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.

由于往往影响其他领域的原因，社会科学数据的分析通常很困难。 故意拒绝与感兴趣现象密切相关的信息，这是一个特别困难的问题。 可以将这些信息视为数据中未观察到的聚类。 该项目将通过广义线性混合Dirichlet模型（GLMDM）实质上改善基于模型的聚类算法的当前状态。 研究人员的主要目标是：（1）更好地了解社会科学数据集中无视的聚类效应，特别是在恐怖主义的经验研究中； （2）适应GLMDM算法通过使用协变量信息的后概率来提供实质性关注的群集； （3）开发一种算法方法，该方法将簇中的变量选择直接包括在一般聚类模型中； （4）通过并行化加速同时聚类和可变选择过程； （5）将该技术作为易于使用的R包分配给其他人。该项目将建立一种使用贝叶斯非参数方法来基于后验概率生成聚类的新方法。 非参数聚类算法的开发有望大大改善数据聚类的当前状态。 将广泛传播的算法发展可以在任何科学领域应用，并将为马尔可夫链蒙特卡洛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。 该项目还将有助于学生的智力发展和一名毒品后研究人员，他们将从该项目的跨学科重点中受益。