Foreword to the special issue on “Advances in Survey Statistics”

Abstract

This Special Issue was inspired by the desire to bring together valuable and modern contributions in the field of Survey Statistics, which shed light on some of the challenging tasks of designing and analyzing surveys. Traditionally, survey statistics has been seen as a separate branch to the rest of statistics, because of its reliance on the design-based approach to inference, as opposed to the model-based approach used in other applications of statistics. The former approach is certainly sensible for survey data, because it is common for design features such as strata, clusters and unequal probabilities to induce strong biasing effects in the data. While these effects can be incorporated in models, an often simpler and more robust approach is to rely on randomization theory and inverse-probability weighting to produce estimates and perform inference.However, there is also a need to recognize the many interactions between randomizationbased inference and statistical modeling. The role of modeling in regression estimation is well understood and has been incorporated into the design-based framework for several decades. At the other extreme, small area estimation methods are fully model-based, with the design effects incorporated through the model or, when appropriate, ignored. But there are many statistical research topics that occur at the intersection of randomization-based and model-based inference, as illustrated by many of the contributions to this Special Issue. These include nonresponse and measurement errors in survey data, estimation under non-measurable designs, and fitting of statistical models under design informativeness. The contributions of this Special Issue deal with topics in either design ([1,7]) or estimation ([2-6]), also including approaches B M. Giovanna Ranalli