Pursuing sources of heterogeneity in modeling clustered population
[1];
[4];
[6]
- Department of Statistics University of Connecticut Storrs Connecticut
- School of Statistics Jiangxi University of Finance and Economics Nanchang China
- Department of Biostatistics Yale School of Public Health New Haven Connecticut
- Department of Statistics University of California Riverside California
- Center for Population Health University of Connecticut Health Center Farmington Connecticut
- Department of Statistics University of Connecticut Storrs Connecticut, Center for Population Health University of Connecticut Health Center Farmington Connecticut
Abstract Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity , that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 1786923
- Journal Information:
- Biometrics, Journal Name: Biometrics Journal Issue: 2 Vol. 78; ISSN 0006-341X
- Publisher:
- Oxford University PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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