15.241

295.843

32.418

295.843

47.407

295.843

93.385

295.843

172.788

300

193.462

10.231

193.462

20.749

193.462

33.598

193.462

60.482

193.462

109.599

500

114.152

5.757

114.152

11.518

114.152

19.506

114.152

35.951

114.152

066.601

0.85

50

1072.357

49.307

1072.357

107.423

1072.357

160.986

1072.357

330.921

1072.357

588.912

100

475.455

23.436

475.455

53.800

475.455

82.333

475.455

148.521

475.455

289.185

200

234.995

12.093

234.995

23.911

234.995

40.235

234.995

71.945

234.995

131.431

300

143.728

7.592

143.728

16.547

143.728

25.453

143.728

48.379

143.728

086.577

500

87.631

4.457

87.631

9.685

87.631

15.531

87.631

25.600

87.631

048.349

0.90

50

751.155

35.897

751.155

70.741

751.155

110.616

751.155

223.772

751.155

394.228

100

319.649

15.176

319.649

35.479

319.649

57.721

319.649

107.874

319.649

194.950

200

160.736

8.034

160.736

16.716

160.736

26.954

160.736

50.332

160.736

092.379

300

105.941

5.516

105.941

10.544

105.941

17.813

105.941

34.241

105.941

061.083

500

59.303

3.141

59.303

6.738

59.303

10.475

59.303

19.608

59.303

036.399

0.95

50

363.060

17.036

363.060

36.569

363.060

59.418

363.060

114.124

363.060

208.338

100

172.228

8.628

172.228

17.518

172.228

27.332

172.228

53.374

172.228

92.085

200

81.364

4.201

81.364

8.389

81.364

13.015

81.364

26.045

81.364

47.510

300

51.623

2.637

51.623

5.702

51.623

8.576

51.623

16.294

51.623

29.323

500

29.589

1.539

29.589

3.452

29.589

5.438

29.589

9.543

29.589

17.590