In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused-l0\ l_0 \ penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington’s disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.
@article{mcdonnell2024,title={Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks},author={McDonnell, Erin I. and Xie, Shanghong and Marder, Karen and Cui, Fanyu and Wang, Yuanjia},journal={Statistics in Medicine},volume={1},number={17},year={2024},keywords={adaptive lasso, neighborhood selection, penalization methods, time-varying networks, undirected graphical models},doi={https://doi.org/10.1002/sim.10143},}
2021
JStatDistribApp
A flexible univariate moving average time-series model for dispersed count data
Kimberly F Sellers, Ali Arab, Sean Melville, and Fanyu Cui
Journal of Statistical Distributions and Applications, 2021
Al-Osh and Alzaid (1988) consider a Poisson moving average (PMA) model to describe the relation among integer-valued time series data; this model, however, is constrained by the underlying equi-dispersion assumption for count data (i.e., that the variance and the mean equal). This work instead introduces a flexible integer-valued moving average model for count data that contain over- or under-dispersion via the Conway-Maxwell-Poisson (CMP) distribution and related distributions. This first-order sum-of-Conway-Maxwell-Poissons moving average (SCMPMA(1)) model offers a generalizable construct that includes the PMA (among others) as a special case. We highlight the SCMPMA model properties and illustrate its flexibility via simulated data examples.
@article{sellers2021,title={A flexible univariate moving average time-series model for dispersed count data},author={Sellers, Kimberly F and Arab, Ali and Melville, Sean and Cui, Fanyu},journal={Journal of Statistical Distributions and Applications},volume={8},issue={1},pages={1--12},year={2021},publisher={SpringerOpen},doi={https://doi.org/10.1186/s40488-021-00115-2},}
2020
Undergrad Thesis
The Spillover Effect of Special Economic Zones on Foreign Direct Investment: Evidence from China