Symbolic Inference for Very Large Datasets

随着当代计算机能力的影响，与数据本身复杂的数据集正变得越来越例行公事)。不是常规的是如何分析这些数据。事实上，数据“收集”的速度正在迅速超过分析它们的能力。显然，即使在理论上可能适用现有方法的情况下，常规使用这种统计技术往往也是不合适的。有些方法(例如压榨)采用具有代表性的“样本”，然后对样本数据使用标准程序。其他人搜索子/模式(例如，数据挖掘)，然后尝试专注于这些模式背后的数据。其他人则以某种有意义的方式汇总数据。一种这样的聚集方法产生所谓的符号数据(例如列表、间隔、分布等)。符号数据的一个优点是，与采样集中的数据不同，符号值保留了所有原始数据，同时减少了数据集的大小。此外，虽然今天遇到的海量数据集是符号数据的一个来源，但还有许多数据本质上是符号的(无论这些数据集是小的还是大的)。所有这些都可以通过为符号数据开发的方法进行更好的分析。调查员涉及三个主要领域。其中一个领域是分类树。这里提出了区间数据和直方图值数据的距离度量，并将其用于将经典CART方法扩展到符号数据的新算法中。其次，回归方法，特别是Logistic回归和Cox比例风险模型，适用于符号数据。最后，对符号数据进行了因子分析和主成分分析。在当代计算机能力的影响下，数据本身复杂的数据集正变得更加无处不在。然而，这些计算机往往缺乏分析这些海量数据集的能力。因此，必须开发新的方法来处理它们。一种方法是以一种具有科学意义的方式聚合数据(实际聚合由手头的问题决定)。这样的聚集必然会产生形成列表、区间、直方图等的数据。研究人员在三个主要领域为区间数据开发了新的方法，在为区间和直方图定义距离度量后的分类树，回归方法，特别是Logistic回归，以及因子分析。结果被应用于数据。在解决当代数据集遇到的实际问题方面，通过整合数学/统计/计算领域，实现了协同增效。这些成果不是仅靠其中一个学科的工具就能实现的，而是需要这三个学科的共同作用。新的方法将广泛适用于那些在气象学、环境科学、社会科学、卫生保健计划、工业等领域产生的数据集，远远超出了那些激励工作的领域。这将对美国科学产生巨大影响。此外，由于博士生将成为合作者，国际研究人员将积极参与，这项研究有助于下一代和未来一代美国科学家的国际化。

Datasets that are complex with the data themselves "complex", and/or with structures that impose complications) are becoming more and more routine with the impact of contemporary computer capacity. What is not routine is how to analyse these data. Indeed, the data "collection" is fast outpacing the ability to analyse them. It is evident that, even in those situations where in theory available methodology might seem to apply, routine use of such statistical techniques is often inappropriate. Some methods (e.g. squashing) take representative "`samples"' and then use standard procedures on the sampled data. Others seek sub/patterns (e.g., data mining) and then try to focus on the data behind those patterns. Others aggregate the data in some meaningful way. One such aggregation method produces so-called symbolic data (such as lists, intervals, distributions, etc.). An advantage of symbolic data is that unlike those in sampled sets, a symbolic-value retains all the original data, while simultaneously reducing the size of the dataset. Further while the massive datasets encountered today are one source of symbolic data, there are many data that are naturally symbolic (be these small or large datasets). All are better analysed by methods developed for symbolic data. The investigator addresses three major areas. One area is classification trees. Here, distances measures for interval and histogram-valued data are developed; and then they are used in new algorithms which extend the classical CART methodolgy to symbolic data. Secondly, regression methods, in particular, logistic regression and Cox's proportional hazard models, are adapted to symbolic data. Finally, factor analysis and principal component methodoly is developed for symbolic data. With the impact of contemporary computer capacity, datasets that are complex with the data themselves "complex" are becoming more ubiquitous. Yet those same computers often lack the capacity to analyse these massive datasets. Therefore, new ways to handle them must be developed. One way is to aggregate the data in a scientifically meaningful way (with the actual aggregation being dictated by the question at hand). Such aggregation will necessarily produce data that form lists, intervals, histograms, etc. The investigator develops new methodologies for interval data in three major areas, classification trees after rst nding distance measures for intervals and histograms, regression methods especially logistic regression, and factor analysis. The results are applied to data. A synergism is achieved by the integration of mathematical/ statistical/computational arenas in addressing real issues encountered by contemporary datasets. The outcomes cannot be achieved by the tools of just one of these disciplines but needs all three. The new methodologies will have wide applicability to those datasets generated in, e.g., meteorology, environmental science, social sciences, health-care programs, industry, and the like, well beyond those motivating the work. This will have enormous impact on US science. Further since doctoral students will be engaged as collaborators and since international researchers will be active participants, the research helps in the internationalization of the next and future generation of US scientists.