One challenge of large-scale data analysis is that the assumption of an identical distribution for all samples is often not realistic. An optimal linear regression might, for example, be markedly different for distinct groups of the data. Maximin effects have been proposed as a computationally attractive way to estimate effects that are common across all data without fitting a mixture distribution explicitly. So far just point estimators of the common maximin effects have been proposed in Meinshausen and Buhlmann (Ann Stat 43(4): 1801-1830,2015). Here we propose asymptotically valid confidence regions for these effects.
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机译：大规模数据分析的一个挑战是，所有样本的相同分布的假设往往不现实。例如，对于数据的不同组，可以是明显不同的线性回归。已经提出了最大效应作为计算估计所有数据中常见的效果的计算最有吸引力的方法，而不明确地拟合混合分布。到目前为止，梅因斯森和布尔曼（ANN STAT 43（4）：1801-1830,2015）提出了普通的最大效应的点估计。在这里，我们为这些效果提出了渐近的有效置信区。
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