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Emotional intelligence is a key factor for success in sporting competitions, arousing great interest in the psychological assessment of athletes. When the evaluation of psychological behaviour relies on Likert-type psychometric scales, individuals could tend to respond to items regardless of their content or by selecting the extremes or the middle part of the response scale, compromising the measurement process. In this vein, the present paper aims to address measurement issues regarding uncertainty and response style during the assessment of emotional intelligence of elite swimmers by exploiting latent trait models. Results provide evidence in favour of models accounting for specific response behaviour compared to simple item response theory models.

The increasing adoption of Artificial Intelligence (AI) in finance raises growing concerns about its environmental footprint, particularly energy consumption and carbon emissions. Finance systems compute millions of model inferences, forecasts, and real-time decisions each day. While Green AI emphasizes energy-efficient and sustainable AI practices and has advanced rapidly in domains such as computer vision and natural language processing, its adoption in finance remains underexplored. This study presents a systematic literature review (SLR) and proposes a new approach for implementing Green AI models in finance. We analyze 58 peer-reviewed studies published between 2018 and 2025 and retrieved from the Scopus database to assess the state of Green AI in financial applications. The SLR identifies major gaps, including the absence of standardized benchmarks and assessment tools for Green AI in finance. It also highlights a persistent trade-off between reducing computational costs and maintaining high predictive accuracy. This tension complicates deployment in real-world financial settings, where economic benefits are often prioritized. Green AI can also help democratize access to advanced analytics, especially for smaller financial institutions that lack substantial computing resources. Finally, we present a taxonomy of Green AI techniques mapped to four stages of the machine learning lifecycle: data preparation, architecture design, model development, and deployment. We propose a theoretical framework that integrates Green AI principles (e.g., model pruning) with energy-monitoring tools to guide sustainable AI adoption in finance. The framework supports financial institutions and policymakers in implementing responsible AI systems that balance performance, compliance, and environmental sustainability.

Psychological attributes rarely operate in isolation: coaches and practitioners reason about networks of related traits rather than single indicators. We analyze a new dataset of 164 female volleyball players from Italy’s C and D leagues that combines standardized psychological profiling with background information. To learn directed relationships among mixed-type variables (ordinal questionnaire scores, categorical demographics, and continuous indicators), we introduce a hybrid structure learning approach that combines a latent Gaussian copula representation with a constraint-based skeleton and a score-based refinement to produce a single directed acyclic graph. We also study a bootstrap-aggregated variant to improve stability. In simulation studies spanning sample size, sparsity, and dimensionality, the proposed method achieves lower structural error and higher edge recovery than recent copula-based alternatives while maintaining high specificity. Applied to volleyball, the learned network organizes mental skills around goal setting and self-confidence, with emotional arousal linking motivation and anxiety, and places key personality traits, most notably neuroticism and extraversion, upstream of skill clusters. Scenario analyses quantify how improvements in specific skills propagate through the network to shift preparation, confidence, and self-esteem. Overall, the approach provides an interpretable, data-driven framework for profiling psychological traits in sport and for supporting decisions in athlete development.

Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.

In rating surveys, people are requested to evaluate objects, items, services, and so on, by choosing among a list of ordered categories. In some circumstances, it may happen that a subset of respondents selects a specific option just to simplify a more demanding choice. In this context, we generalize a class of ordinal data models (called cub and proven effective for fitting and interpretation), for taking the possible presence of a shelter choice into account. After the discussion of interpretative and inferential issues, the usefulness of the approach is checked against real case studies and by means of a simulation experiment. Some final remarks end the paper.

Analyzing sports data has become a challenging issue as it involves not standard data structures coming from several sources and with different formats, being often high dimensional and complex. This paper deals with a dyadic structure (athletes/coaches), characterized by a large number of manifest and latent variables. Data were collected in a survey administered within a joint project of University of Naples Federico II and Italian Swimmer Federation. The survey gathers information about psychosocial aspects influencing swimmers’ performance. The paper introduces a data processing method for dyadic data by presenting an alternative approach with respect to the current used models and provides an analysis of psychological factors affecting the actor/partner interdependence by means of a quantile regression. The obtained results could be an asset to design strategies and actions both for coaches and swimmers establishing an original use of statistical methods for analysing athletes psychological behaviour.

We present a Bayesian approach for the analysis of rating data when a scaling component is taken into account, thus incorporating a specific form of heteroskedasticity. Model-based probability effect measures for comparing distributions of several groups, adjusted for explanatory variables affecting both location and scale components, are proposed. Markov Chain Monte Carlo techniques are implemented to obtain parameter estimates of the fitted model and the associated effect measures. An analysis on students’ evaluation of a university curriculum counselling service is carried out to assess the performance of the method and demonstrate its valuable support for the decision-making process.

Motivated by the analysis of rating data concerning perceived health status , a crucial variable in biomedical, economic and life insurance models, the paper deals with diagnostic procedures for identifying anomalous and/or influential observations in ordinal response models with challenging data structures. Deviations due to some respondents’ atypical behavior, outlying covariates and gross errors may affect the reliability of likelihood based inference, especially when non robust link functions are adopted. The present paper investigates and exploits the properties of the generalized residuals. They appear in the estimating equations of the regression coefficients and hold the remarkable characteristic of interacting with the covariates in the same fashion as the linear regression residuals. Identification of statistical units incoherent with the model can be achieved by the analysis of the residuals produced by maximum likelihood or robust M -estimation, while the inspection of the weights generated by M -estimation allows to identify influential data. Simple guidelines are proposed to this end, which disclose information on the data structure. The purpose is twofold: recognizing statistical units that deserve specific attention for their peculiar features, and being aware of the sensitivity of the fitted model to small changes in the sample. In the analysis of the self-perceived health status, extreme design points associated with incoherent responses produce highly influential observations. The diagnostic procedures identify the outliers and assess their influence.

The paper describes a mixture distribution generated by Beta Binomial and Uniform random variables to allow for a possible overdispersion in surveys when the response of interest is an ordinal variable. This approach considers the joint presence of feeling, uncertainty and a possible dispersion sometimes present in the evaluation contexts. After a discussion of the main properties of this class of models, asymptotic likelihood methods have been applied for efficient statistical inference. The implementation on the survey on household income and wealth (SHIW) will confirm the versatility of this distribution and the usefulness to distinguish the determinants of uncertainty and overdispersion in real data.

In several applied disciplines, as Economics, Marketing, Business, Sociology, Psychology, Political science, Environmental research and Medicine, it is common to collect data in the form of ordered categorical observations. In this paper, we introduce a class of models based on mixtures of discrete random variables in order to specify a general framework for the statistical analysis of this kind of data. The structure of these models allows the interpretation of the final response as related to feeling, uncertainty and a possible shelter option and the expression of the relationship among these components and subjects’ covariates. Such a model may be effectively estimated by maximum likelihood methods leading to asymptotically efficient inference. We present a simulation experiment and discuss a real case study to check the consistency and the usefulness of the approach. Some final considerations conclude the paper.