On finite population sampling theory under certain linear regression models

Abstract

Problems of estimating totals in finite populations, when auxiliary information regarding variate values is available, are considered under some linear regression, ‘ super-population’, models. Optimal strategies involving linear estimators are derived under certain variance assumptions and compared under various assumptions. For a model which seems to apply in many practical problems, the conventional ratio estimator is shown to be, in a certain natural sense, optimal, but for all models considered, the optimal sampling plans are purposive, i.e. nonrandom. With a squared error loss function, the strategy of using a probability proportional to size sampling plan and the Horvitz-Thompson estimator is shown to be inadmissible in many models for which the strategy seems ‘reasonable’ and in a particular model for which it is, in one sense, optimal. Some of the results concerning purposive sampling and the ratio estimator are supported by an empirical study.