3 papers
math.ST2026
Asymptotics for Model Selection in Probabilistic Principal Component Analysis
Mathias Drton, Andrew McCormack, Daniel Windisch
The probabilistic formulation of principal component analysis promises statistically grounded solutions to the problem of selecting the number of principal components. However, dev…
math.ST2025
Singular Learning Theory for Factor Analysis
Mathias Drton, Elizabeth Gross, Dimitra Kosta +4
Watanabe's singular learning theory provides a framework for asymptotic analysis of Bayesian model selection for statistical models with singularities, where traditional statistica…
math.AG2024
Classification of real hyperplane singularities by real log canonical thresholds
Dimitra Kosta, Daniel Windisch
The log canonical threshold (lct) is a fundamental invariant in birational geometry, essential for understanding the complexity of singularities in algebraic varieties. Its real co…