Author

D. Kim

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Preprint Sep 2026

A Stochastic EM Algorithm with Sampling-Importance Resampling for Missing Data in Regression with Nonlinear Predictors

Estimating regression models with nonlinear predictor transformations is challenging when data are missing, because nonlinearity typically renders the conditional distribution of the missing values intractable. Previous methods require specific nonlinear forms, such as polynomials or interactions, or rely on approximat...

D. Kim · 0 citations

Preprint Sep 2026

Identification of Nonlinear and Dependent Latent Factor Structure through Clique Search

Learning the structure of latent factor models involves two central challenges: (1) estimating the number of latent factors and (2) learning the support of the mapping from latent variables to observed variables. This is especially challenging for nonparametric regimes and nonlinear settings. We propose a method for la...

D. Kim, Qing Zhou · 0 citations