Ensemble Methods for Data Analysis in the Behavioral, Social and Economic Sciences

提案ID：0437169 PRINCIPAL调查员：Richard Berk和Robert D MareInstitIONS：UCLATITLE：行为、社会和经济科学中的数据分析方法摘要行为、社会和经济科学中的观测数据分析通常使用统计建模。在过去的十年里，另一种数据分析方法在应用数学、计算机科学和统计学中不断发展，一些人称之为“算法”。通常不需要努力构建数据是如何生成的模型。目标是将一组输入连接到一个或多个输出，以便通过计算机算法优化某些明确的目标函数。这项建议侧重于“集合方法”，这是算法方法的一个特别有前途的特例。这样做的目的是帮助促进集合方法的开发者和行为、社会和经济科学中的实证研究人员之间更有效的互动。这种方法是使用真实的数据集，将集成方法应用于重要的社会科学数据分析问题。这些包括1)复杂计算机模拟的评估程序，2)传统统计模型的诊断程序，3)观测研究中的混杂调整，以及4)当响应变量高度倾斜时的分类和预测练习。在每种情况下，集成方法的性能将与传统建模的性能进行比较，使用建议中详细说明的十个评估标准。建议将集成方法应用于真实数据集应该会对行为科学、社会科学和经济学以及数学和统计科学产生几个广泛的好处。在“数据挖掘”的大标题下，强大而快速发展的数据分析工具将被应用于困难的数据分析问题。这些练习将说明集合方法在某些严苛的实证研究中的长处和短处。这一经验将帮助行为、社会和经济科学了解合奏方法何时有用，以及它们的局限性。同样重要的是，这些应用将为各种集合方法提供“试验台”，从这些方法中可能会出现现有集合程序中的潜在改进和当前文献中没有充分讨论的新的技术问题。因此，数学和统计科学也能从中受益。最后，将要使用的每个数据集都可能具有丰富的实质性影响。虽然这项建议的重点是方法论，但从主题的角度来看，完全有可能进行的数据分析也将具有指导意义。

PROPOSAL ID: 0437169PRINCIPAL INVESTIGATOR: Richard Berk and Robert D MareINSTITUTIONS: UCLATITLE: Ensemble Methods for Data Analysis in the Behavioral, Social and Economic SciencesABSTRACTThe analysis of observational data in the behavioral, social, and economic sciences is commonly undertaken with statistical modeling. Over the past decade, another approach to data analysis has been evolving in applied mathematics, computer science, and statistics that some have called ``algorithmic.'' There is usually no effort to construct a model of how the data were generated. The goal is to link a set of inputs to one or more outputs so that some clear objective function is optimized by a computer algorithm. This proposal focuses on ``ensemble methods,'' which are an especially promising special case of algorithmic methods. The goal this is to help foster more effective interactions between the developers of ensemble methods and empirical researchers in the behavioral, social, and economic sciences. The approach is to apply, using real data sets, ensemble methods to important social science data analysis problems. These include 1) evaluation procedures for complex computer simulations, 2) diagnostic procedures for conventional statistical models, 3) adjustments for confounding in observational studies, and 4) classification and prediction exercises when the response variable is highly skewed. In each case, the performance of the ensemble methods will be compared to the performance of conventional modeling using ten assessment criteria detailed in the proposal.The proposed application of ensemble methods to real data sets should have several broad benefits for the behavioral, social and economic sciences, as well as for the mathematical and statistical sciences. Powerful and rapidly developing data analysis tools, under the broad rubric of "data mining," will be applied to difficult data analysis problems. These exercises will illustrate strengths and weakness of ensemble methods for certain kinds of demanding empirical research. This experience will help inform the behavioral, social, and economic sciences about when ensemble methods can be useful and what their limitations can be. Equally important, the applications will provide a ``test bed'' for a variety of ensemble methods from which will likely emerge potential refinements in existing ensemble procedures and new technical questions insufficiently addressed in the current literature. Thus, the mathematical and statistical sciences can benefit as well. Finally, each of the data sets to be used is potentially rich in substantive implications. Although the emphasis in this proposal is methodological, it is entirely possible that the data analyses to be undertaken will also be instructive from subject-matter perspective.