Flexible Statistical Modeling

灵活的统计建模

• 批准号: 2013736
• 负责人: Trevor Hastie
• 金额: $ 25万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013736&HistoricalAwards=false
• 关键词: Flexible; Statistical; Modeling

This project develops new statistical methodology for solving important applied problems in medicine and science. In personalized medicine, the PI will focus on the prediction of whether particular treatments are suitable for a patient based on their demographics and clinical history, using vast troves of past patient experiences. Biologists seek to understand the folding patterns of chromosomes in cells, a key ingredient in understanding their function. In a second project, the PI will develop novel curve-fitting methods to learn these folding patterns from indirect and noisy measurements of this three dimensional structure. Ecologists try to learn the characteristics of environments that attract certain species, as well as shared aspects of species that coinhabit environments, as a critical component in species survival, pest control and disease prevention. In a third project, the PI will develop methods that can scale to extremely large species populations (such as bacteria and insects) based on site-specific surveys. The project also provides research training opportunities for graduate students. The project develops validation methods to select from a collection of models for estimating heterogenous treatment effects, despite the fact that in observational data there are no direct measurements of the treatment effect. The project develops adaptive nearest-neighbor matching techniques to construct a comparison set for each validation point. With high-dimensional chromosomal contact maps, the PI plans to draw on his early work on principal curves to model the three-dimensional folding structure of chromosomes. This amounts to metric scaling with side information on the local structure of the three-dimensional solution. Generalized linear latent-variable models are popular for modeling species distributions (usually Poisson models for counts, and binomial models for presence/absence), but they grindto a halt when the number of species and/or locations is very large. The PI plans to adapt earlier work on matrix completion to develop alternating maximum-likelihood fitting algorithms to scale these methods to extremely large populations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目为解决医学和科学中的重要应用问题开发了新的统计方法。在个性化医疗方面，PI将利用大量过去患者的经验，根据患者的人口统计数据和临床病史，重点预测特定治疗是否适合患者。生物学家试图了解细胞中染色体的折叠模式，这是了解其功能的关键因素。在第二个项目中，PI将开发新的曲线拟合方法，通过间接和噪声测量三维结构来学习这些折叠模式。生态学家试图了解吸引某些物种的环境特征，以及物种与环境共存的共同方面，作为物种生存，病虫害控制和疾病预防的关键组成部分。在第三个项目中，PI将开发基于特定地点调查的方法，可以扩展到非常大的物种种群（如细菌和昆虫）。该项目还为研究生提供研究培训机会。尽管在观察数据中没有对治疗效果的直接测量，但该项目开发了从一组模型中选择评估异质性治疗效果的验证方法。该项目开发了自适应最近邻匹配技术，为每个验证点构建比较集。有了高维染色体接触图，PI计划利用他早期关于主曲线的工作来模拟染色体的三维折叠结构。这相当于带有三维解的局部结构侧信息的度量缩放。广义线性潜变量模型是常用的物种分布模型（通常是计数的泊松模型和存在/不存在的二项模型），但当物种数量和/或位置非常大时，它们就会停止。PI计划调整早期在矩阵补全方面的工作，开发交替最大似然拟合算法，将这些方法扩展到非常大的群体。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

LinCDE: Conditional Density Estimation via Lindsey's Method

LinCDE：通过 Lindsey 方法进行条件密度估计

• 发表时间: 2022
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Gao, Zijun;Hastie, T.
• 通讯作者: Hastie, T.