Coordination Funds

在信息时代，数据以及可靠和有意义的统计评估的重要性比以往任何时候都要大。然而，面对海量数据，统计方法面临新的挑战。存储或隐私限制甚至需要对干净的原始数据进行预处理，其大规模通常导致随后应用经典有效统计方法的计算困难。通常，预处理后的数据不再具有原始数据的分布特性。此外，统计上有效的数据预处理通常依赖于后续统计推断的给定任务。因此，两个处理步骤是不可分割地联系在一起的。必须开发新的概念，以保证在潜在的预处理数据集上的有效性和效率，同时在计算上可处理大量数据。我们的目标是提供这种共同发展，从而影响现代统计数据分析的所有科学分支。在总共八年的资助期内，这个研究单位将成功地开发出全面的统计方法，以解决现代数据分析的这些新挑战。

In the information age, the importance of data together with reliable and meaningful statistical evaluation is greater than ever. In face of massive amounts of data, however, new challenges enter statistical methodology. Storage or privacy constraints require even clean raw data to be preprocessed, and its massiveness typically leads to computational intractability of subsequently applied classical efficient statistical methodology. Usually, preprocessed data do not share the distributional properties of the raw data any longer. Moreover, statistically efficient data preprocessing typically depends on the given task of subsequent statistical inference. Hence, both processing steps are inseparably linked. New concepts have to be developed which guarantee validity and efficiency on potentially preprocessed data sets while being computationally tractable at the same time for massive data. Our aim is to provide exactly this conjoint development, therefore influencing all scientific branches of modern statistical data analysis. Within a total funding period of eight years, this research unit shall successfully develop comprehensive statistical methodology which addresses these new challenges of modern data analysis.