Theoretical foundations of inference in the presence of large numbers of nuisance parameters

存在大量干扰参数时推理的理论基础

• 批准号: EP/T01864X/1
• 负责人: Heather Battey
• 金额: $ 100.95万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT01864X%2F1
• 关键词: Theoretical; foundations; inference; presence; large

Any method of measurement should be calibrated or at least not highly miscalibrated. Statistical theory ensures such calibration for methods of inference, crucial tools for applied statisticians and scientists, thus making such procedures suitable for purpose. Specifically, in hypothetical repeated application, the proposed methods should give an answer within a small window of the truth. The present research is about calibrated inference for key quantities of interest, like the effect of a drug or treatment, in the presence of so called nuisance parameters. These are aspects of no direct concern or scientific relevance, but that are needed to complete the idealized representation of the physical, biological or sociological system. Large numbers of them arise naturally when one wishes to limit the strength of modelling assumptions in the equations describing the data generating process.On a national level, improved understanding of the scientific or societal truths underpinning the data we observe allows significant long term economic benefits. For instance, it allows costly medical screening or government regulation to be better targeted, and allows secure conclusions to be obtained from, say, studies into the efficacy of new drugs, treatment programs or vaccines.

任何测量方法都应该经过校准，或者至少不会出现严重的误校准。统计理论确保了推理方法的这种校准，这是应用统计学家和科学家的重要工具，从而使这些程序适合目的。具体而言，在假设的重复应用中，所提出的方法应该在真理的一个小窗口内给出答案。本研究是关于在存在所谓的滋扰参数的情况下对感兴趣的关键量（如药物或治疗的效果）的校准推断。这些都是没有直接关系或科学相关性的方面，但需要完成物理，生物或社会学系统的理想化表示。当人们希望限制描述数据生成过程的方程中的建模假设的强度时，自然会出现大量的问题。在国家层面上，对我们观察到的数据所依据的科学或社会真理的理解的提高可以带来显着的长期经济效益。例如，它可以使昂贵的医疗筛查或政府监管更有针对性，并可以从新药、治疗计划或疫苗的功效研究中获得可靠的结论。

项目成果

On partial likelihood and the construction of factorisable transformations

关于部分似然和可分解变换的构造

• DOI: 10.1007/s41884-022-00068-8
• 发表时间: 2022
• 期刊: Information Geometry
• 作者: Battey H

Some aspects of non-standard multivariate analysis

非标准多元分析的一些方面

• DOI: 10.1016/j.jmva.2021.104810
• 发表时间: 2022
• 期刊: Journal of Multivariate Analysis
• 影响因子: 1.6
• 作者: Battey H

On inference in high-dimensional regression

高维回归中的推理

• DOI: 10.1093/jrsssb/qkad001
• 发表时间: 2023
• 期刊: Statistical Methodology
• 作者: Battey H

Heather Battey's Contribution to the Discussion of 'Assumption-Lean Inference for Generalised Linear Model Parameters' by Vansteelandt and Dukes

Heather Battey 对 Vansteelandt 和 Dukes 的“广义线性模型参数的假设精益推理”讨论的贡献

• DOI: 10.1111/rssb.12517
• 发表时间: 2022
• 期刊: Statistical Methodology
• 作者: Battey H

Inducement of population sparsity

人口稀疏的诱因

• DOI: 10.1002/cjs.11751
• 发表时间: 2023
• 期刊: Canadian Journal of Statistics
• 作者: Battey H