arXiv subjects

Vicente J. Bolos

Publications and source records attributed to Vicente J. Bolos.

11 recordsLinked to original sources

arXiv2026

Multidimensional scaling of two-mode three-way asymmetric dissimilarities: finding archetypal profiles and clustering

Multidimensional scaling visualizes dissimilarities among objects and reduces data dimensionality. While many methods address symmetric proximity data, asymmetric and especially three-way proximity data (capturing relationships across multiple occasions) remain underexplored. The h-plot enables the analysis of asymmetric and non-reflexive relationships by embedding dissimilarities in a Euclidean space, allowing further techniques like archetypoid analysis to identify representative extreme profiles. However, no existing methods extract archetypal profiles from three-way asymmetric proximity data. This work extends the h-plot methodology to three-way proximity data under both symmetric and asymmetric, conditional and unconditional frameworks. The proposed approach offers several advantages: intuitive interpretability through a unified Euclidean representation; an explicit, eigenvector-based analytical solution free from local minima; scale invariance under linear transformations; computational efficiency for large matrices; and a straightforward goodness-of-fit evaluation. Furthermore, it enables the identification of archetypal profiles and clustering structures for three-way asymmetric proximities. Its performance is compared with existing models for multidimensional scaling and clustering, and illustrated through a financial application. All data and code are provided to facilitate reproducibility.

stat.ME↗

arXiv2026

Archetypal analysis of European 10-year government bond yields with multidimensional scaling of two-mode three-way asymmetric dissimilarities

A recent methodology for extracting archetypal profiles from three-way asymmetric proximity data is applied to a dataset comprising 23 x 23 x 3 two-mode, three-way asymmetric dissimilarity matrices. The asymmetric dissimilarities are based on the oriented wavelet squared coherence of 10-year government bond yields among 23 European countries across three time intervals from 2001 to the present. First, the h-plot is computed for the unconditional two-mode three-way data, which is then represented in a unified Euclidean space, providing an intuitive and interpretable visualization. Subsequently, archetypoid analysis is performed. This unsupervised methodology identifies the archetypal countries and expresses all remaining countries as mixtures of these archetypal instances. Additionally, the degree of asymmetry for each country is calculated, offering further insight into the structure of the dissimilarities. The dataset and code are provided to support reproducible research.

stat.ME↗

arXiv2026

Biarchetype analysis for univariate functional data. An application to macroeconomic financial time series

We introduce biarchetype analysis for the first time in the context of univariate functional data. This unsupervised methodology extends archetype analysis by simultaneously identifying archetypal structures across both the cases (countries, in our application) and the temporal argument. Both cases and time points are expressed as mixtures of biarchetypes, yielding a concise and highly interpretable representation of complex functional observations. Although biarchetype analysis is not intended as a clustering technique, it offers superior interpretability compared with biclustering approaches, as it is based on extreme, representative patterns rather than average centroids, thereby enhancing human comprehension. We apply the proposed method to 10-year government bond yields of European countries over the period 2001-2025. The results identify three distinct time regimes (the pre-crisis period, the euro-area sovereign debt crisis, and the post-crisis period), and reveal Germany, Greece, and Hungary as country archetypes.

stat.ME↗

arXiv2025

Conventional and Fuzzy Data Envelopment Analysis with deaR

deaR is a recently developed R package for data envelopment analysis (DEA) that implements a large number of conventional and fuzzy models, along with super-efficiency models, cross-efficiency analysis, Malmquist index, bootstrapping, and metafrontier analysis. It should be noted that deaR is the only package to date that incorporates Kao-Liu, Guo-Tanaka and possibilistic fuzzy models. The versatility of the package allows the user to work with different returns to scale and orientations, as well as to consider special features, namely non-controllable, non-discretionary or undesirable variables. Moreover, it includes novel graphical representations that can help the user to display the results. This paper is a comprehensive description of deaR, reviewing all implemented models and giving examples of use.

econ.EM↗

arXiv2024

A multi-criteria decision support system to evaluate the effectiveness of training courses on citizens' employability

This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of training courses in accordance with their capacity to enhance the employability of the students. The methodology is delineated in four stages. Firstly, a `working life curve' was defined to provide a quantitative description of an individual's working life. Secondly, an analysis based on K-medoids clustering defined a control group for each individual for comparison. Thirdly, the performance of a course according to each of the four predefined criteria was calculated using a t-test to determine the mean performance value of those who took the course. Ultimately, the unweighted TOPSIS method was used to evaluate the efficacy of the various training courses in relation to the four criteria. This approach effectively addresses the challenge of using extensive datasets within a system while facilitating the application of a multi-criteria unweighted TOPSIS method. The results of the multi-criteria TOPSIS method indicated that training courses related to the professional fields of administration and management, hostel and tourism and community and sociocultural services have positive impact on employability and improving the working conditions of citizens. However, courses that demonstrate the greatest effectiveness in ranking are the least demanded by citizens. The results will help policymakers evaluate the effectiveness of each training course offered by the regional government.

cs.CY↗

arXiv2024

Interdependence between Green Financial Instruments and Major Conventional Assets: A Wavelet-Based Network Analysis

This paper examines the interdependence between green financial instruments, represented by green bonds and green stocks, and a set of major conventional assets, such as Treasury, investment-grade and high-yield corporate bonds, general stocks, crude oil, and gold. To that end, a novel wavelet-based network approach that allows for assessing the degree of interconnection between green financial products and traditional asset classes across different investment horizons is applied. The~empirical results show that green bonds are tightly linked to Treasury and investment-grade corporate bonds, while green stocks are strongly tied to general stocks, regardless of the specific time period and investment horizon considered. However, despite their common climate-friendly nature, there is no a remarkable association between green bonds and green stocks. This means that these green investments constitute basically two independent asset classes, with a distinct risk-return profile and aimed at a different type of investor. Furthermore, green financial products have a weak connection with high-yield corporate bonds and crude oil. These findings can have important implications for investors and policy makers in terms of investment decision, hedging strategies, and sustainability and energy policies.

math.NA↗

arXiv2024

Continuous models combining slacks-based measures of efficiency and super-efficiency

In the framework of data envelopment analysis (DEA), Tone (2001) introduced the slacks-based measure (SBM) of efficiency, which is a nonradial model that incorporates all the slacks of the evaluated decision-making units (DMUs) into their efficiency scores, unlike classical radial efficiency models. Next, Tone (2002) developed the SBM super-efficiency model in order to differentiate and rank efficient DMUs, whose SBM efficiency scores are always $1$. However, as pointed out by Chen (2013), some interpretation problems arise when the so-called super-efficiency projections are weakly efficient, leading to an overestimation of the SBM super-efficiency score. Moreover, this overestimation is closely related to discontinuity issues when implementing SBM super-efficiency in conjunction with SBM efficiency. Chen (2013) and Chen et al. (2019) treated these problems, but they did not arrive to a fully satisfactory solution. In this paper, we review these papers and propose a new complementary score, called composite SBM, that actually fixes the discontinuity problems by counteracting the overestimation of the SBM super-efficiency score. Moreover, we extend the composite SBM model to different orientations and variable returns to scale, and propose additive versions. Finally, we give examples and state some open problems.

math.OC↗

arXiv2024

wavScalogram: an R package with wavelet scalogram tools for time series analysis

In this work we present the wavScalogram R package, which contains methods based on wavelet scalograms for time series analysis. These methods are related to two main wavelet tools: the windowed scalogram difference and the scale index. The windowed scalogram difference compares two time series, identifying if their scalograms follow similar patterns at different scales and times, and it is thus a useful complement to other comparison tools such as the squared wavelet coherence. On the other hand, the scale index provides a numerical estimation of the degree of non-periodicity of a time series and it is widely used in many scientific areas.

physics.data-an↗

arXiv2024

Chance constrained directional models in stochastic data envelopment analysis

We construct a new family of chance constrained directional models in stochastic data envelopment analysis, generalizing the deterministic directional models and the chance constrained radial models. We prove that chance constrained directional models define the same concept of stochastic efficiency as the one given by chance constrained radial models and, as a particular case, we obtain a stochastic version of the generalized Farrell measure. Finally, we give some examples of application of chance constrained directional models with stochastic and deterministic directions, showing that inefficiency scores obtained with stochastic directions are less or equal than those obtained considering deterministic directions whose values are the means of the stochastic ones.

math.PR↗

arXiv2019

Quantifying non-periodicity of non-stationary time series through wavelets

In this paper, we introduce a new wavelet tool for studying the degree of non-periodicity of time series that is based on some recently defined tools, such as the \textit{windowed scalogram} and the \textit{scale index}. It is especially appropriate for non-stationary time series whose characteristics change over time and so, it can be applied to a wide variety of disciplines. In addition, we revise the concept of the scale index and pose a theoretical problem: it is known that if the scale index of a function is not zero then it is non-periodic, but if the scale index of a function is zero, then it is not proved that it has to be periodic. This problem is solved for the particular case of the Haar wavelet, thus reinforcing the interpretation and applicability of the scale index as a useful tool for measuring non-periodicity. Finally, we discuss the relationship between non-periodicity and unpredictability, comparing the new wavelet tool with the sample entropy.

nlin.CD↗

arXiv2011

Intrinsic definitions of "relative velocity" in general relativity

Given two observers, we define the "relative velocity" of one observer with respect to the other in four different ways. All four definitions are given intrinsically, i.e. independently of any coordinate system. Two of them are given in the framework of spacelike simultaneity and, analogously, the other two are given in the framework of observed (lightlike) simultaneity. Properties and physical interpretations are discussed. Finally, we study relations between them in special relativity, and we give some examples in Schwarzschild and Robertson-Walker spacetimes.

gr-qc↗