Optimal Shrinkage and Empirical Bayes Prediction under Asymmetry, Censoring and Nonexchangeability

不对称、审查和不可交换性下的最优收缩和经验贝叶斯预测

• 批准号: 1811866
• 负责人: Gourab Mukherjee
• 金额: $ 12.74万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811866&HistoricalAwards=false
• 关键词: Optimal; Shrinkage; Empirical; Bayes; Prediction

In every branch of big-data analytics, it is now commonplace to use notions of shrinkage in the construction of robust algorithms and predictors. The concept of shrinkage is important because it provides an elegant framework for combining information from related populations and often leads to substantial improvements in the performances of algorithms used for simultaneous inference. Driven by applications in a wide range of scientific problems over the last decade, the traditional roles of statistical shrinkage have rapidly evolved as new perspectives have been introduced to address and exploit complex, latent structural properties of modern datasets. These new applications often involve non-standard inferential attributes such as asymmetric predictive objectives as well as intricate modeling caveats, such as nonexchangeable prior structures and censored observations. These new age statistical problems pose challenges not only in developing flexible shrinkage algorithms but also in optimally tuning them to obtain efficient shrinkage properties. This project will develop new empirical Bayes predictive methods that possess optimal shrinkage properties and can produce significant enhancements over existing algorithms built on the mathematical convenience of symmetric loss functions and exchangeable prior structures.The common theme underlying this project is that of using optimal shrinkage properties to develop efficient predictive methods. Existing shrinkage algorithms rely heavily on decision theoretic identities that break down under asymmetry and nonexchangeability, and so, there is an urgent need to develop new statistical methodologies, theories, and algorithms. The PI will develop new conditionally linear decision rules for prediction under asymmetry in nonexchangeable Gaussian hierarchical models and will extend the proposed methodologies to non-Gaussian models as well as to settings with non-linear structural constraints. Additionally, optimal empirical Bayes rules will be developed that will work with censored data and can be used for prediction in multi-stage decision making scenarios with asymmetric objectives. The results developed in this project will provide practitioners with an improved understanding of where existing prediction approaches fail under asymmetry, censoring, and nonexchangeability and why algorithms specifically developed to operate under these conditions should be used.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.

在大数据分析的每一个分支中，在构建强大的算法和预测器时使用收缩的概念现在是司空见惯的。收缩的概念是重要的，因为它提供了一个优雅的框架，从相关人群的信息相结合，并经常导致显着改善的性能，用于同时推理的算法。在过去十年中，在广泛的科学问题中的应用的驱动下，统计收缩的传统作用迅速发展，因为新的观点已经被引入来解决和利用现代数据集的复杂的潜在结构特性。这些新的应用通常涉及非标准的推理属性，如非对称预测目标以及复杂的建模警告，如不可交换的先验结构和删失观测。这些新的年龄统计问题提出了挑战，不仅在开发灵活的收缩算法，而且在优化调整它们，以获得有效的收缩性能。该项目将开发新的经验贝叶斯预测方法，具有最佳的收缩性能，并可以产生显着增强现有的算法建立在对称损失函数和可交换的先验结构的数学便利。该项目的共同主题是使用最佳的收缩性能，以开发有效的预测方法。现有的收缩算法在很大程度上依赖于决策理论的身份，打破下不对称和nonexchangeries，因此，迫切需要开发新的统计方法，理论和算法。PI将开发新的条件线性决策规则，用于非交换高斯分层模型中不对称条件下的预测，并将所提出的方法扩展到非高斯模型以及具有非线性结构约束的设置。此外，将开发最佳经验贝叶斯规则，这些规则将适用于删失数据，并可用于具有非对称目标的多阶段决策情景中的预测。在这个项目中开发的结果将为从业者提供一个更好的理解，现有的预测方法在不对称，审查和nonexchangeries下失败，以及为什么应该使用专门开发的算法在这些条件下运行。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

On discrete priors and sparse minimax optimal predictive densities

关于离散先验和稀疏极小极大最优预测密度

• DOI: 10.1214/21-ejs1818
• 发表时间: 2021
• 期刊: Electronic Journal of Statistics
• 影响因子: 1.1
• 作者: Gangopadhyay, Ujan;Mukherjee, Gourab
• 通讯作者: Mukherjee, Gourab

The Use of Single Cell Mass Cytometry to Define the Molecular Mechanisms of Varicella-Zoster Virus Lymphotropism

使用单细胞质量流式细胞仪确定水痘带状疱疹病毒趋淋巴细胞性的分子机制

• DOI: 10.3389/fmicb.2020.01224
• 发表时间: 2020
• 期刊: Frontiers in Microbiology
• 影响因子: 5.2
• 作者: Sen, Nandini;Mukherjee, Gourab;Arvin, Ann M.
• 通讯作者: Arvin, Ann M.

Improved Shrinkage Prediction under a Spiked Covariance Structure

尖峰协方差结构下改进的收缩预测

• 发表时间: 2021
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Banerjee, Trambak;Mukherjee, Gourab;Paul, Debashis
• 通讯作者: Paul, Debashis

HIV efficiently infects T cells from the endometrium and remodels them to promote systemic viral spread

• DOI: 10.7554/elife.55487
• 发表时间: 2020-05-26
• 期刊: ELIFE
• 影响因子: 7.7
• 作者: Ma, Tongcui;Luo, Xiaoyu;Roan, Nadia R.
• 通讯作者: Roan, Nadia R.

A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games

• DOI: 10.1080/01621459.2019.1611584
• 发表时间: 2019-06
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Trambak Banerjee;Gourab Mukherjee;S. Dutta;Pulak Ghosh