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Summary Model-based small area estimation relies on mixed effects regression models that link the small areas and borrow strength from similar domains. When the auxiliary variables that are used in the models are measured with error, small area estimators that ignore the measurement error may be worse than direct estimators. Alternative small area estimators accounting for measurement error have been proposed in the literature but only for continuous auxiliary variables. Adopting a Bayesian approach, we extend the unit level model to account for measurement error in both continuous and categorical covariates. For the discrete variables we model the misclassification probabilities and estimate them jointly with all the unknown model parameters. We test our model through a simulation study. The effect of the model proposed is emphasized through application to data from the Ethiopia Demographic and Health Survey where we focus on the women’s malnutrition issue: a dramatic problem in developing countries and an important indicator of the socio-economic progress of a country.

In this paper we focus on small area models with measurement error in covariates. Based on data from the Measuring Morality study, a nationally representative survey of United States residents, that contains a validated behavioural measure of generosity (the dictator game) along with the household income of respondents, we define a measurement error model suitable to obtain area-level estimates of generosity at the district level. We investigate the effect of introducing the measurement error in this model, focusing on fully Bayesian as well as Empirical Bayesian (EB) estimation proposed in the recent literature. We discuss the characteristics of each of the two approaches and analyze the impact of the measurement error on the resulting estimates based on real data and a simulation study.

Besides health and socio-economic status, the social relationships maintained during elderly play an important role in shaping the living conditions at older ages. In this part of life, the family represents the major framework in which interpersonal relationships are experienced. With population ageing, the family increasingly ensures elderly care, especially where targeted public policies are lacking, and its protective effect on survival is foreseen. In this paper we investigate the complex role of family in mortality and contribute to the debate on the ideal living condition for elderly people, exploiting a nationwide integrated survey on private and collective households in France. Following a cohort of 16,263 individuals aged 55, we investigate the effect of family relations on survival. We question whether the lack of family care connected with the absence of active relationships with family members may be at least partly compensated by institutional settings. Estimates of life expectancy show that at the age of 60, people living in institutions live on average 10 years less that those living in private households, the gap decreasing with age. Cox proportional hazards models show a protective role of children on mortality. Having no children seems to be associated with a lower risk of death, but the effect is significant only for those declaring rare or no contacts with their children. Survival analysis also suggests that institutional living arrangement may be protective for the most fragile individuals, namely the severely disabled, isolated persons.

In survey sampling, interest often lies in unplanned domains (or small areas), whose sample sizes may be too small to allow for accurate design-based inference. To improve the direct estimates by borrowing strength from similar domains, most small area methods rely on mixed effects regression models. ¶ This contribution extends the well known Fay–Herriot model (Fay and Herriot, 1979) within a Bayesian approach in two directions. First, the default normality assumption for the random effects is replaced by a nonparametric specification using a Dirichlet process. Second, uncertainty on variances is explicitly introduced, recognizing the fact that they are actually estimated from survey data. The proposed approach shrinks variances as well as means, and accounts for all sources of uncertainty. Adopting a flexible model for the random effects allows to accommodate outliers and vary the borrowing of strength by identifying local neighbourhoods where the exchangeability assumption holds. Through application to real and simulated data, we investigate the performance of the proposed model in predicting the domain means under different distributional assumptions. We also focus on the construction of credible intervals for the area means, a topic that has received less attention in the literature. Frequentist properties such as mean squared prediction error (MSPE), coverage and interval length are investigated. The experiments performed seem to indicate that inferences under the proposed model are characterised by smaller mean squared error than competing approaches; frequentist coverage of the credible intervals is close to nominal.

Statistical agencies and other institutions collect data under the promise to protect the confidentiality of respondents. When releasing microdata samples, the risk that records can be identified must be assessed. To this aim, a widely adopted approach is to isolate categorical variables key to the identification and analyze multi-way contingency tables of such variables. Common disclosure risk measures focus on sample unique cells in these tables and adopt parametric log-linear models as the standard statistical tools for the problem. Such models often have to deal with large and extremely sparse tables that pose a number of challenges to risk estimation. This paper proposes to overcome these problems by studying nonparametric alternatives based on Dirichlet process random effects. The main finding is that the inclusion of such random effects allows us to reduce considerably the number of fixed effects required to achieve reliable risk estimates. This is studied on applications to real data, suggesting, in particular, that our mixed models with main effects only produce roughly equivalent estimates compared to the all two-way interactions models, and are effective in defusing potential shortcomings of traditional log-linear models. This paper adopts a fully Bayesian approach that accounts for all sources of uncertainty, including that about the population frequencies, and supplies unconditional (posterior) variances and credible intervals., Published at http://dx.doi.org/10.1214/15-AOAS807 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)