Conditional Modeling and Conditional Inference

在许多应用中，尽管存在大量数据集，但数据的复杂性和维数排除了非参数推理。与此同时，对于生成数据的详细机制了解得太少，以至于无法有意义地指定参数模型，这通常是事实。条件建模和条件推理是处理复杂高维数据的半参数方法，其重点是可管理的低维统计建模和估计。应用包括有效的特征估计和数据分类（例如在计算机视觉中），对广泛和科学相关假设的精确测试（例如在多电极神经元记录的统计分析中），探索非平稳过程中的时间尺度（例如在市场动态的研究中），以及通过连续低维扰动构建复杂分布（例如在概率上下文敏感语法的研究中）。高维数据无处不在。来源包括分子生物学、金融学、神经生理学记录以及互联网上的图像和文本。尽管这些数据的可用性几乎是无限量的，但它们的复杂性和高维性挑战了现有的统计模型，并成为成功应用程序的瓶颈。通常可以通过选择和关注数据的低维特征集合的数学方法来处理复杂性和维数。该方法避免了站不住脚或不可测试的模型假设，而不必损害数据的信息内容和力量。该研究处于统计理论和科学应用之间的接口，对技术（例如通过计算机视觉）和更广泛的社会（例如通过神经科学和更好的金融建模）具有潜在影响。

In many applications, the complexity and dimensionality of the data preclude nonparametric inference, despite the availability of massive data sets. At the same time, it is usually true that too little is known about the detailed mechanisms generating the data to meaningfully specify parametric models. Conditional modeling and conditional inference are semi-parametric approaches to complex high-dimensional data in which attention is focused on manageable low-dimensional statistical modeling and estimation. Applications include efficient feature estimation and data classification (e.g. in computer vision), exact tests for broad and scientifically relevant hypotheses (e.g. in the statistical analysis of multi-electrode neuronal recordings), exploration of time scale in non-stationary processes (e.g. in the study of market dynamics), and construction of complex distributions through successive low-dimensional perturbations (e.g. in the study of probabilistic context-sensitive grammars). High-dimensional data are ubiquitous. Sources include molecular biology, finance, neurophysiological recordings, and the imagery and text of the Internet. Despite the availability of almost unlimited amounts of these data, their complexity and high dimensionality challenge existing statistical models and represent a bottleneck to successful applications. Oftentimes the complexity and dimensionality can be finessed through mathematical methods that select and focus on a collection of low-dimensional characteristics of the data. The approach avoids untenable or un-testable model assumptions without necessarily compromising the information content and power of the data. The research is at the interface between statistical theory and scientific application, with potential impact in technology (e.g. through computer vision) and, more broadly, society (e.g. through neuroscience and better financial modeling).