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

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Performance Comparison of Heterogeneity Measures for Count Data Models in Bayesian Perspective the advantage of Bayesian modelling by incorporating plausible prior distributions on the parameter of interest. The study is illustrated with a data on rental bikes obtained from UC Irvine Machine Learning Repository. Results have indicated the impact of prior distributions and usage of heterogeneity estimators in count data models.

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Sampling and Modelling Issues Using Big Data in Now-Castingg and forecasting, with a significant impact on final results and their interpretation. Using a MIDAS model with Google Trends covariates, we analyse sampling error issues and time-domain effects triggered by these digital economy new data sources.

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2194-1009 siness operations and provide essential information for deci.This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics a

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User Profile Construction Method for Personalized Access to Data Sources Using Multivariate Conjoint on a Multivariate Conjoint Analysis approach to get these profiles. The proposed strategy provides a representation of the users and of the items, according to their characteristics, on factorial plans; whereas, the collaborative approach predicts the missing preferences.

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Bayesian Estimation of Causal Effects in Carcinogenicity Tests Based upon CTActual data; (ii) making cumbersome transformations to original counts; (iii) constraining distributions at low concentrations to have a variance larger than the mean. Open issues are discussed in relation to the current practice adopted to perform multi-laboratory experiments on the same substance.

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2194-1009 ers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt withhere. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role..978-3-030-21160-8978-3-030-21158-5Series ISSN 2194-1009 Series E-ISSN 2194-1017