Simultaneous clustering of individuals and covariates for high-dimensional longitudinal data

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Australian & New Zealand Journal of Statistics Pub Date : 2025-02-07 DOI:10.1111/anzs.12437

Chao Han, Jiaqi Wu, Weiping Zhang

引用次数: 0

Abstract

This paper considers identifying and estimating high-dimensional longitudinal data models with latent subgroups and clustered covariates. We propose a regularised approach to recover group structures while simultaneously detecting clusters of significant covariates. The consistency and asymptotic normality are established for the estimator under mild conditions. Besides, we develop an effective algorithm based on local quadratic approximation to optimise the objective function. The finite-sample performance is illustrated through extensive simulations, and the proposed method is applied to study the shift in the economic structure of European countries before and after the debt crisis.

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高维纵向数据中个体和协变量的同时聚类

本文研究了具有潜在子群和聚类协变量的高维纵向数据模型的识别和估计。我们提出了一种正则化的方法来恢复群体结构，同时检测显著协变量的集群。在温和条件下，建立了估计量的相合性和渐近正态性。此外，我们还开发了一种基于局部二次逼近的有效算法来优化目标函数。通过大量的模拟来说明有限样本绩效，并将所提出的方法应用于研究欧洲国家在债务危机前后的经济结构变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Australian & New Zealand Journal of Statistics 数学-统计学与概率论

CiteScore

1.30

自引率

9.10%

发文量

审稿时长

>12 weeks

期刊介绍： The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.