Titlebook: Statistical Models for Data Analysis; Paolo Giudici,Salvatore Ingrassia,Maurizio Vichi Conference proceedings 2013 Springer International

nostrum

Model-Based Classification Via Patterned Covariance Analysis,n rule is derived using Gaussian mixtures where covariance matrices are given according to a multiple testing procedure which asesses a pattern among heteroscedasticity, homometroscedasticity, homotroposcedasticity, and homoscedasticity. The mixture models are then fitted using all available data (l

恶心

Data Stream Summarization by Histograms Clustering, which require to adapt the knowledge discovery process to the new emerging concepts. To deal with this challenge we propose a clustering algorithm where each cluster is summarized by a histogram and data are allocated to clusters through a Wasserstein derived distance. Histograms are a well known g

confederacy

Nonparametric Multivariate Inference Via Permutation Tests for CUB Models,f Uniform and shifted Binomial distributions, CUB models), proposed by Piccolo (2003, ., 85–104), D’Elia & Piccolo (2005, ., 917–934), Piccolo (2006, ., 33–78) and Iannario (2010, ., 87–94). In case of a univariate response, a permutation solution to test for covariates effects has been discussed in

Amorous

大猩猩

ALIEN

,On Two Classes of Weighted Rank Correlation Measures Deriving from the Spearman’s , ,er ones. This paper investigates, from a descriptive perspective, the behaviour of (.) five existing indices that introduce suitable weights in the simplified formula of the Spearman’s . and (.) an additional five indices we derive using the same weights in the Pearson’s product-moment correlation i

Radiculopathy

Beanplot Data Analysis in a Temporal Framework,togram time series or the interval time series can be very useful to model the intra-period variability of the series. These types of new time series can be very useful with High Frequency financial data, data collected with often irregularly spaced observations.

压碎

大骂

Grouping Around Different Dimensional Affine Subspaces,of application. Allowing for different dimensions is needed in many applications. This work extends the TCLUST methodology to deal with the problem of grouping data around different dimensional linear subspaces in the presence of noise. Two ways of considering error terms in the orthogonal of the li

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