This session starts with a contribution dealing with fitting small area unit-level and area-level models under informative sampling design and nonignorable nonresponse. The second contribution studies how to treat measurement errors in small area estimation by investigating functional, structural and naive models approaches. The third contributions give some proposals about the estimation of sampling variances for area level models. Finally, the last contribution introduces area-level compositional mixed models for estimating small area category proportions.
Small area estimation using area-level models can sometimes benefit from covariates that are observed subject to random errors. When it is possible to estimate the variances of these errors across small areas, then one can account for the uncertainty in such covariates using measurement error models (e.g., Ybarra and Lohr, 2008). For instance, estimates of […]
In the context of Small are estimation (SAE), nonresponse may seriously reduce the already small sample size. Accordingly, a joint consideration of both problems is especially challenging. For survey practitioners, it has been a common practice to use weighting and imputation to mitigate nonresponse. Both techniques achieve point-identifiability by imposing the assumption of missing at […]
The OECD defines cut-off sampling as a sampling procedure in which a predetermined threshold is established with all units in the population and all units at or below (above) the threshold are excluded from the possible selection in a sample. This sampling technique is typically used in business surveys, in which small firms are included […]
Compositional data analysis deals with vectors, called compositions, with nonnegative elements representing proportions or counts of some partition of a given population that fulfil a size constraint. Many surveys have categorical variables that produce compositional data after calculating the direct weighted estimators of the domain totals or proportions of categories. For the case of a […]