CAREER: New Statistical Methods for Classification and Analysis of High Dimensional and Functional Data
职业:高维和功能数据分类和分析的新统计方法
基本信息
- 批准号:1812354
- 负责人:
- 金额:$ 12.42万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-08-17 至 2018-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Recent technology advances have generated data of unprecedented size and complexity across different scientific fields. To analyze such complex data, the principal investigator (PI) aims to develop new statistical methodologies. The PI proposes to study four interrelated research topics. First, the PI focuses on large-margin classification and proposes new large-margin classifiers to deliver competitive classification and conditional class probability estimation. He also proposes to address the question of whether a soft or hard classifier is preferred for a particular classification task and how to incorporate estimated conditional class probability to improve dimension reduction for data with a categorical response. Second, the PI proposes an extension of the least angle regression to deal with generalized linear models and, more generally, a strictly convex optimization problem. The new solution path is piecewise given by systems of ordinary differential equations and can be slightly modified to get the corresponding LASSO regularized solution path. Third, data with a sparse and irregular functional predictor are considered. New response-based dimensional reduction methods are proposed for such data using cumulative slicing and a viable scheme is also proposed to extend large-margin classifiers to analyze such data. Fourth, the PI focuses on the semi-parametric multi-index regression. By noticing that the Hessian operator filters out the effect of the linear component automatically, the PI provides a direct estimation scheme to estimate the space spanned by the multiple indices. The new scheme differs from existing methods in that it does not require estimating the nonparametric link while estimating the space spanned by the multiple indices as in other existing approaches.The proposed statistical methodology innovations are widely applicable in various fields. For example, the proposed new large-margin classifiers can be applied to analyze genomic data with a categorical response such as cancer type; new ordinary differential equation based solution path algorithms can used to analyze survival or binary genomic data to identify important predictors; while analyzing longitudinal data of aging, the new proposed statistical methods for sparse and irregular functional data will be useful. In order to facilitate the use of the proposed new methods, the PI will implement them in R or Matlab and make new software available to the public along with the corresponding research reports. The success of the proposed research will help to improve public health.
最近的技术进步产生了不同科学领域前所未有的规模和复杂性的数据。为了分析这些复杂的数据,主要研究者(PI)的目标是开发新的统计方法。PI建议研究四个相互关联的研究主题。首先,PI专注于大间隔分类,并提出新的大间隔分类器来提供竞争性分类和条件类概率估计。他还建议解决软分类器或硬分类器是否适合特定分类任务的问题,以及如何将估计的条件类概率用于改进具有分类响应的数据的降维。其次,PI提出了一个扩展的最小角度回归处理广义线性模型,更一般地说,一个严格的凸优化问题。新的解路径是由常微分方程组分段给出的,并且可以稍微修改以得到相应的LASSO正则化解路径。第三,考虑具有稀疏和不规则函数预测的数据。新的响应为基础的降维方法,提出了这样的数据,使用累积切片和一个可行的计划也提出了扩展的大间隔分类器来分析这样的数据。第四,PI侧重于半参数多指标回归。通过注意到Hessian算子自动过滤掉线性分量的影响,PI提供了一个直接的估计方案来估计多个指数所跨越的空间。与现有方法不同的是,新方法在估计多个指标所覆盖的空间时不需要估计非参数环节,所提出的统计方法学创新在各个领域都有广泛的应用。例如,所提出的新的大边缘分类器可以应用于分析具有分类响应的基因组数据,例如癌症类型;新的基于常微分方程的解路径算法可以用于分析生存或二进制基因组数据以识别重要的预测因子;在分析老化的纵向数据时,新提出的稀疏和不规则功能数据的统计方法将是有用的。为了便于使用拟议的新方法,PI将在R或Matlab中实现它们,并将新软件与相应的研究报告一起沿着向公众提供。这项研究的成功将有助于改善公众健康。
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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Yichao Wu其他文献
Soil phyllosilicate and iron oxide inhibit the quorum sensing of Chromobacterium violaceum
土壤页硅酸盐和氧化铁抑制紫色色杆菌的群体感应
- DOI:
10.1007/s42832-020-0051-5 - 发表时间:
2020-07 - 期刊:
- 影响因子:4
- 作者:
Shanshan Yang;Chenchen Qu;Manisha Mukherjee;Yichao Wu;Qiaoyun Huang;Peng Cai - 通讯作者:
Peng Cai
Research on damage and stress monitoring analysis of cement-based materials based on integrated sensing element (ISE)
基于集成传感元件(ISE)的水泥基材料损伤与应力监测分析研究
- DOI:
10.1016/j.cscm.2025.e04789 - 发表时间:
2025-07-01 - 期刊:
- 影响因子:6.600
- 作者:
Ming Sun;Weiwei Xu;Kaifeng Zheng;Yuanxing Wang;Weijian Ding;Jie Yao;Jianbin Zheng;Yichao Wu;Fengxia Xu - 通讯作者:
Fengxia Xu
Weighted NSFIB Aggregation With Generalized Next Hop of Strict Partial Order
具有严格偏序广义下一跳的加权 NSFIB 聚合
- DOI:
10.1109/tnsm.2022.3150389 - 发表时间:
2022-06 - 期刊:
- 影响因子:5.3
- 作者:
Qing Li;Yichao Wu;Jingpu Duan;Jiahai Yang;Yong Jiang - 通讯作者:
Yong Jiang
Extraction of extracellular polymeric substances (EPS) from red soils (Ultisols)
从红土(Ultisols)中提取细胞外聚合物(EPS)
- DOI:
10.1016/j.soilbio.2019.05.014 - 发表时间:
2019-08 - 期刊:
- 影响因子:9.7
- 作者:
Shuang Wang;Marc Redmile-Gordon;Monika Mortimer;Peng Cai;Yichao Wu;Caroline L. Peacock;Chunhui Gao;Qiaoyun Huang - 通讯作者:
Qiaoyun Huang
Probability approximations with applications in computational finance and computational biology
概率近似在计算金融和计算生物学中的应用
- DOI:
- 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
C. Ji;H. Hurd;Yichao Wu - 通讯作者:
Yichao Wu
Yichao Wu的其他文献
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{{ truncateString('Yichao Wu', 18)}}的其他基金
FRG: Collaborative Research: Mathematical and Statistical Analysis of Compressible Data on Compressive Networks
FRG:协作研究:压缩网络上可压缩数据的数学和统计分析
- 批准号:
2152070 - 财政年份:2022
- 资助金额:
$ 12.42万 - 项目类别:
Continuing Grant
Collaborative Research: A Fast Hierarchical Algorithm for Computing High Dimensional Truncated Multivariate Gaussian Probabilities and Expectations
协作研究:计算高维截断多元高斯概率和期望的快速分层算法
- 批准号:
1821171 - 财政年份:2018
- 资助金额:
$ 12.42万 - 项目类别:
Continuing Grant
CAREER: New Statistical Methods for Classification and Analysis of High Dimensional and Functional Data
职业:高维和功能数据分类和分析的新统计方法
- 批准号:
1055210 - 财政年份:2011
- 资助金额:
$ 12.42万 - 项目类别:
Continuing Grant
Development of Statistical Methods for High-dimensional and Complex Data
高维复杂数据统计方法发展
- 批准号:
0905561 - 财政年份:2009
- 资助金额:
$ 12.42万 - 项目类别:
Standard Grant
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