1,721,006 research outputs found
On four-way CP model estimation efficiency
Full text linkThe latent structure of four-dimensional tensors can be investigated by means of the four-way CANDECOMP/PARAFAC model. This technique is seldom used because its estimating design is challenging from an algorithmic and interpretational standpoint. Parameter estimation with a least-squares approach can be computationally costly, especially under difficult conditions such as factor collinearity and model over-specification. In this work, we implement a 4th-order extension of the efficient trilinear procedure INT-2 to tackle estimating setbacks and test it in a simulation study
Three-way principal balance analysis: algorithm and interpretation
No full textCompositional Data Analysis can be useful for unveiling relative variability patterns among variables describing the parts of a phenomenon. Compositions are often represented as orthonormal balances associated with a sequential binary partition (SBP). Principal balances analysis (PBA) is a tool used to find a meaningful SBP by subsequently maximizing explained variability. The exact estimation of PBA is prohibitive for large datasets; therefore, algorithms providing an acceptable approximation are used instead. For compositional data of third-order, such exploratory search must account for third-mode variability. To this end, this work introduces a three-way adaptation of PBA in which estimation is carried out by Tucker3. A study on the composition of academic recruitment fields by Italian macro-region and gender/role is carried out to illustrate the merits of this procedure
Re-examining consumer engagement in the circular economy
No full textThis study aims to deepen knowledge of consumers’ attitudes towards circular economy products by focusing on the enabling factors that influence their behaviours. The success of the closed-loop economy depends not only on innovation but also on the active participation of the consumer. In these models, the authors witness the transition from the centrality of production to the centrality of use. This paper investigates Italian consumers’ tendency to purchase second-hand products in the clothing sector, one of the most polluting industrial sectors, focusing on the enabling factors that influence their behaviours, the reasons for their purchases and the existence of differences between market segments
A PLS method for seeking canonical correlations in case of perfect multicollinearity
No full textCanonical correlation analysis (CCA) is a useful tool for investigating the relationships between two sets of variables. If dispersion matrices can be inverted, canonical variates with maximal correlation are generally identified by means of singular value decomposition. However, when one or both variable groups are compositional, this classical approach cannot be followed. Compositional data are positive values which carry relative information describing the parts of a whole. In consequence they present a perfectly multicollinear structure and are characterized by singular dispersion matrices. As a solution to this issue which excludes a standard approach, an alternative way of computing canonical variates is proposed. Data are first transformed in log-ratio coordinates, then the Partial Least Squares approach is applied. This method provides a fast and easy way to deal with non-invertible dispersion matrices and, in addition, it yields results which are easy to interpret. The proposed methodology is assessed in an experimental study in which a comparison among alternative PLS algorithms is also provided, namely NIPALS, SIMPLS and Kernel
Detecting Public Social Spending Patterns in Italy Using a Three-Way Relative Variation Approach
No full textStudies on public social spending often fail to address the issues connected with budgetary constraints. Budget lines require public entities to partition resources among sectors of spending on the basis of preferred combinations and trade-offs. Standard exploratory tools do not allow to unveil this preference structure as they are hindered by the differences in budget scales and by the bounded nature of sector variability, i.e. an increase in one sector means a missed increase or a decrease in other sectors. In this work Italian public social spending is modeled with an alternative log-ratio methodology which allows to study relative variation patterns among sectors. It is also important to note that since the data is collected across time a three-way approach is recommended so that the variability of each mode is kept separate
Capitolo 4 - Sintetizzare e confrontare le distribuzioni: i valori di disuguaglianza.
No full textIn questo capitolo s’illustrano alcuni strumenti utili a descrivere e confrontare le distribuzioni di differenti variabili. Per iniziare, s’introducono gli indici di disuguaglianza, valori caratteristici che misurano l’attitudine di un fenomeno ad assumere modalità differenti. I diversi indici sono analizzati nel dettaglio e classificati in base al tipo di variabile cui sono applicabili. S’include anche una misura specifica per caratteri trasferibili: la concentrazione. S’introduce, poi, il box plot, un importante grafico per la rappresentazione delle principali caratteristiche
di una distribuzione. Successivamente, si passa alla definizione dei concetti di simmetria e curtosi per valutare la forma di una distribuzione. Il capitolo si conclude con alcune tecniche per operare confronti basilari tra variabili: rapporti statistici, variazioni e numeri indice.
Obiettivo del capitolo è far comprendere l’importanza che l’analisi della variabilità ha sulla sintesi della distribuzione di una variabile, indirizzando alla scelta della migliore tecnica
Statistical tools for student evaluation of academic educational quality
No full textMeasuring academic educational quality presents three major difficulties, typical
of all customer satisfaction and service quality studies: the use of subjective scales; the
ordinal nature of the data; and the multifold structure of satisfaction. In order to solve these
problems, principal component analysis (PCA) of compositional data is proposed in this
work. The core idea behind this methodology is to analyze by PCA the relative information
within the data rather than focusing on absolute scores. This approach is discussed in
comparison with a widely used Item Response Theory method (the Partial Credit Model) in
order to assess its merits, e.g. always identifying a coherent preference structure. Both
procedures were, thus, carried out on a real dataset collected with the 2013/14 ANVUR
questionnaire by L’Universita´ di Napoli-L’Orientale
An ATLD–ALS method for the trilinear decomposition of large third-order tensors
No full textCP decomposition of large third-order tensors can be computationally challenging. Parameters are typically estimated by means of the ALS procedure because it yields least-squares solutions and provides consistent outcomes. Nevertheless, ALS presents two major flaws which are particularly problematic for large-scale problems: slow convergence and sensitiveness to degeneracy conditions such as over-factoring, collinearity, bad initialization and local minima. More efficient algorithms have been proposed in the literature. They are, however, much less dependable than ALS in delivering stable results because the increased speed often comes at the expense of accuracy. In particular, the ATLD procedure is one of the fastest alternatives, but it is hardly employed because of the unreliable nature of its convergence. As a solution, multi-optimization is proposed. ATLD and ALS steps are concatenated in an integrated procedure with the purpose of increasing efficiency without a significant loss in precision. This methodology has been implemented and tested under realistic conditions on simulated data sets
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