1,721,177 research outputs found
Lorenz Model Selection
No full textIn the paper, we introduce novel model selection measures based on Lorenz zonoids which, differently from measures based on correlations, are based on a mutual notion of variability and are more robust to the presence of outlying observations. By means of Lorenz zonoids, which in the univariate case correspond to the Gini coefficient, the contribution of each explanatory variable to the predictive power of a linear model can be measured more accurately. Exploiting Lorenz zonoids, we develop a Marginal Gini Contribution measure that allows to measure the absolute explanatory power of any covariate, and a Partial Gini Contribution measure that allows to measure the additional contribution of a new covariate to an existing model
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
Vinegars of the World
No full textThe importance of vinegars goes far beyond their merely economic aspect: they are in fact the result of environmental resources and culture, of tradition and science. The origin of vinegar is lost in the dawn of human history, together with the beginning of agriculture and the discovery of alcoholic fermentation of fruits, cereals and vegetables. This book, written by experts and scientists working in the field and enriched by several images and tables, clearly describes some of the main types of vinegar produced in the world in their peculiar aspects. In particular, vinegar technology and microbiology are dealt with extensively. The nomenclature of the microorganisms involved has been updated according to the current taxonomy
La microvinificazione nella selezione della vite con l'impiego di ceppi di lievito caratterizzati
No full textAnalisi di raggruppamento per il Web Usage Mining
No full textNel presente lavoro si è considerato il problema della cluster analysis applicata al web usage minin
Explainable AI methods in cyber risk management
No full textArtificial intelligence (AI) methods are becoming widespread, especially when data are not sufficient to build classical statistical models, as is the case for cyber risk management. However, when applied to regulated industries, such as energy, finance, and health, AI methods lack explainability. Authorities aimed at validating machine learning models in regulated fields will not consider black-box models, unless they are supplemented with further methods that explain why certain predictions have been obtained, and which are the variables that mostly concur to such predictions. Recently, Shapley values have been introduced for this purpose: They are model agnostic, and powerful, but are not normalized and, therefore, cannot become a standardized procedure. In this paper, we provide an explainable AI model that embeds Shapley values with a statistical normalization, based on Lorenz Zonoids, particularly suited for ordinal measurement variables that can be obtained to assess cyber risk
Shapley-Lorenz eXplainable Artificial Intelligence
No full textExplainability of artificial intelligence methods has become a crucial issue, especially in the most regulated fields, such as health and finance. In this paper, we provide a global explainable AI method which is based on Lorenz decompositions, thus extending previous contributions based on variance decompositions. This allows the resulting Shapley-Lorenz decomposition to be more generally applicable, and provides a unifying variable importance criterion that combines predictive accuracy with explainability, using a normalised and easy to interpret metric. The proposed decomposition is illustrated within the context of a real financial problem: the prediction of bitcoin prices
Why to buy insurance? An explainable artificial intelligence approach
No full textWe propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well
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