38 citations · 57 across the 9 of their papers we have counts for
7 papers · 1 filter
Analytic Standard Errors for Latent Gaussian Discrete-Valued Multivariate Time Series
Christopher M. Crawford, Marie-Christine Düker, Younghoon Kim +3
Unlike their continuous-valued counterparts, there are no universally preferred methodologies for modeling discrete-valued time series. This is especially problematic in fields suc…
Latent community paths in VAR-type models via dynamic directed spectral co-clustering
Younghoon Kim, Changryong Baek
This paper proposes a dynamic network framework for uncovering latent community paths in high-dimensional VAR-type models. By embedding a degree-corrected stochastic co-blockmodel…
Joint modeling and inference of multiple-subject high-dimensional sparse vector autoregressive models
Younghoon Kim, Zachary F. Fisher, Vladas Pipiras
The multiple-subject vector autoregression (multi-VAR) model captures heterogeneous network Granger causality across subjects by decomposing individual sparse VAR transition matric…
Structured Estimation of Heterogeneous Time Series
Zachary F. Fisher, Younghoon Kim, Vladas Pipiras +4
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al., (2022) introduced the mult…
Group integrative dynamic factor models with application to multiple subject brain connectivity
Younghoon Kim, Zachary F. Fisher, Vladas Pipiras
This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time series data, called GRoup Integrative DYnamic factor (GRIDY) mo…
Latent Gaussian dynamic factor modeling and forecasting for multivariate count time series
Younghoon Kim, Marie-Christine Düker, Zachary F. Fisher +1
This work considers estimation and forecasting in a multivariate, possibly high-dimensional count time series model constructed from a transformation of a latent Gaussian dynamic f…
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data
Zachary F. Fisher, Younghoon Kim, Barbara Fredrickson +1
Intensive Longitudinal Data (ILD) is increasingly available to social and behavioral scientists. With this increased availability come new opportunities for modeling and predicting…