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Small area estimation is receiving considerable attention due to the high demand for small area statistics. Small area estimators of means and totals have been widely studied in the literature. Moreover, in the last years also small area estimators of quantiles and poverty indicators have been studied. In contrast, small area estimators of inequality indicators, which are often used in socio-economic studies, have received less attention. In this article, we propose a robust method based on the M-quantile regression model for small area estimation of the Theil index and the Gini coefficient, two popular inequality measures. To estimate the mean squared error a non-parametric bootstrap is adopted. A robust approach is used because often inequality is measured using income or consumption data, which are often non-normal and affected by outliers. The proposed methodology is applied to income data to estimate the Theil index and the Gini coefficient for small domains in Tuscany (provinces by age groups), using survey and Census micro-data as auxiliary variables. In addition, a design-based simulation is carried out to study the behaviour of the proposed robust estimators. The performance of the bootstrap mean squared error estimator is also investigated in the simulation study. [ABSTRACT FROM AUTHOR]

Suicide is a leading cause of death among adolescents and is recognized as a serious public health problem. This study aimed to investigate the relationship between family characteristics and the risk of suicide among adolescents in Italy using nationwide official data. We carried out a cohort study based on the record linkage between the 15th Italian Population Census, the Italian Population Register, and the National Register of Causes of Death. Suicides in adolescents aged 10–19 years from 2012 to 2016 were analyzed. Hazard ratios of mortality from suicide were estimated through a multivariable Cox regression model using time-on-study as the time scale. We included 8,284,359 children and adolescents (51% males, 49% females). Over the 5-year follow-up, we registered 330 deaths from suicides (74% males), mostly occurred in the age class 15–19 years (86%). The suicide rate was 1.71 per 100,000 person-years among males and 0.65 among females. We found some familial characteristics associated with a higher risk of dying by suicide, including: living in single-parent or reconstructed families (among boys), a 40-year or more age gap between mother and child (among girls), having highly educated parents, an age difference between parents greater than 5 years. Furthermore, the study showed a lower risk for boys living in urban areas and for both boys and girls living in South Italy. Our results could help in identifying adolescents at high risk of suicide who could benefit from the planning of targeted intervention strategies. [ABSTRACT FROM AUTHOR]

Summary: A three‐level M‐quantile model for small area estimation is proposed. The methodology represents an efficient alternative to prediction by using a three‐level linear mixed model in the presence of outliers and it is based on an extension of M‐quantile regression. A modified method of the traditional M‐quantile (two‐level) approach for poverty estimation is also proposed. In addition, an estimator of the mean‐squared prediction error is described, which is based on a bootstrap procedure. The methodology proposed, as well as the three‐level empirical best predictor, are applied to Polish European Union Survey on Income and Living Conditions and census data to estimate poverty at local administrative unit 1 level in Poland, i.e. the level for which the Central Statistical Office of Poland has not published any official estimates to date. [ABSTRACT FROM AUTHOR]