Search Results

You are looking at 1 - 2 of 2 items for

  • Author: Fabrizio Solari x
Clear All Modify Search
Open access

Michele D’Aló, Stefano Falorsi and Fabrizio Solari

Abstract

Most important large-scale surveys carried out by national statistical institutes are the repeated survey type, typically intended to produce estimates for several parameters of the whole population, as well as parameters related to some subpopulations. Small area estimation techniques are becoming more and more important for the production of official statistics where direct estimators are not able to produce reliable estimates. In order to exploit data from different survey cycles, unit-level linear mixed models with area and time random effects can be considered. However, the large amount of data to be processed may cause computational problems. To overcome the computational issues, a reformulation of predictors and the correspondent mean cross product estimator is given. The R code based on the new formulation enables the elaboration of about 7.2 millions of data records in a matter of minutes.

Open access

Orietta Luzi, Fabrizio Solari and Fabiana Rocci

Abstract

The Frame SBS is a statistical register which has been developed at the Italian National Statistical Institute to support the annual estimation of structural business statistics (SBS). Actually, a number of core SBS are estimated by combining microdata directly supplied by different administrative sources. In this context, more accurate estimates for those SBS that are not covered by administrative sources can be obtained through small area estimation (SAE). In this article, we illustrate an application of SAE methods in the framework of the Frame SBS register in order to assess the potential advantages that can be achieved in terms of increased quality and reliability of the target variables. Different types of auxiliary information and approaches are compared in order to identify the optimal estimation strategy in terms of precision of the estimates.