Small Area Estimation (SAE) models commonly assume Normal distribution or more generally exponential family. We propose a SAE unit-level model based on Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS completely release the exponential family distribution assumption and allow each parameter to depend on covariates. Besides, a bootstrap approach to estimate MSE is proposed. The performance of the proposed estimators is evaluated with model- and design-based simulations. Results show that the proposed redictor work better than the well-known EBLUP. The presented models are used to estimate the per-capita expenditure in small areas, based on the Italian data.

翻译：小面积估计模型通常采用正常分布模式,或更一般的指数式家庭模式。我们根据位置、规模和形状通用Additive模型(GAMLSS)提出SAE单位级模型。GAMLSS完全释放指数式家庭分布假设,允许每个参数取决于共变情况。此外,还提出了估算MSE的陷阱法。拟议估算器的性能是通过模型和设计模拟来评估的。结果显示,拟议的调整器比众所周知的EBLUP工作得更好。提出的模型用来根据意大利的数据估算小地区的人均支出。