New Statistical Methods for Medical Signals and Images

描述（由申请人提供）：医学和生物数据通常以数字化信号和图像的形式出现；例如，质谱图，心电图痕迹，人类步态周期，甚至基因表达阵列的表示。随着仪器数据采集成为常规，这些图像、信号或路径的序列通常与其他协变量测量一起被收集，导致数据集的基本测量单位或响应是一个非常高维的对象。基因微阵列是新技术如何导致大规模数据采集的一个主要例子；我们还期望通过质谱法获得更直接的蛋白质测量。该项目继续专注于开发建模和理解这些自然适应高维的数据的技术。对于基因表达阵列的回归和分类，我们考虑的方法是单变量和多变量之间的微妙混合，提供良好的预测和基因选择。为了研究协方差结构，该项目继续开发主成分的“稀疏”形式和判别分析，这些形式可能对不一定是光滑形式的局部现象更敏感，或者更适合于不规则观测数据。

DESCRIPTION (provided by applicant): Medical and biological data often come in the form of digitized signals and images; for example, mass spectrograms, electrocardiogram traces, human gait cycles, and even the representation of gene expression arrays. As instrumental data acquisition becomes routine, sequences of such images, signals or paths are collected, often along with other covariate measurements, resulting in datasets where the basic unit of measurement, or response, is a very high-dimensional object. The gene microarray is a leading example of how new technology has led to data acquisition on a massive scale; we also expect to work with more direct protein measurements obtained through mass spectrometry. The project continues to focus on developing techniques for modeling and understanding such data that naturally adapt to the high dimensionality. For regression and classification with gene expression arrays, we consider methods that are a subtle blend between univariate and multivariate, that offer both good prediction and gene selection. To study covariance structure, the project continues to develop "sparse" forms of principal components and discriminant analysis that may be more sensitive to either local phenomena of not necessarily smooth form or that are more adapted to irregularly observed data. Corresponding quadratically regularized methods in appropriate bases form a natural foil for comparison, and inference procedures for some of these are proposed. For estimation of means, the project will examine sparse empirical Bayes and False Discovery Rate methods for estimating non smooth local phenomena. Much of this work will be carried out in existing and new collaborations with researchers in oncology, genetics, cardiology and other specialties, working for example on cancer, heart disease and human locomotion.