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Prediction Models Based on Large Scale Image Data

基于大规模图像数据的预测模型

基本信息

批准号：
1613060
负责人：
Xiao Wang
金额：
$ 10万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613060&HistoricalAwards=false
关键词：
Prediction Models Based Large Scale

项目摘要

Research in statistics involves the development and understanding of models based on data. Generally, these data are in the form of numbers, but more recently, statisticians have begun to develop models for data in the form of images. These functional image models have broad applications in neuroscience, engineering, and biomedical practice. This research will further the development of these image models. This project will also include the development of new courses at the undergraduate and graduate levels to train students in the use and understanding of these models. This project is to develop an integrated research program that studies a broad class of large scale functional image models. The PI aims to develop the adaptive and/or local region regression, the finite mixture regression, and the transformation survival regression with ultra-high dimensional image data. The key advantages of these models are to preserve sharp edges for better interpretation, to incorporate the heterogeneity in the population for better representation, and to handle sophisticated censored data. The theoretical contributions of the proposed research are made towards addressing fundamental issues across several disciplines, including nonparametric statistics and machine learning. These functional image models have broad applications in neuroscience, engineering, and biomedical practice. Courses will be developed to train students in the use and understanding of these models.

统计学研究涉及基于数据的模型的开发和理解。一般来说，这些数据是数字形式的，但最近，统计学家开始开发图像形式的数据模型。这些功能图像模型在神经科学、工程和生物医学实践中有着广泛的应用。本研究将进一步发展这些图像模型。该项目还将包括在本科和研究生一级开发新课程，以培训学生使用和理解这些模型。本计画旨在发展一个综合性的研究计画，研究大规模功能影像模型。PI的目的是开发自适应和/或局部区域回归，有限混合回归，和变换生存回归超高维图像数据。这些模型的主要优点是保留锐边以更好地解释，将异质性纳入群体以更好地表示，并处理复杂的删失数据。所提出的研究的理论贡献是为了解决跨多个学科的基本问题，包括非参数统计和机器学习。这些功能图像模型在神经科学、工程和生物医学实践中有着广泛的应用。将开设课程，培训学生使用和理解这些模型。