5 papers
Bayesian Estimation of the Eigenstructure in High-Dimensional Approximate Factor Models
Seongmin Kim, Jaeyong Lee
High-dimensional economic datasets often display strong co-movement driven by a small number of latent factors, which are typically modeled using approximate factor models. When th…
Bayesian Node-Level Outlier Detection for Graph Signals
Seongmin Kim, Kyusoon Kim
This paper proposes a fully Bayesian framework for node-level outlier detection in graph signals, where measurements are observed on the nodes of an underlying graph. Unlike tradit…
Eigenstructure inference for high-dimensional covariance with generalized shrinkage inverse-Wishart prior
Seongmin Kim, Kwangmin Lee, Sewon Park +1
In multivariate statistics, estimating the covariance matrix is essential for understanding the interdependence among variables. In high-dimensional settings, where the number of c…
Bayesian Analysis of Spiked Covariance Models: Correcting Eigenvalue Bias and Determining the Number of Spikes
Kwangmin Lee, Sewon Park, Seongmin Kim +1
We study Bayesian inference in the spiked covariance model, where a small number of spiked eigenvalues dominate the spectrum. Our goal is to infer the spiked eigenvalues, their cor…
Bayesian Bootstrap based Gaussian Copula Model for Mixed Data with High Missing Rates
Seongmin Kim, Jeunghun Oh, Hungkuk Ko +2
Missing data is a common issue in various fields such as medicine, social sciences, and natural sciences, and it poses significant challenges for accurate statistical analysis. Alt…