Multi-model ensembles are widely used in climate science, yet Coupled Model Intercomparison Project Phase 6 (CMIP6) models are neither independent nor equally skillful. We present a framework for interpretable ensemble learning that improves prediction while making model contributions explicit. Using monthly CMIP6 near-surface temperature and precipitation (1948–2014) against ERA5, we compare arithmetic multi-model ensemble (AMME) averaging with Ridge regression, random forest, and a continuous gating mixture that adaptively interpolates between them. On held-out test years (2006–2014), gating achieves the best temperature performance and improves precipitation distributional behavior relative to both AMME and individual learners. We trace these gains back to individual models and space: dominant-cluster and dominant-model maps show that gating reallocates trust in regionally coherent patterns rather than collapsing to a single best model, while entropy-based diagnostics quantify where trust is concentrated versus distributed. These analyses provide a practical pathway for transparent ensemble learning that supports both predictive skill and scientific accountability.

High-frequency mortality data have attracted growing attention, but their use has largely been confined to specific applications rather than general modeling and forecasting. Such data pose new challenges to traditional mortality models due to pronounced seasonal patterns and short-term fluctuations. To address these challenges and produce more accurate forecasts with the high-frequency mortality data, this paper introduces a novel integration of gradient boosting techniques into traditional stochastic mortality models under a multi-population setting. Our key innovation lies in using the Li and Lee model as the weak learner within the gradient boosting framework, replacing conventional decision trees. Empirical studies are conducted using weekly mortality data from 30 countries (Human Mortality Database, 2015–2019). Empirical evidence highlights that the proposed methodology not only enhances model fit by accurately capturing underlying mortality trends and seasonal patterns but also achieves superior forecast accuracy, compared to the benchmark models. We also investigate a key challenge in multi-population mortality modeling: how to select appropriate subpopulations with sufficiently similar mortality experiences. A comprehensive clustering exercise is conducted based on mortality improvement rates and seasonal strength. The empirical results demonstrate that our proposed model maintains strong forecast accuracy across different clustering configurations, thereby reducing the need for extensive data preprocessing.

The weakening of traditional social cleavages in predicting political preferences does not mean that group membership for detecting political behaviour no longer exists—it may only be the case that the boundaries of these groups need to be redefined. Using original and unique data, this article investigates the relevance of lifestyle for capturing and delineating social groups sharing political attitudes. Logistic regression machine learning models test how lifestyle predicts political behaviour alongside conventional sociodemographic variables. K-means clustering identifies three Quebecer lifestyle profiles and their relationship to sociodemographic features. Findings show lifestyle better predicts voting intentions and lifestyle clusters significantly associated with vote choice, even after controlling for sociodemographics. This research reassesses the weakening of socio-structural influences and the importance of contextual and individual variables in understanding political behaviour.

This retrospective study analysed 14,625 isolates of the six major hospital-associated ‘ESKAPE’ pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) collected between 2002 and 2024 in a Hungarian tertiary-care centre. Antimicrobial resistance was assessed using the antibiotic resistance index (ARI), multidrug resistance (MDR) ratios, and resistance instability index (RII). A. baumannii and E. faecium showed the highest resistance burdens and instability. Age showed a significant monotonic association with resistance (Spearman r = 0.88), with peaks in infants, middle-aged women, and the elderly. Species-specific age trends varied, with a negative correlation seen in Enterobacter spp. Hierarchical clustering grouped pathogens by resistance trajectory rather than taxonomy. Pairwise resistance distances confirmed divergence between Gram-positive and Gram-negative species. Resistance to aminoglycosides and sulphonamides showed the highest year-to-year variability, as quantified by the RII, particularly in A. baumannii and E. faecium. Vector autoregressive (VAR) modelling predicted continued MDR increases in these species. A strong correlation was found between ARI and RII (Pearson r = 0.85, p = 0.032). These findings underscore the importance of integrating resistance magnitude and volatility in surveillance.

Coalition research increasingly emphasizes party-level explanations of coalition outcomes. However, this work does not account for the complex multilevel structure between parties and governments: many parties participate in multiple governments and governments often comprise multiple parties. In this paper, I show that this crisscrossing structure creates dependencies among observations both across and within governments. If ignored, these dependencies produce downward-biased uncertainty estimates that cluster-robust standard errors fail to fully correct. To address this issue, I then introduce a model that extends the Multiple Membership Multilevel Model to represent the multilevel structure of coalition government data. The model accounts for party-level dependencies across governments through party-specific effects in each coalition they join, and for dependencies within governments by representing the total party effect on a government as a weighted sum of its members’ contributions. By allowing party weights to vary with covariates describing their interrelationships, the model enables researchers to examine the interdependent nature of coalition outcomes. I validate the model through simulation and an empirical application to coalition government survival, showing that ignoring party-level dependencies can produce misleading conclusions at all levels of analysis. The model is estimated via Bayesian MCMC and implemented in the accompanying R package ‘bml’.

As a direct consequence of liquid kerosene injection, aeroengine combustors may be categorized as non-premixed combustion systems, characterized by a swirl-stabilized and highly complex flow field. In addition to the flow of air through the fuel injector, there are a large number of other features through which the oxidizer can enter the heat release region. These can have an impact on local fuel–air mixing, inducing strong spatial and temporal variations in stoichiometry, thereby affecting emissions and combustion system performance. This article discusses a novel statistical methodology, based on principal component analysis (PCA) and K-means clustering, that aims to improve the understanding of fuel–air mixing in realistic aeroengine combustors. The method is applied in a post-processing step to data sampled from a large-eddy simulation, where every chamber inflow has been tagged with a unique passive scalar, which allows it to be traced across space and time. PCA is used to construct a low-dimensional, visually interpretable representation of a spatially localized fuel–air mixing process, while K-means clustering is employed to produce an unsupervised discretization of the flow field into regions of similar fuel–air mixing characteristics. The proposed methodology is computationally inexpensive, and the easily interpretable outputs can help the combustion engineer make better-informed decisions about combustor design.

The Latent Position Model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positions in a latent space, such that the probability of an interaction between each pair of individuals or nodes is determined by their distance in this latent space. A key feature of this model is that it allows one to visualize nuanced structures via the latent space representation. The LPM can be further extended to the Latent Position Cluster Model (LPCM), to accommodate the clustering of nodes by assuming that the latent positions are distributed following a finite mixture distribution. In this paper, we extend the LPCM to accommodate missing network data and apply this to non-negative discrete weighted social networks. By treating missing data as “unusual” zero interactions, we propose a combination of the LPCM with the zero-inflated Poisson distribution. Statistical inference is based on a novel partially collapsed Markov chain Monte Carlo algorithm, where a Mixture-of-Finite-Mixtures (MFM) model is adopted to automatically determine the number of clusters and optimal group partitioning. Our algorithm features a truncated absorb-eject move, which is a novel adaptation of an idea commonly used in collapsed samplers, within the context of MFMs. Another aspect of our work is that we illustrate our results on 3-dimensional latent spaces, maintaining clear visualizations while achieving more flexibility than 2-dimensional models. The performance of this approach is illustrated via three carefully designed simulation studies, as well as four different publicly available real networks, where some interesting new perspectives are uncovered.

In many contexts, an individual’s beliefs and behavior are affected by the choices of their social or geographic neighbors. This influence results in local correlation in people’s actions, which in turn affects how information and behaviors spread. Previously developed frameworks capture local social influence using network games, but discard local correlation in players’ strategies. This paper develops a network games framework that allows for local correlation in players’ strategies by incorporating a richer partial information structure than previous models. Using this framework we also examine the dependence of equilibrium outcomes on network clustering—the probability that two individuals with a mutual neighbor are connected to each other. We find that clustering reduces the number of players needed to provide a public good and allows for market sharing in technology standards competitions.

This study analyzes 1,000 meta-analyses drawn from 10 disciplines—including medicine, psychology, education, biology, and economics—to document and compare methodological practices across fields. We find large differences in the size of meta-analyses, the number of effect sizes per study, and the types of effect sizes used. Disciplines also vary in their use of unpublished studies, the frequency and type of tests for publication bias, and whether they attempt to correct for it. Notably, many meta-analyses include multiple effect sizes from the same study, yet fail to account for statistical dependence in their analyses. We document the limited use of advanced methods—such as multilevel models and cluster-adjusted standard errors—that can accommodate dependent data structures. Correlations are frequently used as effect sizes in some disciplines, yet researchers often fail to address the methodological issues this introduces, including biased weighting and misleading tests for publication bias. We also find that meta-regression is underutilized, even when sample sizes are large enough to support it. This work serves as a resource for researchers conducting their first meta-analyses, as a benchmark for researchers designing simulation experiments, and as a reference for applied meta-analysts aiming to improve their methodological practices.

Texts, whether literary or historical, exhibit structural and stylistic patterns shaped by their purpose, authorship and cultural context. Formulaic texts, which are characterized by repetition and constrained expression, tend to differ in their information content (as defined by Shannon) compared to more dynamic compositions. Identifying such patterns in historical documents, particularly multi-author texts like the Hebrew Bible, provides insights into their origins, purpose and transmission. This study aims to identify formulaic clusters: sections exhibiting systematic repetition and structural constraints, by analyzing recurring phrases, syntactic structures and stylistic markers. However, distinguishing formulaic from non-formulaic elements in an unsupervised manner presents a computational challenge, especially in high-dimensional and sample-poor data sets where patterns must be inferred without predefined labels.

To address this, we develop an information-theoretic algorithm leveraging weighted self-information distributions to detect structured patterns in text. Our approach directly models variations in sample-wise self-information to identify formulaicity. By extending classical discrete self-information measures with a continuous formulation based on differential self-information in multivariate Gaussian distributions, our method remains applicable across different types of textual representations, including neural embeddings under Gaussian priors.

Applied to hypothesized authorial divisions in the Hebrew Bible, our approach successfully isolates stylistic layers, providing a quantitative framework for textual stratification. This method enhances our ability to analyze compositional patterns, offering deeper insights into the literary and cultural evolution of texts shaped by complex authorship and editorial processes.

Our politics are increasingly polarised. Polarisation takes many forms. One is increasing clustering, whereby people hold down-the-line liberal or conservative views on a wide range of orthogonal issues. Some philosophers think that such clustering is indicative of irrationality, and so finding yourself in one of several clusters gives you evidence that not all your political beliefs are true. I argue that the reverse is true, presenting a simple model of belief-formation in which finding yourself in one of several clusters of opinion on orthogonal issues should increase, rather than decrease, your confidence that all your beliefs are true.

Factor score indeterminacy is a characteristic property of factor analysis (FA) models. This research introduces a novel procedure, regression-based factor score exploration (RFE), which uniquely determines factor scores and simultaneously estimates other parameters of the FA model. RFE uniquely determines factor scores by minimizing a loss function that balances FA and multivariate regression, regulated by a tuning parameter. Theoretical aspects of RFE, including the uniqueness of factor scores, the relationship between observed and latent variables, and rotational indeterminacy, are examined. Additionally, clustering-based factor exploration (CFE) is presented as a variant of RFE, derived by generalizing the penalty term to enable the clustering of factor scores. It is demonstrated that CFE creates cluster structures more accurately than the existing method. A simulation study shows that the proposed procedures accurately recover true parameter matrices even in the presence of error-contaminated data, with lower computational demand compared to existing methods. Real data examples illustrate that the proposed procedures provide interpretable results, demonstrating high relevance to the factor scores obtained by existing methods.