Individualized Group Learning
Individualized Group Learning
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DOI：
10.1080/01621459.2021.1947306
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发表时间：
2019-06
影响因子：
3.7
通讯作者：
Chencheng Cai;Rong Chen;Min‐ge Xie
中科院分区：
文献类型：
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作者：
Chencheng Cai;Rong Chen;Min‐ge Xie
摘要许多海量数据集是通过收集群体中大量个体的信息而组装起来的。对这些数据的分析，特别是在个性化推理和解决方案方面，有可能为实际应用创造重要价值。传统上，对数据集中的个体的推断要么仅仅依赖于个体的信息，要么通过总结关于整个群体的信息。然而，随着大数据的可用性，我们有机会，以及一个独特的挑战，使一个更有效的个性化的推理，同时考虑到人口信息和个人的差异。为了处理群体中可能存在的异质性，同时为数据集中的个体提供有效和可信的推理，本文开发了一种称为个性化群体学习（iGroup）的新方法。iGroup方法使用局部非参数技术，通过汇集群体中与目标个体具有相似特征的其他实体来生成个性化组，即使个体估计值因有限数量的观察而存在偏倚。讨论了iGroup的三种一般情形，并研究了它们的渐近性质.理论结果和实证模拟表明，通过应用iGroup，在个人层面上的统计推断的性能得到了保证，并可以从基于单独的个人信息或整个人口信息的推断得到大幅改善。该方法具有广泛的应用范围。并给出了一个金融统计的例子。
Abstract Many massive data sets are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the dataset is either solely relying on the information of the individual or from summarizing the information about the whole population. However, with the availability of big data, we have the opportunity, as well as a unique challenge, to make a more effective individualized inference that takes into consideration of both the population information and the individual discrepancy. To deal with the possible heterogeneity within the population while providing effective and credible inferences for individuals in a dataset, this article develops a new approach called the individualized group learning (iGroup). The iGroup approach uses local nonparametric techniques to generate an individualized group by pooling other entities in the population which share similar characteristics with the target individual, even when individual estimates are biased due to limited number of observations. Three general cases of iGroup are discussed, and their asymptotic performances are investigated. Both theoretical results and empirical simulations reveal that, by applying iGroup, the performance of statistical inference on the individual level are ensured and can be substantially improved from inference based on either solely individual information or entire population information. The method has a broad range of applications. An example in financial statistics is presented.