6 papers
On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study
Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory Goldstein
Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstream generalization remains poorly understood. In this work, we develop…
New Confidence Regions for Linear Regression Parameters with Stationary-Ergodic Dependent Errors
Mous-Abou Hamadou, Martial Longla, Mathias Nthiani Muia +1
We develop joint confidence regions for linear regression coefficients when the regressors and errors are jointly stationary and ergodic with unspecified serial dependence. The met…
Penalized KLIC Model Selection for the Generalized Method of Moments in Longitudinal Data with Time-Dependent Covariates
Mahmud Hasan, Mathias Nthiani Muia, Mous-Abou Hamadou +1
Model selection plays an important role in longitudinal data analysis, especially when models are estimated using the generalized method of moments (GMM) in the presence of time-de…
Uniform Asymptotic Theory for Local Likelihood Estimation of Covariate-Dependent Copula Parameters
Mathias Nthiani Muia
Conditional copula models allow dependence structures to vary with observed covariates while preserving a separation between marginal behavior and association. We study the uniform…
Kernel Smoothing for Bounded Copula Densities
Mathias N. Muia, Olivia Atutey, Mahmud Hasan
Nonparametric estimation of copula density functions using kernel estimators presents significant challenges. One issue is the potential unboundedness of certain copula density fun…
A Point on Discrete versus Continuous State-Space Markov Chains
Mathias N. Muia, Martial Longla
This paper examines the impact of discrete marginal distributions on copula-based Markov chains. We present results on mixing and parameter estimation for a copula-based Markov cha…