Heterogeneity modeling and longitudinal clustering

Clustering;Heterogeneity modeling;Longitudinal data;Subgrouping

Zhu, Xiaolu

关键词: Clustering;    Heterogeneity modeling;    Longitudinal data;    Subgrouping;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/98160/ZHU-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Personalization has broad applications in many fields these days. Due to significant subject variations, it has become critical to incorporate subjects' heterogeneous characteristics in order to efficiently allocate personalized treatment or marketing strategies to tailor for subject specific needs.In this thesis, we develop several types of methods and theory to accommodate heterogeneity modelingin various personalization applications for longitudinal data. In the first application, we propose a personalized drug dosage recommendation scheme. Specifically, we model patients' heterogeneity using subject-specific random effects, and propose an adaptive procedure to estimate new patients' random effects and provide dosage recommendations for new patients over time. An advantage of our approach is that we do not impose any distribution assumption on estimating random effects. Moreover, the new approach can accommodate general time-varying covariates corresponding to random effects. We show that the proposed method is more efficient compared to existing approaches, especially when covariates are time-varying. In the second part of the thesis, we develop an efficient cluster analysis approach to subgroup longitudinal profiles using a penalized regression method. We utilize a pairwise-grouping penalization onthe parameters corresponding to the individual nonparametric B-spline models, and thereby identify clusters based on different patterns of the predicted longitudinal curves. Oneadvantage of the proposed method is that we approximate the longitudinal profiles and cluster trajectories into subgroups simultaneously. To implement the proposed method, we develop an alternating direction method of multipliers (ADMM) algorithmwhich hasthe desirable convergence property. In theory,we establish the consistency properties asymptotically. In addition, we show that our method outperforms the existing competitive approaches in our simulation studies and real data example. In the third part of the thesis, we are interested inmarketing segmentation, where customers are clustered into different subgroups due to their heterogeneous responses to the same marketing strategy. Specifically, we propose a pairwise subgrouping approach to identify and categorize similar marketing effects into subgroups. We model customers' purchase decisions as binary responses under the generalized linear model framework and incorporate their longitudinal correlation. We impose penalization on pairwise distances of individual effects to formulate subgroups, where different subgroups are associated with different marketing effects. In theory, we establish the consistency of subgroup identification in the sense that the true underlying segmentation structure can be recovered successfully, in addition to model estimation consistency. We apply the proposed approach to a real data application using IRI marketing data on in-store display marketing effects, where the proposed method performs favorably in terms of subgrouping identification and effects estimation.