Nonparametric Prediction Distribution from Resolution-Wise Regression with Heterogeneous Data
Nonparametric Prediction Distribution from Resolution-Wise Regression with Heterogeneous Data
复制标题
异质数据的分辨率回归的非参数预测分布
DOI：
10.1080/07350015.2022.2115498
复制
发表时间：
2022
影响因子：
3
通讯作者：
Liu, Yufeng
中科院分区：
文献类型：
--
作者：
Li, Jialu;Zhang, Wan;Wang, Peiyao;Li, Qizhai;Zhang, Kai;Liu, Yufeng
由于个性化营销的快速发展，异构数据的建模和推理近年来引起了人们的极大兴趣。大多数现有的回归方法都是基于条件均值的，可能需要额外的聚类信息来适应数据的异质性。在本文中，我们提出了一种新的非参数分辨率回归过程，以提供响应的估计分布而不是单一值。我们通过将响应和预测的信息分别分解为基于边际二进制展开的分辨率和模式来实现这一点。分辨率和模式之间的关系通过惩罚逻辑回归建模。结合分辨率预测，我们提供了条件响应的直方图来近似分布。此外，我们还证明了该方法具有一定的独立筛选性和增长维数的一致性。仿真和一个房地产估值数据集进一步证明了该方法的有效性。
Modeling and inference for heterogeneous data have gained great interest recently due to rapid developments in personalized marketing. Most existing regression approaches are based on the conditional mean and may require additional cluster information to accommodate data heterogeneity. In this article, we propose a novel nonparametric resolution-wise regression procedure to provide an estimated distribution of the response instead of one single value. We achieve this by decomposing the information of the response and the predictors into resolutions and patterns, respectively, based on marginal binary expansions. The relationships between resolutions and patterns are modeled by penalized logistic regressions. Combining the resolution-wise prediction, we deliver a histogram of the conditional response to approximate the distribution. Moreover, we show a sure independence screening property and the consistency of the proposed method for growing dimensions. Simulations and a real estate valuation dataset further illustrate the effectiveness of the proposed method.
DOI：
10.1080/10618600.2020.1763808
发表时间：
2021
期刊：
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
影响因子：
--
作者：
Chen J;Tran-Dinh Q;Kosorok MR;Liu Y
通讯作者：
Liu Y
基于惩罚模型的聚类
DOI：
--
发表时间：
2009
期刊：
影响因子：
--
作者：
Wei Zhang;Kang Ling James
通讯作者：
Kang Ling James