1,721,012 research outputs found
Introduzione ai metodi statistici per il credit scoring
No full textIl libro descrive in dettaglio gli strumenti statistici maggiormente utilizzati nel settore della valutazione e-ante del rischio di credito
Bayesian Inference for Graphical Factor Analysis Models
No full textWe generalize factor analysis models by allowing the concentration matrix of the residuals to have
nonzero off-diagonal elements. The resulting model is named graphical factor analysis model. Allowing a
structure of associations gives information about the correlation left unexplained by the unobserved variables,
which can be used both in the confirmatory and exploratory context. We first present a sufficient
condition for global identifiability of this class of models with a generic number of factors, thereby extending
the results in Stanghellini (1997) and Vicard (2000).We then consider the issue of model comparison
and show that fast local computations are possible for this purpose, if the conditional independence graphs
on the residuals are restricted to be decomposable and a Bayesian approach is adopted. To achieve this
aim, we propose a new reversible jump MCMC method to approximate the posterior probabilities of the
considered models. We then study the evolution of political democracy in 75 developing countries based
on eight measures of democracy in two different years
Graphical Factor Analysis Models: Specification and Model Comparison
No full textWe introduce a graphical factor Analysis Model as graphical Gaussian model with latent variables staisfying a set of conditional indepdndence assumptions. The study of the associations left unexplained by the latent factors allows a better interpretation of the model. A real data example is presented to clarify the ideas. We propose a MCMC method to approximate both the model probabilities and the inference on the quantities of interest
On the identification of discrete graphical models with hidden nodes
No full textWe focus on the identification of discrete undirected graphical
models with one unobserved binary variable and establish a
necessary and sufficient condition for the rank of the
transformation from the natural parameters to the parameters of
the model to be full. This ensures local identification of this
class of models. These models generalize the latent class model,
by allowing associations between the observed variables
conditionally on the latent one. The practical importance of this
issue is witnessed by several applied papers.
For non-full rank models, the obtained characterization allows us to
find the expression of the (sub)space where the identifiability
breaks down. Geometrically, this corresponds to the singularities in
the parameter space. This in turn allows us (a) to derive a reparametrization that leads to an identified model and (b) to compute the correct dimension of the model. The condition is based on the topology of the undirected graph associated with the model and relies on the faithfulness assumption
- …
