Small area estimation is a research area in official and survey statistics of great practical
relevance for national statistical institutes and related organizations. Despite rapid …

Official statistics is now considering seriously big data as a significant data source for
producing statistics. It holds the potential for providing faster, cheaper, more detailed and …

In many applications of small area estimation, dichotomous or categorical outcomes are the
targets of statistical inference. For example, in the analysis of labour markets, proportions of …

In recent years, M‐quantile regression has been applied to small area estimation to obtain
reliable and outlier robust estimators without recourse to strong parametric assumptions. In …

When the probabilities of selecting individuals (units) for the sample depend on the outcome
values, the selection mechanism is said to be informative. Under informative selection …

The main sources of statistical data on monetary and non‐monetary poverty are from sample
surveys on income and living conditions. These rarely give credible estimates at sub …

M-quantile random-effects regression represents an interesting approach for modelling
multilevel data when the researcher is focused on conditional quantiles. When data are …

This paper introduces small area estimators of poverty indexes, with special attention to the
poverty rate (or Head Count Index), and studies the sampling design consistency and the …

Small area (or small domain) estimation is still rarely applied in business statistics, because
of challenges arising from the skewness and variability of variables such as turnover. We …

In this paper, we propose a flexible nested error regression small area model with high-
dimensional parameter that incorporates heterogeneity in regression coefficients and …