Heterogeneity modeling and longitudinal clustering

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 modeling in 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 on the parameters corresponding to the individual nonparametric B-spline models, and thereby identify clusters based on different patterns of the predicted longitudinal curves. One advantage 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) algorithm which has the 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 in marketing 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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Zhu, Xiaolu

Contributors dc:contributor

• Qu, Annie
• Simpson, Douglas
• Narisetty, Naveen
• Zhu, Ruoqing

• Clustering
• Heterogeneity modeling
• Longitudinal data
• Subgrouping

Statement dc:rights

• Copyright 2017 Xiaolu Zhu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/98160

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/98160