In this paper we propose a flexible nested error regression small area model with high dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area specific model parameters. We propose a parametric bootstrap and jackknife method to estimate not only the mean squared errors but also other commonly used uncertainty measures such as standard errors and coefficients of variation. We conduct both modelbased and design-based simulation experiments and real-life data analysis to evaluate the proposed methodology

翻译：在本文中,我们提出一个灵活的嵌套错误回归小区域模型,带有高维参数,包括回归系数和差异构件的异质性。我们制定了一个新的强固小区域具体估计方程方法,以便能够在估计小区域特定模型参数时适当汇集大量区域。我们提出一个参数式靴子和竹刀刀法,以便不仅估计平均正方差,而且估计其他常用的不确定性措施,例如标准差错和变异系数。我们进行基于模型和基于设计的模拟试验和真实数据分析,以评估拟议方法。