Modelling complex survey data with population level information: an empirical likelihood approach
提出在分层不等概率抽样下，用经验似然比统计量处理含冗余参数的模型，其渐近服从卡方分布，模拟显示置信区间覆盖率和尾部误差优于传统方法。
Survey data are often collected with unequal probabilities from a stratified population. In many modelling situations, the parameter of interest is a subset of a set of parameters, with the others treated as nuisance parameters. We show that in this situation the empirical likelihood ratio statistic follows a chi-squared distribution asymptotically, under stratified single and multi-stage unequal probability sampling, with negligible sampling fractions. Simulation studies show that the empirical likelihood confidence interval may achieve better coverages and has more balanced tail error rates than standard approaches involving variance estimation, linearization or resampling.