In a time when increasingly larger and complex data collections are
being produced, it is clear that new and adaptive forms of data
representation and analysis have to be conceived and implemented.
Distributional data, i.e., data where a distribution rather than a
single value is recorded for each descriptor, on each unit, come
into this framework. Distributional data may result from the
aggregation of large amounts of open/collected/generated data, or
it may be directly available in a structured or unstructured form,
describing the variability of some features. This book provides
models and methods for the representation, analysis,
interpretation, and organization of distributional data, taking
into account its specific nature, and not relying on a reduction to
single values, to be conform to classical paradigms. Conceived as
an edited book, gathering contributions from multiple authors, the
book presents alternative representations and analysis' methods for
distributional data of different types, and in particular, -Uni-
and bi-variate descriptive statistics for distributional data
-Clustering and classification methodologies -Methods for the
representation in low-dimensional spaces -Regression models and
forecasting approaches for distribution-valued variables
Furthermore, the different chapters -Feature applications to show
how the proposed methods work in practice, and how results are to
be interpreted, -Often provide information about available
software. The methodologies presented in this book constitute
cutting-edge developments for stakeholders from all domains who
produce and analyse large amounts of complex data, to be analysed
in the form of distributions. The book is hence of interest for
companies operating not only in the area of data analytics, but
also on logistics, energy and finance. It also concerns national
statistical institutes and other institutions at European and
international level, where microdata is aggregated to preserve
confidentiality and allow for analysis at the appropriate regional
level. Academics will find in the analysis of distributional data a
challenging up-to-date field of research.
General
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