Collaborative Research: Identifying Structure in Social Data Models using Markov Chain Monte Carlo Algorithms

协作研究：使用马尔可夫链蒙特卡罗算法识别社会数据模型中的结构

• 批准号: 1028329
• 负责人: Linda Young
• 金额: $ 16.25万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1028329&HistoricalAwards=false
• 关键词: Collaborative; Research; Identifying; Structure; Social

The analysis of social science data is often difficult for reasons that tend to affect other fields less substantially. One problem that is particularly difficult to handle with traditional statistical models is deliberately withheld information that correlates strongly with phenomena of interest. Such information can be thought of as unobserved clustering in the data. This project will substantially improve the current state of model-based clustering algorithms using Generalized Linear Mixed Dirichlet Models (GLMDM). The investigators' key objectives are to: (1) better understand unobserved clustering effects that are pervasive in social science datasets, notably with empirical studies of terrorism; (2) adapt GLMDM algorithms to provide substantive clusters of interest through posterior probabilities using covariate information; (3) develop an algorithmic approach that directly includes variable selection within clusters into a general clustering model; (4) speed up the simultaneous clustering and variable selection process by parallelization; and (5) distribute this technology as an easy-to-use R package for general use by others.This project will establish a new approach for using Bayesian nonparametric methods to produce clustering based on posterior probabilities. The development of nonparametric clustering algorithms is expected to substantially improve the current state of data clustering. The algorithmic developments, which will be disseminated widely, can be applied in any scientific field and will contribute to the statistical literature on Markov chain Monte Carlo. This new approach will be applied to the empirical study of terrorism. The project also will aid in the intellectual development of students and a post-doctorate researcher who will benefit from the project's interdisciplinary focus.

由于对其他领域影响较小的原因，社会科学数据的分析通常很困难。 传统统计模型特别难以处理的一个问题是故意隐瞒与感兴趣的现象密切相关的信息。 此类信息可以被视为数据中未观察到的聚类。 该项目将使用广义线性混合狄利克雷模型 (GLMDM) 大幅改进基于模型的聚类算法的当前状态。 调查人员的主要目标是：（1）更好地理解社会科学数据集中普遍存在的未观察到的聚类效应，特别是恐怖主义的实证研究； (2) 采用 GLMDM 算法，通过使用协变量信息的后验概率来提供实质性的感兴趣聚类； (3) 开发一种算法方法，将聚类内的变量选择直接纳入通用聚类模型中； (4)通过并行化加速同时聚类和变量选择过程； (5) 将这项技术作为易于使用的 R 包分发以供其他人普遍使用。该项目将建立一种使用贝叶斯非参数方法来生成基于后验概率的聚类的新方法。 非参数聚类算法的发展预计将大大改善数据聚类的当前状态。 该算法的发展将被广泛传播，可以应用于任何科学领域，并将为马尔可夫链蒙特卡罗的统计文献做出贡献。 这种新方法将应用于恐怖主义的实证研究。 该项目还将帮助学生和博士后研究人员的智力发展，他们将从该项目的跨学科重点中受益。