Sampling Error Estimation in Stratified Surveys

Abstract

Many operations carried out by official statistical institutes use large-scale surveys obtained by stratified random sampling without replacement. Variables commonly examined in this type of surveys are binary, categorical and continuous, and hence, the estimates of interest involve estimates of proportions, totals and means. The problem of approximating the sampling relative error of this kind of estimates is studied in this paper. Some new jackknife methods are proposed and compared with plug-in and bootstrap methods. An extensive simulation study is carried out to compare the behavior of all the methods considered in this paper.