On Nonparanormal Likelihoods
Nonparanormal models describe multivariate responses via a latent Gaussian copula while allowing flexible nonparametric marginals, making them attractive when multivariate normality is questionable but interpretability is paramount. The paper presents suitable parameterisations, four novel nonparanormal log‑likelihood functions, and discusses optimisation challenges, including non‑convexity, biconvexity, and convex approximations, with a core contribution of a score function for multivariate normal log‑probabilities computed via Genz’ procedure. A demonstration includes transformation discrim…
Nonparanormal models describe multivariate responses via a latent Gaussian copula while allowing flexible nonparametric marginals, making them attractive when multivariate normality is questionable but interpretability is paramount. The paper presents suitable parameterisations, four novel nonparanormal log‑likelihood functions, and discusses optimisation challenges, including non‑convexity, biconvexity, and convex approximations, with a core contribution of a score function for multivariate normal log‑probabilities computed via Genz’ procedure. A demonstration includes transformation discrim…
Swiss AI can apply these interpretable, flexible multivariate modeling techniques to improve analysis of complex biomedical data.
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