Design-based composite estimation of small proportions in small domains - Čiginas, Andrius (Nonlinear Analysis: Modelling and Control, 2023)
Traditional direct estimation methods are inefficient for domains of a survey population with small sample sizes. To estimate the domain proportions, we combine the direct estimators and the regression-synthetic estimators based on domain-level auxiliary information. For the case of small true proportions, we propose the design-based linear combination that is a robust alternative to the empirical best linear unbiased predictor (EBLUP) based on the Fay–Herriot model.We imitate the Lithuanian Labor Force Survey, where we estimate the proportions of the unemployed and employed in municipalities. We show where the proposed design-based composition and estimator of its mean square error are competitive for EBLUP and its accuracy estimation.
Keywords: small area estimation; area-level model; composite estimator; sample-size-dependent estimator; Labor Force Survey