Statistical Methodologies for High Dimensional Correlated Data

高维相关数据的统计方法

• 批准号: 288332-2012
• 负责人: Gao, Xin
• 金额: $ 1.09万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571395
• 关键词: Statistical; Methodologies; Dimensional; Correlated; Data

Nowadays research efforts in various disciplines have generated complex data sets different from the traditional types. The new kinds of data sets may have high-dimensionality, a large number of parameters, complicated dependency relationship, or measurements from different experimental platforms. There is an increasing demand for the development of statistical methodologies and inference procedures to analyze these complex data sets. We plan to conduct theoretical investigations on how to develop statistical methods especially designed for high dimensional correlated data. The proposed new methods can be used to address the problems arising from the fields of statistical genetics and bioinformatics. To properly analyze high dimensional data in the presence of complex dependency structure will be the main motivation and also the major challenge for our project. To reduce the dimensionality of the problem, there are a number of strategies, including selecting a simpler sub-model, or enforcing a sparse model through penalization. In this research project, we will focus on the investigation of penalized estimation and model selection methods for dependent data. The theories will provide more insight into the inference we can draw from data. Especially, the method will enable us to discern which pieces of information are important among huge amount of data. On the applied side of our project, we will be investigating different methods to perform data integration. Such procedures are in great need because current technologies produce various kinds of data in different platforms. We will also aim to set up a general framework based on pseudo likelihood to perform genetic analysis for correlated populations. We expect to develop this unified approach which is general for a wide range of genetic problems.

如今，各个学科的研究工作产生了不同于传统类型的复杂数据集。新类型的数据集可能具有高维数、数量多的特点