Table 12 MSE of stratified and calibrated memory-type ratio estimators based on EEWMA at different values of ρ, \(\:{\lambda\:}_{1}=0.25,\:0.50,\:0.75\), \(\:{\lambda\:}_{2}=\:0.05,\:0.15,\:0.20\), and sample sizes \(\:n\).
From: Innovative memory-type calibration estimators for better survey accuracy in stratified sampling
ρ | n | λ1 = 0.25, λ2 = 0.05 | λ1 = 0.50, λ2 = 0.15 | λ1 = 0.75, λ2 = 0.20 | |||
---|---|---|---|---|---|---|---|
y* | \(\:{\widehat{\varvec{y}}}_{\varvec{m}\varvec{r}\varvec{e}}\) | y* | \(\:{\widehat{\varvec{y}}}_{\varvec{m}\varvec{r}\varvec{e}}\) | y* | \(\:{\widehat{\varvec{y}}}_{\varvec{m}\varvec{r}\varvec{e}}\) | ||
0.05 | 10 | 0.001284 | 0.000136 | 0.002778 | 0.000223 | 0.005422 | 0.000360 |
20 | 0.000576 | 0.000068 | 0.001377 | 0.000109 | 0.002681 | 0.000177 | |
30 | 0.000384 | 0.000045 | 0.000920 | 0.000074 | 0.001798 | 0.000119 | |
50 | 0.000226 | 0.000027 | 0.000560 | 0.000045 | 0.001078 | 0.000070 | |
200 | 0.000057 | 0.000007 | 0.000140 | 0.000011 | 0.000268 | 0.000017 | |
500 | 0.000023 | 0.000003 | 0.000055 | 0.000004 | 0.000107 | 0.000007 | |
0.25 | 10 | 0.001055 | 0.000011 | 0.002564 | 0.000182 | 0.004938 | 0.000285 |
20 | 0.000519 | 0.000056 | 0.001288 | 0.000089 | 0.002477 | 0.000142 | |
30 | 0.000358 | 0.000038 | 0.000857 | 0.000060 | 0.001640 | 0.000093 | |
50 | 0.000216 | 0.000023 | 0.000515 | 0.000036 | 0.000990 | 0.000057 | |
200 | 0.000054 | 0.000006 | 0.000127 | 0.000009 | 0.000247 | 0.000014 | |
500 | 0.000021 | 0.000002 | 0.000051 | 0.000004 | 0.000099 | 0.000006 | |
0.50 | 10 | 0.000948 | 0.000089 | 0.002286 | 0.000124 | 0.004477 | 0.000200 |
20 | 0.000475 | 0.000044 | 0.001137 | 0.000062 | 0.002214 | 0.000098 | |
30 | 0.000314 | 0.000029 | 0.000752 | 0.000040 | 0.001458 | 0.000065 | |
50 | 0.000188 | 0.000018 | 0.000455 | 0.000025 | 0.000886 | 0.000039 | |
200 | 0.000047 | 0.000004 | 0.000123 | 0.000006 | 0.000223 | 0.000010 | |
500 | 0.000018 | 0.000002 | 0.000046 | 0.000002 | 0.000088 | 0.000004 | |
0.75 | 10 | 0.000833 | 0.000062 | 0.002026 | 0.000070 | 0.003957 | 0.000112 |
20 | 0.000417 | 0.000031 | 0.001002 | 0.000034 | 0.001936 | 0.000054 | |
30 | 0.000280 | 0.000020 | 0.000675 | 0.000023 | 0.001298 | 0.000036 | |
50 | 0.000169 | 0.000012 | 0.000405 | 0.000014 | 0.000779 | 0.000022 | |
200 | 0.000042 | 0.000003 | 0.000100 | 0.000003 | 0.000196 | 0.000005 | |
500 | 0.000016 | 0.000001 | 0.000041 | 0.000001 | 0.000078 | 0.000002 | |
0.95 | 10 | 0.000753 | 0.000040 | 0.001801 | 0.000026 | 0.003512 | 0.000040 |
20 | 0.000373 | 0.000020 | 0.000900 | 0.000013 | 0.001735 | 0.000019 | |
30 | 0.000244 | 0.000013 | 0.000611 | 0.000008 | 0.001154 | 0.000013 | |
50 | 0.000149 | 0.000008 | 0.000363 | 0.000005 | 0.000692 | 0.000008 | |
200 | 0.000038 | 0.000002 | 0.000089 | 0.000001 | 0.000177 | 0.000002 | |
500 | 0.000015 | 0.000001 | 0.000036 | 0.000001 | 0.000069 | 0.000001 |