Table 10 MSE of memory-type and proposed calibrated ratio estimators based on EWMA statistic at different values of correlation coefficient ρ, smoothing parameters \(\:\lambda\:=0.50,\:0.75,\:1.00,\) and sample sizes \(\:n\).
From: Innovative memory-type calibration estimators for better survey accuracy in stratified sampling
ρ | n | λ = 0.50 | λ = 0.75 | λ = 1.00 | |||
---|---|---|---|---|---|---|---|
\(\:{{\stackrel{-}{y}}^{M}}_{pt}\) | \(\:{\widehat{y}}_{mp}\) | \(\:{{\stackrel{-}{y}}^{M}}_{pt}\) | \(\:{\widehat{y}}_{mp}\) | \(\:{{\stackrel{-}{y}}^{M}}_{pt}\) | \(\:{\widehat{y}}_{mp}\) | ||
− 0.05 | 10 | 0.035752 | 0.000025 | 0.063054 | 0.000004 | 0.105316 | 0.000075 |
20 | 0.017522 | 0.000012 | 0.031670 | 0.000022 | 0.053501 | 0.000038 | |
30 | 0.011821 | 0.000008 | 0.021123 | 0.000015 | 0.035537 | 0.000025 | |
50 | 0.007111 | 0.000005 | 0.012642 | 0.000009 | 0.021145 | 0.000015 | |
200 | 0.001757 | 0.000001 | 0.003162 | 0.000002 | 0.005311 | 0.000004 | |
500 | 0.000703 | 0.000000 | 0.001282 | 0.000001 | 0.002115 | 0.000002 | |
− 0.25 | 10 | 0.038048 | 0.000023 | 0.068815 | 0.000042 | 0.115304 | 0.000071 |
20 | 0.018849 | 0.000012 | 0.034376 | 0.000021 | 0.057268 | 0.000035 | |
30 | 0.012711 | 0.000008 | 0.023094 | 0.000014 | 0.037734 | 0.000023 | |
50 | 0.007593 | 0.000047 | 0.013591 | 0.000008 | 0.022993 | 0.000014 | |
200 | 0.001911 | 0.000001 | 0.003396 | 0.000002 | 0.005721 | 0.000004 | |
500 | 0.000768 | 0.000000 | 0.001368 | 0.000001 | 0.002287 | 0.000001 | |
− 0.50 | 10 | 0.040971 | 0.000022 | 0.073906 | 0.000038 | 0.123899 | 0.000064 |
20 | 0.020640 | 0.000011 | 0.037184 | 0.000019 | 0.061697 | 0.000032 | |
30 | 0.013831 | 0.000007 | 0.024630 | 0.000013 | 0.041739 | 0.000022 | |
50 | 0.008296 | 0.000004 | 0.014952 | 0.000008 | 0.024880 | 0.000013 | |
200 | 0.002089 | 0.000001 | 0.003725 | 0.000002 | 0.006138 | 0.000003 | |
500 | 0.000834 | 0.000000 | 0.001493 | 0.000001 | 0.002496 | 0.000001 | |
− 0.75 | 10 | 0.044780 | 0.000011 | 0.081238 | 0.000036 | 0.133767 | 0.000058 |
20 | 0.022196 | 0.000010 | 0.040066 | 0.000018 | 0.066787 | 0.000029 | |
30 | 0.011482 | 0.000006 | 0.026627 | 0.000012 | 0.044253 | 0.000019 | |
50 | 0.008846 | 0.000004 | 0.016090 | 0.000007 | 0.026514 | 0.000011 | |
200 | 0.002230 | 0.000001 | 0.004026 | 0.000002 | 0.006645 | 0.000003 | |
500 | 0.000897 | 0.000000 | 0.001616 | 0.000001 | 0.002676 | 0.000001 | |
− 0.95 | 10 | 0.046862 | 0.000018 | 0.084832 | 0.000032 | 0.141350 | 0.000053 |
20 | 0.023610 | 0.000009 | 0.042147 | 0.000016 | 0.071195 | 0.000027 | |
30 | 0.015585 | 0.000006 | 0.028655 | 0.000011 | 0.047536 | 0.000018 | |
50 | 0.009454 | 0.000004 | 0.017143 | 0.000007 | 0.028471 | 0.000011 | |
200 | 0.002333 | 0.000001 | 0.004229 | 0.000002 | 0.007157 | 0.000003 | |
500 | 0.000953 | 0.000000 | 0.001710 | 0.000001 | 0.002853 | 0.000001 |