A Flexible and Parsimonious Modelling Strategy for Clustered Data Analysis

TL;DR

This paper introduces a flexible yet parsimonious modeling strategy for clustered data that balances heterogeneity and homogeneity, with applications extending to transfer learning, supported by theoretical and empirical validation.

Contribution

It proposes a novel modeling approach that effectively balances flexibility and parsimony in clustered data analysis, with established asymptotic properties and demonstrated practical utility.

Findings

The proposed method accurately captures cluster heterogeneity and homogeneity.

Simulation studies show strong performance of the modeling strategy.

Real data analysis confirms practical applicability.

Abstract

Statistical modelling strategy is the key for success in data analysis. The trade-off between flexibility and parsimony plays a vital role in statistical modelling. In clustered data analysis, in order to account for the heterogeneity between the clusters, certain flexibility is necessary in the modelling, yet parsimony is also needed to guard against the complexity and account for the homogeneity among the clusters. In this paper, we propose a flexible and parsimonious modelling strategy for clustered data analysis. The strategy strikes a nice balance between flexibility and parsimony, and accounts for both heterogeneity and homogeneity well among the clusters, which often come with strong practical meanings. In fact, its usefulness has gone beyond clustered data analysis, it also sheds promising lights on transfer learning. An estimation procedure is developed for the unknowns in the resulting model, and asymptotic properties of the estimators are established. Intensive simulation studies are conducted to demonstrate how well the proposed methods work, and a real data analysis is also presented to illustrate how to apply the modelling strategy and associated estimation procedure to answer some real problems arising from real life.