Understanding Regression Heterogeneity Through Joint Estimation of Conditional Quantiles
通过条件分位数的联合估计了解回归异质性
基本信息
- 批准号:1613173
- 负责人:
- 金额:$ 15万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-08-01 至 2019-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In many data-driven scientific investigations, the primary goal is to understand the relationship between a response variable and a set of predictors. Standard statistical techniques attempt to detect and quantify the nature of such relationships through changes in the average response. However, in the real world, predictor-response relationships are often more complex and nuanced. In disciplines like climate science, ecology, economics, public health and sociology, investigators are often interested in understanding changes to the extreme response percentiles. Additional insights are gained by quantifying how the rate of change varies as one moves from the average to the extremes. This project aims to develop sophisticated and theoretically sound statistical tools that can answer these questions from large and complex data sets. Statistical tools are sought within the recently popularized modeling framework of linear quantile regression. The proposed framework expands the scope of linear quantile regression to scenarios where the response variables exhibit additional dependency. Such dependency manifests in many common situations, e.g., when one simultaneously measures multiple response variables per observation unit, when a response is measured repeatedly over time, or, when data is drawn from a network of individuals. Standing between the promise of quantile regression and its wider applicability is the lack of a proper model-based estimation framework. The PI has recently introduced a modeling framework that leads to Bayesian or penalized likelihood based joint estimation of linear quantile planes over arbitrary predictor spaces. Proposed model extensions augment this framework with autoregressive and copula formulations to address various kinds of structural dependency between the observation units. The project will develop efficient inference algorithms using advanced Bayesian techniques based on stochastic computation, and public, open source software in the form of R packages. Software development will incorporate possible leveraging of distributed computing architectures to render scalability to big data. For all model extensions, the PI will also carry out sharp analyses of theoretical guarantees on model performance by working out the large sample distribution theory of Bayesian parameter estimates.
在许多数据驱动的科学调查中,主要目标是了解响应变量和一组预测变量之间的关系。 标准的统计技术试图通过平均响应的变化来检测和量化这种关系的性质。 然而,在真实的世界中,预测-响应关系往往更加复杂和微妙。 在气候科学、生态学、经济学、公共卫生和社会学等学科中,研究人员通常对了解极端反应的变化感兴趣。 通过量化变化率如何随着从平均值到极端值的变化而变化,可以获得更多的见解。 该项目旨在开发先进的、理论上合理的统计工具,从大型复杂的数据集中回答这些问题。在最近流行的线性分位数回归建模框架内寻求统计工具。 所提出的框架扩展了线性分位数回归的范围,响应变量表现出额外的依赖性的情况下。 这种依赖性表现在许多常见的情况下,例如,当同时测量每个观察单位的多个响应变量时,当响应随时间重复测量时,或者当数据从个体网络中提取时。 分位数回归的承诺和其更广泛的适用性之间的立场是缺乏一个适当的基于模型的估计框架。PI最近引入了一个建模框架,导致贝叶斯或惩罚似然联合估计的线性分位数平面在任意预测空间。提出的模型扩展增强了这个框架与自回归和copula公式,以解决各种结构之间的依赖关系的观察单位。该项目将使用基于随机计算的高级贝叶斯技术和R包形式的公共开源软件开发高效的推理算法。 软件开发将结合分布式计算架构的可能利用,以提供大数据的可扩展性。对于所有模型扩展,PI还将通过计算贝叶斯参数估计的大样本分布理论,对模型性能的理论保证进行尖锐的分析。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A vignette on model-based quantile regression: analysing excess zero response
基于模型的分位数回归的小插图:分析过量的零响应
- DOI:10.1016/b978-0-12-815862-3.00008-1
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Cunningham, Erika;Tokdar, Surya T;Clark, James S.
- 通讯作者:Clark, James S.
Joint quantile regression for spatial data
- DOI:10.1111/rssb.12467
- 发表时间:2021-08-23
- 期刊:
- 影响因子:5.8
- 作者:Chen, Xu;Tokdar, Surya T.
- 通讯作者:Tokdar, Surya T.
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Surya Tokdar其他文献
Surya Tokdar的其他文献
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{{ truncateString('Surya Tokdar', 18)}}的其他基金
Analyzing Dependent Extremes via Joint Quantile Regression
通过联合分位数回归分析相关极值
- 批准号:
2014861 - 财政年份:2020
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
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