4 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…
Conditional Dirichlet Processes and Functional Condition Models
Jaeyong Lee, Kwangmin Lee, Jaegui Lee +1
In this paper, we study the conditional Dirichlet process (cDP) when a functional of a random distribution is specified. Specifically, we apply the cDP to the functional condition…
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…