Papers

Induction is the process by which empirical evidence is transformed to knowledge. Hume famously argued—and Popper and others agree—that there can be no logical justification for induction. A weaker form of induction, due to Bayes, expresses the aforementioned knowledge in terms of probabilities, and we review some well-known and not-so-well-known criticisms of the Bayesian solution. We then investigate the relatively new possibilistic inferential model (IM) framework, showing that, in addition to the IM's strong, statistical reliability guarantees that it uniquely enjoys, it is safe from those criticisms that damage the Bayesian foundations. For illustration, we reconsider the classical sunrise problem and compare our proposed solution with Laplace's famous rule of succession.

Generative artificial intelligence (GenAI) is a large language model (LLM) that has the ability to generate media based on user-provided prompts. Given the demonstrated capabilities of models such as ChatGPT in information synthesis and programming, there is growing interest in their potential role within the research process. However, little work has evaluated recent GenAI models for research tasks in the domain of statistical research. This case study examines GenAI as a tool for developing a literature review and translating methodology from academic papers into code, for the topic of dynamic treatment regime (DTR) estimation via the dynamic weighted ordinary least squares (dWOLS) approach. Specifically, we utilize ChatGPT-5 and ScholarAI (Sept-Nov 2025 release) in the processes of identifying relevant sources for the literature review, creating summaries of papers, identifying gaps in research, and R code generation to implement methodology. Our findings show that current GenAI models lack the depth and contextual understanding required to accomplish these tasks without careful prompting and supervision of a knowledgeable researcher. Nonetheless, GenAI has potential to increase efficiency of tasks which take advantage of its search and summarization abilities, as well as basic code debugging and algorithm formation. We demonstrate that under a knowledgeable guide, GenAI can function as a research tool, but not as a substitute for methodological expertise.

Longitudinal data are fundamental across scientific disciplines for modeling how complex systems evolve over time. A core challenge in these settings is handling temporal misalignment: different subjects undergo a similar underlying process but at varying speeds and starting times. This difficulty is further compounded when tracking multivariate dynamics, where features interact dynamically rather than following simple, independent pathways. To address these challenges, we present leaspy (LEArning Spatiotemporal patterns in PYthon), an open-source Python library. Built on a mixed effects model, leaspy enables the estimation of population-level trajectories while accounting for subject-specific variability. The library supports multivariate formulation across diverse data types, including continuous, time-to-event (joint), and mixture models-and has been successfully applied to characterize disease heterogeneity, and generate individual predictions We demonstrate its practical utility through an application in neurodegenerative disease progression. Developed following modern software engineering practices, including systematic testing and continuous integration, leaspy facilitates the integration of new models and provides a robust user-friendly library for longitudinal progression modeling.

These days, Sri Lanka is facing a severe dengue outbreak. In this situation, it is very useful to provide an overall understanding of Sri Lanka's historical dengue trends as well as the current dengue situation. A dashboard is an easy and effective way to convey a large amount of information to the public. Therefore, the Sri Lanka Dengue Dashboard is an initial attempt to provide comprehensive information about the dengue situation in Sri Lanka, together with the relationship between climate factors and dengue cases. Keywords: Data visualization, Heat map, Climate-dengue relationship, Tree map, Time series analysis, Quarto, Dashboard, Dengue fever

A growing literature argues that progress in human longevity is slowing, and that this slowdown may signal an approaching biological limit to lifespan. The argument rests on visible flattening of cumulative life expectancy records, evaluated through linear regressions or through comparisons of average gains across decades. These summaries, however, provide only weak evidence for slowing improvements, because they cannot distinguish a true from an apparent slowdown. An apparent slowdown may arise due to periods of low variance around unchanged mean annual gains. Applying a Bayesian change-point framework to annual gains, both at the best-practice frontier and separately for each record-holding country, we find no evidence that the expected annual gain has changed across nearly two centuries, but strong evidence that its variance has moved through distinct historical regimes, with progress most regular in recent decades. The recent flattening is consistent with a low-variance regime and provides little evidence for a decline in the underlying mean pace of improvement. Claims that the best-practice frontier signals a biological ceiling should therefore not rest on the recent bend in the cumulative record alone. The frontier is better read as an empirical benchmark, the highest country-specific survival achieved under observed historical conditions.

Latent space models represent network nodes as points in a geometric space, with connection probabilities determined by distances between latent positions under a fixed metric, typically Euclidean, spherical, or hyperbolic. We generalize the classical formulation by introducing nodal multiplicative effects motivated by a local deformation of the latent metric. This modification approximates a conformal deformation of the metric tensor while preserving the logistic predictor and the geometric interpretability of the model, thereby capturing additional structural heterogeneity without altering the global reference geometry. We study the generative behavior of the proposed model through simulation experiments and develop an optimization-based inference scheme derived from a hierarchical Bayesian formulation with parameter regularization. Applications to eight real networks show that the proposed approach increases the generative flexibility of classical latent space models and more accurately reproduces several topological properties observed in real networks.

A unified inferential framework is developed to address the stress-strength reliability of multicomponent systems under progressive Type II censoring. The maximum likelihood estimate of reliability is obtained using an expectation-maximization algorithm, followed by the determination of the corresponding Fisher information matrix and confidence intervals based on the missing-value principle. To facilitate a comparative inferential assessment, maximum product spacing estimates are also developed. By employing both informative and non-informative prior models, a comprehensive analysis is conducted within a Bayesian framework, and suitable summaries are obtained using the Markov chain Monte Carlo algorithm. The performance of all the estimators is analyzed through an extensive simulation study. Finally, a practical application of the proposed methodology is presented using a reliability data set. Furthermore, we determine optimal progressive censoring strategies using three different optimality measures and discuss their usefulness in reliability studies.

Understanding e-bike route choice is essential for developing effective cycling infrastructure, yet empirical evidence remains limited. This study investigates shared e-bike route choice in Washington, DC, using Global Positioning System (GPS) trajectory data from the Capital Bikeshare system. A Path Size Logit model is estimated using a hybrid choice set consisting of observed routes and corresponding shortest paths, integrating Geographic Information System (GIS)-based infrastructure variables with computer vision-derived street-level visual features extracted from Street View images (SVI). The results indicate that e-bike riders tend to choose routes that minimize conflicts with both motor vehicles and pedestrians while maintaining travel continuity. Roadway hierarchy substantially moderates the influence of bicycle facilities, with the presence of bicycle facilities having a much greater impact on route choice along major roads than along minor roads. Longer trips also exhibit stronger preferences for cycling infrastructure. Incorporating street-level visual features improves model performance, although their effects are generally smaller than those of road infrastructure, with trees being the only greenery component showing a consistently positive effect. Standardized effect sizes further identify the most behaviorally important route attributes. These findings provide practical evidence for cycling infrastructure planning in the e-bike era.