1,721,392 research outputs found
Editorial
No full textDear readers, it is a wonderful honor and a privilege to assume the coordinating
editorship of the journal Advances in Data Analysis and Classification, following the
footsteps of the previous Coordinating Editor Hans-Hermann Bock and working with
the other two founding editors of ADAC: Wolfgang Gaul and Akinori Okada, and with
the new, very welcome Editor Claus Weihs who joins the board in 2012.
We founded ADAC in 2007, with the idea to foster and support technical and
scientific information in the context of Data Analysis, Classification and in general in
Multivariate Statistics, by combining different international viewpoints. Our auspices
were to have a great resonance with readers and contributors, from all relevant scientific
communities, by offering a tool for augmenting their scientific research impact. After
five years of activities, thanks to the papers submitted by significant contributors and
the appropriate peer review selection, perfectly coordinated by Hans Bock, the journal
has quickly increased its scientific influence in the international community, as evidenced by the impact factor obtained, in 2010, just three years after the journal has
been created and despite the high competition generated by numerous new journals,
proposed in the last five years from all parts of the world.
For this reason I start this new task and challenge with a positive sensation and great
enthusiasm. My major purpose is to continue the work done by Hans in improving the
scientific impact of the journal as a premier communication medium in Data Analysis,
Classification and Multivariate Statistics, including their applications
Two-mode multi-partitioning
No full textNew methodologies for two-mode (objects and variables) multi-partitioning of two way data are presented. In particular, by reanalyzing the double k-means, that identifies a unique partition for each mode of the data, a relevant extension is discussed which
allows to specify more partitions of one mode, conditionally to the partition of the other one. The performance of such generalized
double k-means has been tested by both a simulation study and an application to gene microarray data
Stirring, Mixing, Growing: Microscale Processes Change Larger Scale Phytoplankton Dynamics
No full textThe quantitative description of marine systems is constrained by a major issue of scale separation: phytoplankton production processes occur at sub-centimeter scales, while the contribution to the Earth's biogeochemical cycles is expressed at much larger scales, up to the planetary one. In spite of vastly improved computing power and observational capabilities, the modeling approach has remained anchored to an old view that sees the microscales as unable to substantially affect larger ones. The lack of a widespread theoretical appreciation of the interactions between vastly different scales has led to the proliferation of numerical models with uncertain predictive capabilities. In this paper, we use the phenology of phytoplankton blooms as one example of a macroscopic ecosystem feature affected by microscale interactions. We describe two distinct mechanisms that produce patchiness within a productive water column: turbulent entrainment of less-productive water at the base of the mixed layer, and stirring by slow turbulence of a vertical phytoplankton gradient sustained by depth-dependent light availability. In current eddy-diffusive models, patchiness produced in this way is wiped out very rapidly, because the time scales of irreversible mixing largely overlap those of mechanical stirring. We propose a novel Lagrangian modeling framework that allows for the existence of microscale patchiness, even when that is not fully resolved. We show, with a mixture of theoretical arguments and numerical simulations of increasing realism, how the presence of patchiness, in turn, affects larger-scale properties, demonstrating that the timing of phytoplankton blooms and vertical variability of chlorophyll in the oceanic upper layers is determined by the mutual interplay between the stirring, mixing and growing processes
Trusted smart statistics: The challenge of extracting usable aggregate information from new data sources
Full text linkRecent years have seen dramatic changes in sources of data, amounts of data, availability of data, frequency of data, and types of data. Along with advances in data analytic technology these changes have opened up huge possibilities for improving the information content and timeliness of official statistics. in this paper we characterise such 'smart statistics', examining their potential benefits and the obstacles that must be overcome if they are to be trusted and relied upon. In particular, we list eight specific recommendations which we believe producers of smart statistics should adhere to if the full potential for economic and social benefit is to be achieved
Growing Clustering Algorithms in Market Segmentation: Defining Target Groups and Related Marketing Communication
No full textDecker R, Scholz S, Wagner R. Growing Clustering Algorithms in Market Segmentation: Defining Target Groups and Related Marketing Communication. In: Zani S, Cerioli A, Riani M, Vichi M, eds. Data Analysis, Classification and the Forward Search. Studies in classification, data analysis and knowledge organization. Heidelberg: Springer; 2006: 23-30
A composite indicator for the waste management in the EU via Hierarchical Disjoint Non-Negative Factor Analysis
No full textIn the last years, the quantity of information and statistics about waste management are more and more consistent but so far, few studies are available in this field. The goal of this paper is of producing a model-based Composite Indicator of “good” Waste Management, in order to provide a useful tool of support for EU countries’ policy-makers and institutions. Composite Indicators (CIs), usually, are multidimensional concepts with a hierarchical structure characterized by the presence of a set of specific dimensions, each one corresponding to a subsets of manifest variables. Thus, we propose a CI for Waste Management in Europe by using a hierarchical model-based approach with positive loadings. This approach guarantees to comply with all the good properties on which a composite indicator should be based and to detect the main dimensions (i.e., aspects) of the Waste Management phenomenon. In other terms, this paper provides a hierarchically aggregated index that best describes the Waste Management in EU with its main features by identifying the most important high order (i.e., hierarchical) relationships among subsets of manifest variables. All the parameters are estimated according to the maximum likelihood estimation method (MLE) in order to make inference on the parameters and on the validity of the model
A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory
No full text
- …
