Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys

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Calibration estimation using empirical likelihood in survey sampling (2009, unknown): “‚ (k+1) 2 (xi i „x) • L i1 w in Step 3. See also Chen et al (2002) .”
Empirical likelihood for small area estimation (2011, background): “Chen et al. [2002] discuss a modified Newton-Raphson algorithm with guaranteed convergence.”
Empirical likelihood for quantile regression models with longitudinal data (2011, methodology): “Throughout our numerical studies, we solve (2) for lðbÞ by employing the modified Newton–Raphson algorithm of Chen et al. (2002).”
Calibration Estimation in Survey Sampling (2010, background): “Huang & Fuller (1978), Park & Fuller (2005) and Chen et al. (2002) considered the range restriction in deriving a calibration estimator.”
Propensity Score Weighting for Causal Inference with Clustered Data (2017, methodology): “Calibration has been used in many scenarios. In survey sampling, calibration is widely used to integrate auxiliary information in esti12 mation or handle nonresponse; see, e.g., Wu and Sitter (2001), Chen et al. (2002), Kott (2006), Chang and Kott (2008) and Kim et al. (2016). In causal inference, c”