2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…
Rational Maximum Likelihood Estimators of Kronecker Covariance Matrices
Mathias Drton, Alexandros Grosdos, Andrew McCormack
As is the case for many curved exponential families, the computation of maximum likelihood estimates in a multivariate normal model with a Kronecker covariance structure is typical…