Administration/Data Management

行政/数据管理

• 批准号: 69260471
• 负责人: Professor Dr. Axel Munk
• 金额: --
• 依托单位: Institut für Mathematische Stochastik (IMS)
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/69260471?language=en
• 关键词: Administration; Data; Management

A basic challenge for statistics at the interface of different sciences is the developmentof methods for the analysis of massive data sets, complex data structures and highdimensionalpredictors. The objectives of this German-Swiss research group are specific developmentand analysis of statistical regularization methods for complex data structures as they mayoccur in different fields of application. In the foreground, there are methods in which regularization is given by qualitative constraints on the structure or geometry of data models. Our basic hypothesis is that statistical regularization by qualitative constraints produces a consistent methodology for modeling of data structures which, on the one hand, is flexible enough to identify and scientifically utilize main structural features of data, but, on the other hand, specific enough to control prediction and classification error. Aims and scope of the research group. Our primary goal in this research group is to develop and analyze regularization methods for complex data structures in a unifying way from a statistical perspective. Particular emphasis is on regularization methods based on qualitative constraints, i.e. taking into account prior information about the geometric shape of certain model parameters or structural assumptions such as additivity or monotonicity. On the one hand we focus on different data models in various fields of application where we will develop specifically tailored regularization methods according to the needs in these subject matters. On the other hand, our fundamental claim is that statistical regularization via qualitative constraints reveals a unifying principle for a modeling process which is flexible enough to explain important data features but also specific enough to control the prediction or classification error in highly complex data structures. We aim at understanding their methodological commonalities on a broader scale. Our group consists of researchers having dealt with regularization techniques from various perspectives and in various disciplines for a long time, in particular from the perspective of statistics, numerical analysis, machine learning, pattern recognition, and econometrics. All of us cooperated already with some members of this group for a certain time, and in April 2008 we have been started to work all together at this ambitious project. In this second funding proposal three new colleagues (Gerard van den Berg, Tatyana Krivobokova, Ulrike Schneider) have been integrated. Each project is driven by a real substantial application problem. In many cases the same data set is tackled from various perspectives within different projects (e.g. the labor market data provided by B. Fitzenberger’s group or the microscopy data by S. Hell’s group). Various data projects from associated research groups from various fields of applications, such as biophysics, econometrics, medical imaging or molecular biology significantly enhance the practical merits of this Research group. We claim that statistical regularization procedures with structural or qualitative constraints allow a consistent methodical perspective and solution strategy while dealing with those subject fields.

统计学在不同科学的界面上面临的一个基本挑战是发展分析大量数据集、复杂数据结构和高维预测因子的方法。这个德国-瑞士研究小组的目标是针对复杂数据结构的统计正则化方法的具体开发和分析，因为它们可能出现在不同的应用领域。在前景中，存在通过对数据模型的结构或几何形状的定性约束来给出正则化的方法。我们的基本假设是，通过定性约束的统计正则化产生了一种一致的数据结构建模方法，一方面，这种方法足够灵活，可以识别和科学地利用数据的主要结构特征，但另一方面，又足够具体，可以控制预测和分类错误。研究小组的目标和范围。我们在这个研究小组的主要目标是从统计的角度以统一的方式开发和分析复杂数据结构的正则化方法。特别强调的是基于定性约束的正则化方法，即考虑有关某些模型参数的几何形状或结构假设（如可加性或单调性）的先验信息。一方面，我们专注于不同应用领域的不同数据模型，我们将根据这些主题的需求开发专门定制的正则化方法。另一方面，我们的基本主张是，通过定性约束的统计正则化揭示了建模过程的统一原则，该原则足够灵活，可以解释重要的数据特征，但也足够具体，可以控制高度复杂的数据结构中的预测或分类错误。我们的目标是在更广泛的范围内了解它们的方法学共性。我们的团队由研究人员组成，他们长期以来从各种角度和各个学科处理正则化技术，特别是从统计学，数值分析，机器学习，模式识别和计量经济学的角度。我们所有人都已经与这个小组的一些成员合作了一段时间，2008年4月，我们已经开始在这个雄心勃勃的项目中共同努力。在第二个供资提案中，纳入了三位新同事（Gerard货车den贝格、Tatyana Krivobokova和Ulrike Schneider）。每个项目都是由一个真实的实质性的应用问题驱动的。在许多情况下，同一数据集在不同的项目中从不同的角度处理（例如，B提供的劳动力市场数据）。Fitzenberger等人的显微镜数据或S.地狱之组）。来自各个应用领域的相关研究小组的各种数据项目，如生物物理学，计量经济学，医学成像或分子生物学，显着增强了该研究小组的实际价值。我们声称，统计正则化程序与结构或定性的限制，允许一个一致的方法的角度和解决方案的策略，同时处理这些主题领域。