Sparse predictors in functional data analysis
函数数据分析中的稀疏预测变量
基本信息
- 批准号:0806088
- 负责人:
- 金额:$ 19.06万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2008
- 资助国家:美国
- 起止时间:2008-07-01 至 2012-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Proposed research is motivated from the discrimination task with high dimension, low sample size data. The investigator studies the intrinsic difficulties of the discrimination problem by exploring asymptotic geometric structure of such data. Three main activities are proposed: a) the asymptotic inconsistency of leave-one-out cross-validation. The study is expected to explain why it shall fail when the number of variables greatly exceeds the number of observations; b) the effect of the relationship between the dimensionality and the sample size on the difficulty of discrimination task; c) a discriminant direction vector that only exists for the data with high dimension, low sample size. The data points collapse on this direction vector and also are most separated by group labels. The investigator explores its various theoretical and empirical properties such as its optimality, uniqueness, and asymptotic performances.Even though these topics are loosely related one another in their technical aspects, their goals are essentially the same: exploring the nontraditional and unique challenges in high dimension, low sample size discrimination. While it has been an actively researched area over recent years, however, understanding fundamental challenges of high dimension, low sample size problems is yet satisfactory. This research approaches this problem in a way that may be regarded atypical in a traditional sense, but is more relevant to the problem itself. The applications of proposed research include text document classification such as Spam email filter, medical imaging such as functional magnetic resonance imaging, and bioinformatics such as microarray gene expression and proteomics.
本文的研究来源于高维、低样本量数据的判别任务。研究人员通过探索这些数据的渐近几何结构来研究识别问题的内在困难。提出了三个主要活动:a)留一法交叉验证的渐近不一致性。本研究期望解释当变量数目大大超过观察数目时,判别失败的原因;b)维度与样本量之间的关系对判别任务难度的影响;c)仅对高维、低样本量的数据存在的判别方向向量。数据点在此方向向量上折叠,并且也最大程度上由组标签分隔。研究人员探索了它的各种理论和经验性质,如它的最优性、唯一性和渐近性能。尽管这些主题在技术方面彼此松散关联,但它们的目标本质上是相同的:探索高维、低样本容量区分的非传统和独特的挑战。然而,尽管近年来它一直是一个活跃的研究领域,但对高维、低样本量问题的基本挑战的理解仍然令人满意。这项研究以一种在传统意义上可能被视为非典型的方式来处理这个问题,但更与问题本身相关。所提出的研究的应用包括文本文档分类,如垃圾邮件过滤,医学成像,如功能磁共振成像,以及生物信息学,如微阵列基因表达和蛋白质组学。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ian McKeague其他文献
Maternal C-reactive protein levels in pregnancy and its association with growth trajectories of head circumference in infants
- DOI:
10.1016/j.bbi.2024.01.031 - 发表时间:
2023-11-01 - 期刊:
- 影响因子:
- 作者:
Ezra Aydin;Marisa Spann;Keely Cheslack-Postava;Andre Sourander;Emmi Heinonen;Bin Cheng;Ian McKeague;Alan Brown - 通讯作者:
Alan Brown
Was there ice along the shore of the Sea of Galilee during the last 12,000?—Reply to a comment by Prange et al. (2007) and a comment by Friedman (2007)
- DOI:
10.1007/s10933-007-9137-7 - 发表时间:
2007-10-02 - 期刊:
- 影响因子:1.300
- 作者:
Doron Nof;Ian McKeague;Nathan Paldor - 通讯作者:
Nathan Paldor
Maternal C-reactive protein levels in pregnancy and its association with growth trajectories of head circumference in infants
- DOI:
10.1016/j.bbi.2024.01.049 - 发表时间:
2023-11-01 - 期刊:
- 影响因子:
- 作者:
Ezra Aydin;Marisa Spann;Keely Cheslack-Postava;Andre Sourander;Emmi Heinonen;Bin Cheng;Ian McKeague;Alan Brown - 通讯作者:
Alan Brown
Is There a Paleolimnological Explanation for ‘Walking on Water’ in the Sea of Galilee?
- DOI:
10.1007/s10933-005-1996-1 - 发表时间:
2006-04-01 - 期刊:
- 影响因子:1.300
- 作者:
Doron Nof;Ian McKeague;Nathan Paldor - 通讯作者:
Nathan Paldor
Ian McKeague的其他文献
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{{ truncateString('Ian McKeague', 18)}}的其他基金
Optimal treatment policies and adaptive screening for functional predictors
最佳治疗政策和功能预测因子的适应性筛查
- 批准号:
1307838 - 财政年份:2013
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
Bayesian, Empirical Likelihood and Counting Process Methods for Semiparametric Models
半参数模型的贝叶斯、经验似然和计数过程方法
- 批准号:
0204688 - 财政年份:2002
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
Collaborative Research: CMG: Ocean Circulation Climatology and Dynamics Using Bayesian Hierarchical Methods
合作研究:CMG:使用贝叶斯分层方法的海洋环流气候学和动力学
- 批准号:
0222244 - 财政年份:2002
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
Efficient Condensation of Spatial/Temporal Data
空间/时间数据的高效压缩
- 批准号:
9971784 - 财政年份:1999
- 资助金额:
$ 19.06万 - 项目类别:
Continuing Grant
Empirically Determined Climate Predictability Using Nonlinear Time Series Models
使用非线性时间序列模型根据经验确定气候可预测性
- 批准号:
9417528 - 财政年份:1995
- 资助金额:
$ 19.06万 - 项目类别:
Standard Grant
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