Prediction Models Based on Large Scale Image Data

统计学研究涉及基于数据的模型的开发和理解。通常，这些数据是以数字的形式存在的，但最近，统计学家开始为图像形式的数据开发模型。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。这项研究将进一步推动这些图像模型的发展。该项目还将包括在本科生和研究生一级开发新课程，以培训学生使用和理解这些模式。这个项目是开发一个综合的研究计划，研究一大类大规模的功能图像模型。PI的目标是利用超高维图像数据发展自适应和/或局部区域回归、有限混合回归和变换生存回归。这些模型的主要优点是为了更好地解释而保留尖锐的边缘，为了更好地表现总体中的异质性，以及处理复杂的删失数据。拟议研究的理论贡献是为了解决几个学科的基本问题，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。将开发课程，培训学生使用和理解这些模式。

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.