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Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these models is jeopardized in high-dimensional spaces, where they tend to be over-parameterized. As a consequence, different solutions have been proposed, often relying on matrix decompositions or variable selection strategies. Recently, a methodological link between Gaussian graphical models and finite mixtures has been established, paving the way for penalized model-based clustering in the presence of large precision matrices. Notwithstanding, current methodologies implicitly assume similar levels of sparsity across the classes, not accounting for different degrees of association between the variables across groups. We overcome this limitation by deriving group-wise penalty factors, which automatically enforce under or over-connectivity in the estimated graphs. The approach is entirely data-driven and does not require additional hyper-parameter specification. Analyses on synthetic and real data showcase the validity of our proposal. [ABSTRACT FROM AUTHOR]

Model-independent searches in particle physics aim at completing our knowledge of the universe by looking for new possible particles not predicted by the current theories. Such particles, referred to as signal, are expected to behave as a deviation from the background, representing the known physics. Information available on the background can be incorporated in the search, in order to identify potential anomalies. From a statistical perspective, the problem is recasted to a peculiar classification one where only partial information is accessible. Therefore a semi-supervised approach shall be adopted, either by strengthening or by relaxing assumptions underlying clustering or classification methods respectively. In this work, following the first route, we semi-supervise nonparametric clustering in order to identify a possible signal. The main contribution consists in tuning a nonparametric estimate of the density underlying the experimental data to identify a partition which guarantees a signal warning while allowing for an accurate classification of the background. As a side contribution, a variable selection procedure is presented. The whole procedure is tested on a dataset mimicking proton–proton collisions performed within a particle accelerator. While finding motivation in the field of particle physics, the approach is applicable to various science domains, where similar problems of anomaly detection arise. [ABSTRACT FROM AUTHOR]

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR. [ABSTRACT FROM AUTHOR]

Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these data, we need to account for relations among both time instants and variables and, at the same time, for subject heterogeneity. We propose a new co-clustering methodology for grouping individuals and variables simultaneously, designed to handle both functional and longitudinal data. Our approach borrows some concepts from the curve registration framework by embedding the shape invariant model in the latent block model, estimated via a suitable modification of the SEM-Gibbs algorithm. The resulting procedure allows for several user-defined specifications of the notion of cluster that can be chosen on substantive grounds and provides parsimonious summaries of complex time-dependent data by partitioning data matrices into homogeneous blocks. Along with the explicit modelling of time evolution, these aspects allow for an easy interpretation of the clusters, from which also low-dimensional settings may benefit. [ABSTRACT FROM AUTHOR]

The article discusses political philosophy and political history, with reference to the works of 20th century British born Russian-Jewish political and social philosopher, Isaiah Berlin, and 18th century Savoy count Joseph Marie de Maistre, with a focus on the development of Fascism. Topics discussed include the views of Berlin on the work of de Maistre, the influence of de Maistre on Fascist political thought, and the historical roots of Fascism, with reference to political philosophy and the history of Europe.

The article discusses the views of the British philosopher and historians of philosophy Isaiah Berlin on the writings of German economic philosopher Karl Marx. Among other topics addressed are how Berlin came to research Marx's life and writings at the impetus of editor H.A.L. Fisher of New College, Oxford, in Oxford, England, the political and historical context of the request, and the resulting biography of Marx "Karl Marx: His Life and Environment".