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Table 3 Average model standard error (and Monte Carlo standard error) relative to empirical standard deviation

From: Two-stage sampling in the estimation of growth parameters and percentile norms: sample weights versus auxiliary variable estimation

 

Fixed intercept

Fixed Age

R.Intercepts

R.Coefficients

R.Covariance

Complete

0.5694 (2.2505)

-0.4001 (2.2288)

-0.6407 (2.2251)

-1.6948 (2.2007)

3.3954 (2.3145)

Naive

2.1402 (2.2864)

-1.3941 (2.2074)

2.6297 (2.3006)

-1.6499 (2.2035)

-1.0186 (2.2171)

Weighted

8.5475 (2.4301)

-2.9089 (2.1738)

0.1789 (2.2503)

-0.4633 (2.2335)

-1.3902 (2.2119)

PoAux

-0.0666 (2.2366)

-3.0192 (2.1711)

-0.4454 (2.2310)

-1.1911 (2.2138)

-1.2034 (2.2128)

TPoAux

1.7493 (2.2775)

-3.1518 (2.1681)

-2.7690 (2.1791)

-1.5976 (2.2046)

-1.3179 (2.2104)

PoMiss

3.3083 (2.3130)

-4.2109 (2.1448)

1.2974 (2.2726)

-1.4011 (2.2091)

-1.1374 (2.2158)

TPoMiss

-3.3571 (2.1646)

-4.4398 (2.1396)

-4.8465 (2.1341)

-1.6610 (2.2033)

-1.5355 (2.2064)

NbAux

0.6920 (2.7424)

-1.9741 (2.6677)

-0.9206 (2.6994)

-0.6157 (2.7084)

-0.6002 (2.7096)

  1. The higher the value in absolute terms the larger the discrepancy between the model standard error and the empirical standard deviation. The models are abbreviated as follows: Complete the complete data model, Naive model 1 fitted to incomplete data, Weighted the Weighted model, PoAux the Poisson auxiliary model, TPoAux the transformed Poisson auxiliary model, PoMiss the Poisson/missingness model, TPoMiss the transformed Poisson/missingness model, NbAux the negative binomial auxiliary model