A simple variance estimator for unequal probability sampling without replacement

Survey sampling textbooks often refer to the Sen-Yates-Grundy variance estimator for use with without-replacement unequal probability designs. This estimator is rarely implemented because of the complexity of determining joint inclusion probabilities. In practice, the variance is usually estimated by simpler variance estimators such as the Hansen-Hurwitz with replacement variance estimator; which often leads to overestimation of the variance for large sampling fractions that are common in business surveys. We will consider an alternative estimator: the Hájek (1964) variance estimator that depends on the first-order inclusion probabilities only and is usually more accurate than the Hansen-Hurwitz estimator. We review this estimator and show its practical value. We propose a simple alternative expression; which is as simple as the Hansen- Hurwitz estimator. We also show how the Hájek estimator can be easily implemented with standard statistical packages.<br/

An adaptive algorithm for estimating inclusion probabilities and performing the Horvitz–Thompson criterion in complex designs

Complex sampling schemes, Horvitz-Thomson estimation, Replications, Empirical inclusion probabilities, Bennet inequality,

Monitoring and forecasting annual public deficit every month: the case of France

French state deficit, Temporal aggregation, Intra-annual, Forecasting, C22, C53, E62, H60,