56 citations · 128 across the 12 of their papers we have counts for
10 papers · 1 filter
Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
Yiwei Gong, Anna L. Smith, Dena Asta +1
Many real-world networks exhibit hierarchical, tree-like structure and heavy-tailed degree distributions, phenomena not readily captured by standard statistical models for network…
Markov Random Fields: Structural Properties, Phase Transition, and Response Function Analysis
J. Brandon Carter, Catherine A. Calder
This paper presents a focused review of Markov random fields (MRFs)--commonly used probabilistic representations of spatial dependence in discrete spatial domains--for categorical…
Mixture of Directed Graphical Models for Discrete Spatial Random Fields
J. Brandon Carter, Catherine A. Calder
Current approaches for modeling discrete-valued outcomes associated with spatially-dependent areal units incur computational and theoretical challenges, especially in the Bayesian…
Statistical Inference for Complete and Incomplete Mobility Trajectories under the Flight-Pause Model
Marcin Jurek, Catherine A. Calder, Corwin Zigler
We formulate a statistical flight-pause model for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. W…
Restricted Spatial Regression Methods: Implications for Inference
Kori Khan, Catherine A. Calder
The issue of spatial confounding between the spatial random effect and the fixed effects in regression analyses has been identified as a concern in the statistical literature. Mult…
The Geometry of Continuous Latent Space Models for Network Data
Anna L. Smith, Dena M. Asta, Catherine A. Calder
We review the class of continuous latent space (statistical) models for network data, paying particular attention to the role of the geometry of the latent space. In these models,…
Empirical Reference Distributions for Networks of Different Size
Anna Smith, Catherine A. Calder, Christopher R. Browning
Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics…
Regression-based covariance functions for nonstationary spatial modeling
Mark D. Risser, Catherine A. Calder
In many environmental applications involving spatially-referenced data, limitations on the number and locations of observations motivate the need for practical and efficient models…
Bilinear Mixed-Effects Models for Affiliation Networks
Yanan Jia, Catherine A. Calder, Christopher R. Browning
An affiliation network is a particular type of two-mode social network that consists of a set of `actors' and a set of `events' where ties indicate an actor's participation in an e…
Bayesian Spatial Binary Classification
Candace Berrett, Catherine A. Calder
In analyses of spatially-referenced data, researchers often have one of two goals: to quantify relationships between a response variable and covariates while accounting for residua…