Statistical Inference and Modeling for Complex Data

这个项目集中在与复杂数据相关的三个研究领域。第一个研究领域是高维图形、数据分析和相关主题。第二个研究领域是混合模型和碰撞搜索问题。第三个领域是关于有偏差的数据的模型和推理程序。人们寻求几种不同的似然、半参数和迭代程序来对数据进行建模和推断。我们生活在信息时代。信息通常隐藏在具有不同外观的数据集中。挖掘和建模(海量)数据集包括发现模式、子组件、凹凸不平或特殊事件，以及开发解释数据和预测未来的模型。本研究旨在为高维、复杂或有抽样偏差的数据中出现的一些有趣的问题提供解决方案。探索了从复杂数据中提取有用信息的理论、方法和有效的计算算法。应用领域包括天文学、神经科学、质量控制和一些(其他)观测研究。

This project focuses on three research areas related to complex data.The first research area is on high dimensional graphics, data analysisand related topics. The second research area is on mixture models andbump hunting problems. The third area is on models and inferentialprocedures for data with biases. Several variants of likelihood,semi-parametric and iterative procedures are sought for modeling andmaking inferences about the data.We live in the information age. Information often hides in data setswith different appearances. Mining and modeling (massive) data setsinclude the discovery of patterns, subcomponents, bumps, or specialevents, and development of models that explain the data and predictthe future. This research is aimed at providing solutions to someinteresting problems arising from the data which are either highdimension, or complicated, or with a sampling bias. Theories,methods, and efficient computing algorithms are explored forextracting useful information from the complex data. Applicationareas include astronomy, neurosciences, quality control, and some(other) observational studies.