Pseudo-empirical likelihood ratio confidence intervals for complex surveys

The authors show how an adjusted pseudo-empirical likelihood ratio statistic that is asymptotically distributed as a chi-square random variable can be used to construct confidence intervals for a finite population mean or a finite population distribution function from complex survey samples. They consider both non-stratified and stratified sampling designs, with or without auxiliary information. They examine the behaviour of estimates of the mean and the distribution function at specific points using simulations calling on the Rao-Sampford method of unequal probability sampling without replacement. They conclude that the pseudo-empirical likelihood ratio confidence intervals are superior to those based on the normal approximation, whether in terms of coverage probability, tail error rates or average length of the intervals.