By Stefano Cabras (auth.), Nicola Torelli, Fortunato Pesarin, Avner Bar-Hen (eds.)
This quantity comprises contributions chosen after a double blind overview technique and offered as a initial model on the forty fifth assembly of the Italian Statistical Society. The papers offer major and leading edge unique contributions and canopy a huge variety of subject matters together with: statistical idea; equipment for time sequence and spatial facts; statistical modeling and knowledge research; survey method and legit information; research of social, demographic and well-being information; and fiscal records and econometrics.
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Additional info for Advances in Theoretical and Applied Statistics
Most of the interest has been for the statistical analysis of Symbolic Data that represent complex data structure where variables can assume more than just a single value. Thus, Symbolic Data allow to describe classes of statistical units as a whole. Furthermore, other entities can be deﬁned in the realm of Symbolic data. These entities are the Symbolic objects, deﬁned in terms of the relationships between two different knowledge levels. This article aims at introducing a new type of SO based on the archetypal analysis.
Data depth basic ideas can easily ﬁt more general data structures than just units-byvariables data matrices. A notable example is functional data depth . Robustness is a key property of sample statistics and from this point of view halfspace depth performs better than simplicial depth. The IF of depth values is dimension-free for halfspace depth whereas it is linearly dependent on dimension for simplicial depth. This points out a possible weakness of simplicial depth because the depth values are more exposed to distortion by contaminating observations in high dimensions.
Natural Environment Research Council: Flood Studies Report. Natural Environment Research Council (1975) 23. : Principles of Statistical Inference. World Scientiﬁc, Singapore (1997) 24. : Statistical inference using extreme order statistics. Ann. Stat. 3, 119–131 (1975) 25. : Bayesian analysis in regression models using pseudolikelihoods. Comm. Stat. Theor Meth. 39, 3444–3455 (2010) 26. : Likelihood Methods in Statistics. Oxford University Press, Oxford (2000) 27. : Extreme Values in Finance, Telecommunications and the Environment.
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