Titlebook: New Statistical Developments in Data Science; SIS 2017, Florence, Alessandra Petrucci,Filomena Racioppi,Rosanna Verd Conference proceeding

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New Statistical Developments in Data Science978-3-030-21158-5Series ISSN 2194-1009 Series E-ISSN 2194-1017

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Monitoring the Spatial Correlation Among Functional Data Streams Through Moran’s Indexatially located sensors are used for performing at a very high frequency, repeated measurements of some variable. Due to the spatial correlation, sensed data are more likely to be similar when measured at nearby locations rather than in distant places. In order to monitor such correlation over time

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User Profile Construction Method for Personalized Access to Data Sources Using Multivariate Conjointources they questioned, nor their description and content. Consequently, their queries reflect no more a need that must be satisfied but an intention that must be refined. The purpose of personalization is to facilitate the expression of users’ needs. It allows them to obtain relevant information by

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Clustering Communities Using Interval K-Meansined as communities. In this work we will propose an approach to cluster the different communities using interval data. This approach is relevant in the context of the analysis of large networks and, in particular, in order to discover the different functionalities of the communities inside a networ

GLARE

Text Mining and Big Textual Data: Relevant Statistical Modelsss in Big Data environments. At issue are early stage case studies relating to: research publishing and research impact; literature, narrative and foundational emotional tracking; and social media, here Twitter, with a social science orientation. Central relevance and importance will be associated w

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Comparing FPCA Based on Conditional Quantile Functions and FPCA Based on Conditional Mean Function on the functional mean. Quantile regression characterizes the conditional distribution of a response variable and, in particular, some features like the tails behavior; smoothing splines have also been usefully applied to quantile regression to allow for a more flexible modelling. This framework fi

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