Figures & data: Semiparametric fractional imputation using empirical likelihood in survey sampling

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Table 1. The Monte Carlo bias (× 10⁻²), standard error (SE) (× 10⁻²) and root mean squared error (RMSE) (× 10⁻²) for four different methods with two error distributions in Simulation one.

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Figure 1. Scatter plot of BPXSY1 vs. BMXBMI.

Figure 2. QQ stands for quantile quantile plot (left panel) and residual vs. fitted value plot (right panel).

Table 2. The Monte Carlo bias (× 10⁻²), standard error (SE) (× 10⁻²) and root mean squared error (RMSE) (× 10⁻²) for four different methods and four parameters.

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Semiparametric fractional imputation using empirical likelihood in survey sampling

Table 1. The Monte Carlo bias (× 10⁻²), standard error (SE) (× 10⁻²) and root mean squared error (RMSE) (× 10⁻²) for four different methods with two error distributions in Simulation one.

Table 2. The Monte Carlo bias (× 10⁻²), standard error (SE) (× 10⁻²) and root mean squared error (RMSE) (× 10⁻²) for four different methods and four parameters.

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Semiparametric fractional imputation using empirical likelihood in survey sampling

Figures & data

Table 1. The Monte Carlo bias (× 10−2), standard error (SE) (× 10−2) and root mean squared error (RMSE) (× 10−2) for four different methods with two error distributions in Simulation one.

Table 2. The Monte Carlo bias (× 10−2), standard error (SE) (× 10−2) and root mean squared error (RMSE) (× 10−2) for four different methods and four parameters.

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Table 1. The Monte Carlo bias (× 10⁻²), standard error (SE) (× 10⁻²) and root mean squared error (RMSE) (× 10⁻²) for four different methods with two error distributions in Simulation one.

Table 2. The Monte Carlo bias (× 10⁻²), standard error (SE) (× 10⁻²) and root mean squared error (RMSE) (× 10⁻²) for four different methods and four parameters.