6 papers
A note on a local entropy condition in the Wasserstein space
Chang Jun Im, Jeong Min Jeon, Byeong U. Park
Entropy conditions are widely used in empirical-process analyses to establish asymptotic properties of M-estimators. In regression problems with responses taking values in a genera…
Local Fréchet Regression with Riemannian Predictors
Chang Jun Im, Jeong Min Jeon
Fréchet regression is well developed for Euclidean predictors, but local linear methods remain limited for general manifold-valued predictors. We propose local constant and local l…
Functional Principal Component Analysis for Manifold-Indexed Data
Chang Jun Im, Jeong Min Jeon
Functional principal component analysis (FPCA) is a central tool for dimension reduction and covariance analysis in functional data analysis. We study FPCA for discretely observed…
Local Fréchet regression with toroidal predictors
Chang Jun Im, Jeong Min Jeon
We provide the first regression framework that simultaneously accommodates responses taking values in a general metric space and predictors lying on a general torus. We propose int…
Hybrid deep additive neural networks
Gyu Min Kim, Jeong Min Jeon
Traditional neural networks (multi-layer perceptrons) have become an important tool in data science due to their success across a wide range of tasks. However, their performance is…
Local Fréchet regression with circular predictors
Chang Jun Im, Jeong Min Jeon
Fréchet regression extends the principles of linear regression to accommodate responses valued in generic metric spaces. While this approach has primarily focused on exploring rela…