Inference for High Dimensional Quantile Regression

高维分位数回归的推理

基本信息

  • 批准号:
    1712760
  • 负责人:
  • 金额:
    $ 12.5万
  • 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:
    2017-09-01 至 2022-08-31
  • 项目状态:
    已结题

项目摘要

Quantile regression is emerging as an important and active research area driven by diverse applications. Compared with the conventional least squares regression, quantile regression methods are robust against outliers and can capture heterogeneity. In recent years, significant results related to estimation and variable selection for quantile regression have been obtained for big data applications. However, there exists little work on inferential methods including hypothesis testing and confidence interval construction for quantile regression in the high-dimensional setting. This project seeks to develop new statistical theory, methodology and algorithms to address inference problems in high-dimensional quantile regression. The research is motivated by a large clinical trial of diabetes intervention, and the developed methods can also be applied to data from genome-wide association studies, neuroscience, and environmental studies. The research will focus on two main directions. First, new testing procedures will be developed to assess the overall significance of high-dimensional covariates on quantiles of the response distribution. Two types of tests are proposed, including a maximum-type statistic based on marginal quantile regression, and a score-type statistic. Theory and methods for both fixed and diverging dimensions will be studied. Second, focusing on high-dimensional quantile regression without the conventional minimum signal strength condition on the coefficients, the PI will rigorously study the asymptotic theory of penalized estimators and develop valid post-selection inference methods based on both asymptotic theory and bootstrap procedures. The PI will integrate research and education by developing advanced topics courses, engaging graduate and undergraduate students, especially those from under-represented groups, in the project, and reaching out to K-12 students and developing countries through collaboration and knowledge sharing.
在各种应用的推动下,分位数回归正在成为一个重要且活跃的研究领域。与传统的最小二乘回归相比,分位数回归方法对异常值具有鲁棒性,并且可以捕获异质性。 近年来,在大数据应用中,与分位数回归的估计和变量选择相关的重要成果已经获得。然而,关于高维环境中分位数回归的假设检验和置信区间构建等推理方法的研究很少。 该项目旨在开发新的统计理论、方法和算法来解决高维分位数回归中的推理问题。 该研究的动机是糖尿病干预的大型临床试验,所开发的方法也可以应用于全基因组关联研究、神经科学和环境研究的数据。 研究将集中在两个主要方向。首先,将开发新的测试程序来评估响应分布分位数的高维协变量的整体显着性。提出了两种类型的检验,包括基于边际分位数回归的最大型统计量和分数型统计量。 将研究固定维度和发散维度的理论和方法。其次,重点关注高维分位数回归,而无需传统的系数最小信号强度条件,PI将严格研究惩罚估计量的渐近理论,并基于渐近理论和自举程序开发有效的后选择推理方法。 PI 将通过开发高级主题课程、吸引研究生和本科生(尤其是来自弱势群体的学生)参与该项目,并通过合作和知识共享接触 K-12 学生和发展中国家,从而将研究和教育结合起来。

项目成果

期刊论文数量(13)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
EXTREMAL LINEAR QUANTILE REGRESSION WITH WEIBULL-TYPE TAILS
具有威布尔型尾部的极值线性分位数回归
  • DOI:
    10.5705/ss.202018.0073
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    1.4
  • 作者:
    He Fengyang;Wang Huixia Judy;Tong Tiejun
  • 通讯作者:
    Tong Tiejun
Copula-Based Semiparametric Models for Spatiotemporal Data
基于 Copula 的时空数据半参数模型
  • DOI:
    10.1111/biom.13066
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Tang, Yanlin;Wang, Huixia J.;Sun, Ying;Hering, Amanda S.
  • 通讯作者:
    Hering, Amanda S.
Copula‐based semiparametric analysis for time series data with detection limits
基于 Copula 的半参数分析,用于具有检测限的时间序列数据
  • DOI:
    10.1002/cjs.11503
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Li, Fuyuan;Tang, Yanlin;Wang, Huixia Judy
  • 通讯作者:
    Wang, Huixia Judy
Spatial cluster detection with threshold quantile regression
  • DOI:
    10.1002/env.2696
  • 发表时间:
    2021-07
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Junho Lee;Ying Sun;Huixia Judy Wang
  • 通讯作者:
    Junho Lee;Ying Sun;Huixia Judy Wang
Sparse Learning and Structure Identification for Ultrahigh-Dimensional Image-on-Scalar Regression
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Feifang Hu其他文献

Response-adaptive treatment randomization for multiple comparisons of treatments with recurrentevent responses
反应适应性治疗随机化,用于治疗与复发事件反应的多重比较
  • DOI:
    10.1177/09622802221095244
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Jingya Gao;Feifang Hu;Siu Hung Cheung;Pei-Fang Su
  • 通讯作者:
    Pei-Fang Su
Adaptive treatment allocation for comparative clinical studies with recurrent events data
使用复发事件数据进行比较临床研究的适应性治疗分配
  • DOI:
    10.1111/biom.13117
  • 发表时间:
    2019-09
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Jingya Gao;Pei‐Fang Su;Feifang Hu;Siu Hung Cheung
  • 通讯作者:
    Siu Hung Cheung
Statistical inference of adaptive randomized clinical trials for personalized medicine
个性化医疗适应性随机临床试验的统计推断
  • DOI:
    10.4155/cli.15.15
  • 发表时间:
    2015-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Feifang Hu;Yanqing Hu;Wei Ma;Lixin Zhang;Hongjian Zhu
  • 通讯作者:
    Hongjian Zhu
AI-Generated Synthetic Patient Data Helps in Evaluating Daratumumab Treatment Benefit in Multiple Myeloma Subgroups
  • DOI:
    10.1182/blood-2024-208174
  • 发表时间:
    2024-11-05
  • 期刊:
  • 影响因子:
  • 作者:
    Merav Bar;Andrew J. Cowan;Qian Shi;Zixuan Zhao;Zexin Ren;Feifang Hu;Will Ma
  • 通讯作者:
    Will Ma
Optimal responses-adaptive designs based on efficiency, ethic, and cost
基于效率、道德和成本的最佳响应自适应设计
  • DOI:
    10.4310/sii.2018.v11.n1.a9
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0.8
  • 作者:
    Chen Feng;Feifang Hu
  • 通讯作者:
    Feifang Hu

Feifang Hu的其他文献

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{{ truncateString('Feifang Hu', 18)}}的其他基金

New Covariate-Adjusted Response-Adaptive Designs and Associated Methods for Statistical Inference
新的协变量调整响应自适应设计和相关统计推断方法
  • 批准号:
    1612970
  • 财政年份:
    2016
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
CAREER: A new and pragmatic framework for modeling and predicting conditional quantiles in data-sparse regions
职业:一种新的实用框架,用于在数据稀疏区域建模和预测条件分位数
  • 批准号:
    1525692
  • 财政年份:
    2014
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Adaptive Design Based upon Covariate Information: New Designs and Their Properties
基于协变量信息的自适应设计:新设计及其属性
  • 批准号:
    1442192
  • 财政年份:
    2013
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
Adaptive Design Based upon Covariate Information: New Designs and Their Properties
基于协变量信息的自适应设计:新设计及其属性
  • 批准号:
    1209164
  • 财政年份:
    2012
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
New Developments in Estimation, Selection and Applications for Mixed Models
混合模型估计、选择和应用的新进展
  • 批准号:
    0906661
  • 财政年份:
    2009
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
Adaptive Designs and Sequential Monitoring
自适应设计和顺序监控
  • 批准号:
    0907297
  • 财政年份:
    2009
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant
CAREER: Use of Covariate Information in Adaptive Designs
职业:在自适应设计中使用协变量信息
  • 批准号:
    0349048
  • 财政年份:
    2004
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Continuing Grant
Power, Variability, and Optimality in Adaptive Designs
自适应设计中的强大功能、可变性和最优性
  • 批准号:
    0204232
  • 财政年份:
    2002
  • 资助金额:
    $ 12.5万
  • 项目类别:
    Standard Grant

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