Small data analysis in sample surveys

Abstract

Sample surveys are widely recognised as high-quality and cost-effective sources of information to obtain estimates of target parameters at the population and subpopulation levels. If the sample size of the subpopulation is small (or even zero in some areas), then the researcher encounters the small area estimation (SAE) dilemma. SAE techniques have been developed to provide official statistics by leveraging survey samples and other available information, allowing estimators to borrow strength. In the first part of this talk, we present the main rationale behind introducing SAE machinery and discuss the necessary assumptions. The second part delves into the tools addressing simultaneous inference within the SAE framework, showcasing their practical application in measuring poverty rates in the Spanish region of Galicia. We conclude with general remarks on the potential applicability of machine learning within the SAE framework.