Comparing Sampling and Estimation Strategies in Establishment Populations
Keywords: best linear unbiased predictor, deep stratification, general regression estimator, measure of heteroscedasticity, optimal sample, robust variance estimation, weighted balance
AbstractPopulation structure is a key determinant of the efficiency of sampling plans and estimators. Variables in many establishment populations have structures that can be described by simple linear models with a single auxiliary variable and a variance related to some power of that auxiliary. If a working model can be devised that is a good approximation to the population structure, then very efficient sample designs and estimators are possible. This study compares alternative strategies of (i) selecting a pilot study to estimate the variance power and using that estimate to select a main sample and (ii) selecting a only main sample based on an educated guess about the variance power. We also examine a number of sampling plans, including probability proportional to size, deep stratification based on a measure of size, and weighted balanced sampling. Population totals are estimated by best linear unbiased predictors, general regression estimators, and some other choices often used in practice.
Copyright for articles published in this journal is retained by the authors, with first publication rights granted to the journal. By virtue of their appearance in this open access journal, users can use, reuse and build upon the material published in the journal but only for non-commercial purposes and with proper attribution.