Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data.

Graphical models have been widely used to explicitly capture the statistical relationships among the variables of interest in the form of a graph. The central question in these models is to infer significant conditional dependencies or independencies from high-dimensional data. In the current literature, it is common to assume that the high-dimensional data come from a homogeneous source and follow a parametric graphical model.

Statistical clustering of documents via stochastic blockmodels.

As the online market grows rapidly, people are relying more on product review when they purchase the product. Hence, many companies and researchers are interested in analyzing product review which essentially a text data. In the current literature, it is common to use only text analysis tools to analyze text dataset.

Extracting Data from the Electronic Health Record of Patients with ADHD Reveals Pediatricians' Discussions of Educational Support and Document Collection.

Primary care physicians (PCPs) have an important role in the identification and management of Attention Deficit Hyperactivity Disorder (ADHD). There is a paucity of research on PCPs' practices related to the discussion of educational interventions. We conducted a retrospective chart review using Natural Language Processing to extract data on how often PCPs in an outpatient clinic: 1) discuss educational support with patients and caregivers; and 2) obtain educational records.

Estimating Finite Mixtures of Ordinal Graphical Models.

Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the conditional relations among variables using graph theory. Despite recent advances, existing methods on graphical models usually assume a homogeneous population and focus on binary or continuous variables. However, ordinal variables are very popular in many areas of psychological science, and the population often consists of several different groups based on the heterogeneity in ordinal data.

Efficacy of microcurrent therapy for treatment of acute knee pain: A randomized double-blinded controlled clinical trial.

Objective: We would like to determine whether electrotherapy, specifically microcurrent therapy, increases function and decreases pain in people who have acute knee pain.

Design: Randomized, double-blinded, placebo-controlled clinical trial.

Setting: University laboratory and patient home.

Model-based clustering of time-evolving networks through temporal exponential-family random graph models.

Dynamic networks are a general language for describing time-evolving complex systems, and discrete time network models provide an emerging statistical technique for various applications. It is a fundamental research question to detect a set of nodes sharing similar connectivity patterns in time-evolving networks. Our work is primarily motivated by detecting groups based on interesting features of the time-evolving networks (e.

An Intersection Database Facilitates Access to Complex Signalized Intersections for Pedestrians with Vision Disabilities.

A growing number of intersections and crosswalks pose barriers to pedestrians with vision disabilities. This project investigated the effects of providing verbal descriptions of intersections and crosswalks on the performance of street-crossing subtasks by individuals who are totally blind. The authors designed an intersection database containing information relevant to crossing subtasks such as finding and aligning with the crosswalk, deciding when to cross, remaining in the crosswalk, and recognizing the end of a crossing.

A review of dynamic network models with latent variables.

We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. We begin with an overview of the static models, and then we introduce the dynamic extensions.