6533b822fe1ef96bd127cdf9
RESEARCH PRODUCT
Register data in sample allocations for small-area estimation
Jussi HakanenErkki PahkinenMauno Ketosubject
Computer scienceGeneral MathematicsGeography Planning and DevelopmentPopulationSample (statistics)01 natural sciences010104 statistics & probabilitySmall area estimationmodel-based EBLUP0502 economics and businessSampling designStatisticsrekisteritotanta0101 mathematicseducation050205 econometrics DemographyEstimationta113education.field_of_studyta112kaupparekisteritauxiliary and proxy data05 social sciencesEstimatortrade-off between areas and populationmonitavoiteoptimointiStratified samplingkohdentaminenmulti-objective optimizationSample size determinationGeneral Agricultural and Biological Sciencesperformancedescription
The inadequate control of sample sizes in surveys using stratified sampling and area estimation may occur when the overall sample size is small or auxiliary information is insufficiently used. Very small sample sizes are possible for some areas. The proposed allocation based on multi-objective optimization uses a small-area model and estimation method and semi-collected empirical data annually collected empirical data. The assessment of its performance at the area and at the population levels is based on design-based sample simulations. Five previously developed allocations serve as references. The model-based estimator is more accurate than the design-based Horvitz–Thompson estimator and the model-assisted regression estimator. Two trade-off issues are between accuracy and bias and between the area- and the population-level qualities of estimates. If the survey uses model-based estimation, the sampling design should incorporate the underlying model and the estimation method. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-27 | Mathematical Population Studies |