In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. We also show the proposed method to produce better asymptotic efficiency than using the simple average. Furthermore, simulation examples show that the proposed method can significantly improve statistical inference.

中文翻译：

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分布式统计推断的分区拟似然性

在大数据设置中，工作数据集通常分布在多台计算机上。然而，通常开发经典的统计方法来解决单一估计或推论的问题。我们在广义线性模型中采用了一种新颖的并行拟似然方法，以使不同子估计量之间的方差相对相似。估计是从数据的子集的投影获得的，后来通过适当挑选的未知的权重组合。我们还展示了所提出的方法比使用简单平均值产生更好的渐近效率。此外，仿真实例表明，该方法可以显着改善统计推断。