The task to write on data analysis on nonstandard spaces is quite substantial, with a huge
body of literature to cover, from parametric to nonparametrics, from shape spaces to …

We analyze the problem of maximum likelihood estimation for Gaussian distributions that
are multivariate totally positive of order two (MTP_2). By exploiting connections to …

We consider the problem of learning the structure of a causal directed acyclic graph (DAG)
model in the presence of latent variables. We define" latent factor causal models"(LFCMs) as …

The paper gives an overview of recent advances in structural equation modeling. A
structural equation model is a multivariate statistical model that is determined by a mixed …

We consider the problem of testing a null hypothesis defined by polynomial equality and
inequality constraints on a statistical parameter. Testing such hypotheses can be …

This chapter offers a concise introduction to the theory of latent tree models. It highlights the
role of tree metrics in the structural description of this model class, in designing learning …

The recently introduced wald space models phylogenetic trees from an evolutionary
perspective. We show that it is a stratified space and propose algorithms to compute …

We consider the problem of structure recovery in a graphical model of a tree where some
variables are latent. Specifically, we focus on the Gaussian case, which can be reformulated …

We consider general Gaussian latent tree models in which the observed variables are not
restricted to be leaves of the tree. Extending related recent work, we give a full semi …

Testing the goodness-of-fit of a model with its defining functional constraints in the
parameters could date back to Spearman (1927), who analyzed the famous" tetrad" …