6,047 research outputs found
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
Identification of principal causal effects using secondary outcomes.
No full textUnless strong assumptions are made, identification of principal causal effects
in causal studies can only be partial and bounds (or sets) for the causal effects
are established. In the presence of a secondary outcome, recent results exist to
sharpen the bounds that exploit conditional independence assumptions (Mealli and
Pacini, 2012). More general results, though not embedded in a causal framework,
can be found on concentration graphs with a latent variable (Stanghellini and Vantaggi,
2013). The aim of this paper is to establish a link between the two settings
and to show that adapting results contained in the latter paper can help achieving
identification of principal casual effects in studies with more than one secondary
outcome
Big Data e Intelligenza Artificiale in Medicina di Laboratorio
No full textIn the last few years, artificial intelligence (AI) is gaining attention in several medical disciplines, including laboratory
medicine (LM). The raised interest on AI has been fueled not only by the huge amounts of information daily generated,
but also by the special natural context offered by laboratories, where digitalization have already occupied an important
part of the routine workflow of patients’ data. Motivated by these topics and under the auspices of SIBioC, a conference
on AI and big data was organized in May 2022 in Bologna, Italy. This conference covered several topics of AI and big
data, including but not limited to the current and future perspectives, comprising ethical challenges and the role of
laboratory specialists, including young professionals, the productive integration of AI with information technologies and
with other digital infrastructure, such as the LOINC and the block chain. Furthermore, some examples of real application
of AI in LM were reported, including diagnosis and monitoring of familiar hypercholesterolemia, management of insulin
treatments for diabetes, reference intervals identification and verification by indirect methods, COVID-19 diagnosis
and the monitoring of outpatients monoclonal gammopathy treatment by digital healthcare
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
Forecasting Probability of Bankruptcy from unbalanced data
No full textWhen analysing the determinants of bankruptcy of small and medium enterprises, one of the most common problems is that of unbalanced data, as very often the event under study happens in only a small percentage of cases. The aim of this paper is to explore three different statistical methods of coping with unbalanced data and to identify which of these has the greatest predictive capability in the context of the bankrupcty event.
The dataset is composed of all firms which were active in Tuscany in 2006. For each of them we have a five-year series of balance sheet indicators. Bankruptcy is represented by their legal status at May 2010. We focused on some indicators previously identified as predictors of the state of bankruptcy (Pierri 2013; Pierri, Burchi and Stanghellini 2013) and we tested the same model using the following three methods: logistic regression for matched case-control studies, logistic regression for a random balanced data sample, logistic regression for a sample balanced by ROSE (Random OverSampling Examples, Menardi and Torelli 2014). We built a training sample to develop the models and a hold-out sample to compare their discriminatory ability through ROC curves
Exact mediation analysis for ordinal outcome and binary mediator
No full textWith reference to a single mediator context, this brief report presents a
model-based strategy to estimate counterfactual direct and indirect effects
when the response variable is ordinal and the mediator is binary. Postulating a
logistic regression model for the mediator and a cumulative logit model for the
outcome, the exact parametric formulation of the causal effects is presented,
thereby extending previous work that only contained approximated results. The
identification conditions are equivalent to the ones already established in the
literature. The effects can be estimated by making use of standard statistical
software and standard errors can be computed via a bootstrap algorithm. To make
the methodology accessible, routines to implement the proposal in R are
presented in the Appendix. A natural effect model coherent with the postulated
data generating mechanism is also derived.Comment: 16 pages, 5 figure
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