1,721,008 research outputs found
L'uso dei modelli logitlineari e loglineari nell'analisi dei dati epidemiologici
No full textCLEUP, Padov
Management of uncertainty in Statistical Reasoning: The case of Regression Analysis
No full textAbstractStatistical Reasoning is affected by various sources of Uncertainty: randomness, imprecision, vagueness, partial ignorance, etc. Traditional statistical paradigms (such as Statistical Inference, Exploratory Data Analysis, Statistical Learning) are not capable to account for the complex action of Uncertainty in real life applications of Statistical Reasoning. A conceptual framework, called “Informational Paradigm”, is introduced in order to analyze the role of Information and Uncertainty in these complex contexts. Regression Analysis is taken as the reference problem for developing the discussion. Three basic sources of Uncertainty are considered in this respect: (1) uncertainty about the relationship between response and explanatory variables; (2) uncertainty about the relationship between the observed data and the “universe” of possible data; (3) uncertainty about the observed values of the variables (imprecision, vagueness). Some of the available methods for coping with these different types of Uncertainty are discussed in an orderly way, from the simpler cases where only one source at a time is dealt with, to the more complex ones where all sources act together. Probabilistic and Fuzzy-Possibilistic tools are exploited, in this connection. In spite of the recent relevant contributions in this domain, the weaknesses and deficiencies of the current procedures for managing Uncertainty in Regression Analysis, as well as in other areas of Statistics, are emphasized. The elements of a generalized system of Statistical Reasoning, capable to deal with the various sources of Uncertainty, are finally introduced and the lines for future investigation in this perspective are indicated
The treatment of different sources of uncertainty affecting the data and the statistical models according to the Informational Paradigm
No full textIl nuovo ruolo del metodo statistico nelle scienze sociali secondo l'"approccio strategico integrato"
No full text
Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization
No full textFuzzy unsupervised clustering models based on entropy regularization are suggested in order to classify time-varying data. In particular, in the proposed models, objective functions, which are the sum of two terms, are minimized. The first term is a dynamic generalization of intra-cluster distance, in a fuzzy framework, that takes into account the instantaneous and/or longitudinal features of the time-varying observations (the so-called multivariate time trajectories); in this way, the within cluster dispersion is minimized (maximize the internal cohesion). The second term represents the Shannon entropy measure as applied to fuzzy partitions (entropy regularization); then, a given measure of entropy is maximized or, equivalently, the converse of the entropy is minimized. Overall, the total functional depending on both the previous aspects is optimized. The dynamic fuzzy entropy clustering models have been applied to a meteorological dataset and an empirical comparison with the instantaneous and/or longitudinal fuzzy C-means clustering models has been made. © 2005 Elsevier B.V. All rights reserved
Regression Analysis with Fuzzy Informational Paradigm: An Adaptive Fuzzy Regression Model
No full texted. C. Provasi, Cleuped. C. Provasa, Cleu
Fuzzy K-means clustering models for triangular fuzzy time trajectories
No full textWe focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fully informational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated. © Springer-Verlag 2002
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