Regularization for High Dimensional Inference and Sparse Recovery

高维推理和稀疏恢复的正则化

• 批准号: 1205296
• 负责人: Jelena Bradic
• 金额: $ 12万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205296&HistoricalAwards=false
• 关键词: Regularization; Dimensional; Inference; Sparse; Recovery

This proposal aims at answering pertinent questions to identifying sparse subsets of high dimensional covariate spaces, in the context of regularization methods, when simple least square loss functions are not well suited. The broad goal is to understand the fundamental interactions between nonlinear and/or censored structure of the statistical model, the regularization scheme and the intrinsic dimensionality of the problem. Specifically, the PI aims at (1) Identifying new statistical problems and regularization schemes, with complex nonlinear and time-to-event structure with high dimensional covariate space that are tuned to the characteristics of the data; (2) Developing novel non-asymptotic oracle bounds on the behavior of regularized estimators where techniques of random matrix theory, especially high probability bounds on various matrix norms, and approximation theory, will be utilized to enhance understanding of the effects of dimensionality on the non-asymptotic properties; (3) Investigating new non-asymptotic bounds on risk of semiparametric methods where the statistical model is possibly misspecified; (4) Analyzing and developing models that use special interplay between censoring rate, sample size and dimensionality of the problem and importantly (5) Introducing new algorithms that optimally and efficiently solve the investigated large scale problems.Explosion of microarray technologies has lead to vast number of large-scale genome-wide association studies where simultaneous analysis of a large number of SNPs is pertinent to discovering genetic identification of complex diseases. Presence and importance of time to event component calls for significant advances in statistical methodology for both NP dimensionality and censored structure. This research proposal aims at developing innovative and effective statistical methods for such complex data with special impact in genetic, public health and bioinformatic sciences, where censoring and vast number of gene interactions make identification of misbehaving genes very difficult. Moreover, the developments of this inter-disciplinary project will enhance new scientific discoveries, make new collaborative connections with practitioners and will promote teaching and training of graduate students on the contemporary state-of-the-art machine learning techniques applied to semiparametric models and censored data. To promote the progress of science, PI will make explicit collaborations of department of Mathematics, Biostatistics division of the Medical School at UCSD and Supercomputer Center in San Diego. Through dissemination of the results of this proposal, PI plans to expose biology to mathematics majors and promote science among underrepresented groups and women in mathematics.

该建议旨在回答相关问题，以确定稀疏的高维协变量空间的子集，在正则化方法的背景下，当简单的最小二乘损失函数是不是很适合。广义目标是理解统计模型的非线性和/或删失结构、正则化方案和问题的内在维度之间的基本相互作用。具体来说，PI旨在（1）识别新的统计问题和正则化方案，具有复杂的非线性和时间到事件结构，具有高维协变量空间，这些协变量空间被调整到数据的特征;（2）开发正则化估计量行为的新的非渐近预言界，其中随机矩阵理论的技术，特别是各种矩阵范数的高概率界，（3）研究半参数方法风险的新的非渐近界，其中统计模型可能被错误指定;（4）分析和开发使用删失率之间的特殊相互作用的模型，重要的是（5）引入新的算法来优化和有效地解决所研究的大规模问题。 全基因组关联研究，其中大量SNP的同时分析与发现复杂疾病的遗传鉴定有关。事件时间分量的存在和重要性要求在NP维度和删失结构的统计方法上取得重大进展。这项研究提案旨在为遗传学、公共卫生和生物信息科学中具有特殊影响的复杂数据开发创新和有效的统计方法，在这些科学中，审查和大量的基因相互作用使识别行为不端的基因变得非常困难。此外，这个跨学科项目的发展将促进新的科学发现，与从业者建立新的合作联系，并将促进研究生在应用于半参数模型和删失数据的当代最先进机器学习技术方面的教学和培训。为了促进科学的进步，PI将与加州大学圣地亚哥分校医学院数学系、生物统计学系和圣地亚哥超级计算机中心进行明确的合作。通过传播这一建议的结果，PI计划让数学专业的学生接触生物学，并在数学界代表性不足的群体和妇女中促进科学。