Small area estimation of proportions and rates under area-level Dirichlet mixed models
提出区域级狄利克雷混合模型，用于预测小区域的成分指标（如就业、失业、非经济活动人口比例），通过参数自助法估计均方误差，并应用于2022年西班牙劳动力调查数据。
Abstract This paper introduces an area-level Dirichlet mixed model for predicting compositional indicators of small areas. Direct estimators of the domain category proportions of a classification variable are the target variables of the new model. Once the model has been selected and fitted to the data, predictors of proportions, totals and rates of small areas are obtained and their mean square errors are estimated by parametric bootstrap. Several simulation experiments, designed to analyse the behaviour of the fitting algorithm, the small area predictors and the bootstrap procedure, are carried out. An application to real data from the Spanish Labour Force Survey, in the last quarter of 2022, is given. The target is the estimation of proportions of employed, unemployed and inactive people and unemployment rates by province, sex and age group.