Titlebook: Statistical Models and Methods for Data Science; Leonardo Grilli,Monia Lupparelli,Maurizio Vichi Conference proceedings 2023 The Editor(s)

不规则

shrill

AXIS

,Hierarchical Clustering of Income Data Based on Share Densities,similarity of the sub-populations in terms of income inequality. To this end, we propose to extract information from a particular function, the so-called share density, strongly related to different inequality measures, such as the Gini and Theil indexes. The Jensen-Shannon dissimilarity measure is

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CHOIR

爆米花

,Marginals Matrix Under a Generalized Mallows Model Based on the Power Divergence,study the effect on the Marginals matrix by generalizing the Mallows model via the Cressie-Read power divergence. Marginals matrices, induced by different ranking models, are extensively compared and their symmetric property over the class of Hoeffding distances is rigorously proved. Furthermore, th

addict

,Time Series Clustering Based on Forecast Distributions: An Empirical Analysis on Production Indicesording to their forecast distributions. The procedure uses feedforward neural networks to approximate the original nonlinear process combined with the pair bootstrap as a resampling technique. An empirical analysis of the construction sector for 21 European countries is performed. Since the COVID-19

HPA533

Partial Reconstruction of Measures from Halfspace Depth,s of all closed halfspaces containing .. A natural question is whether the halfspace depth, as a function of ., determines the measure . completely. In general, it turns out that this is not the case, and it is possible for two different measures to have the same halfspace depth function everywhere

发出眩目光芒

Posterior Predictive Assessment of IRT Models via the Hellinger Distance: A Simulation Study,nvestigating overall model fit and potential misfit due to specific items. Different approaches rely on graphical analysis, posterior predictive .-values, the relative entropy, and, more recently, the Hellinger distance. In this study, we focus on the performance of the Hellinger distance in the cas

全国性

Shapley-Lorenz Values for Credit Risk Management,banking, finance and insurance, Artificial Intelligence methods lack explainability and, therefore, authorities aimed at monitoring risks may not validate them. To solve this issue, explainable machine learning methods have been introduced to “interpret” black-box models. Among them, Shapley values