Search Results (486)

This study investigates statistical inference for upper record ranked set sampling (URRSS) data from the Kies distribution. In multiple-cycle URRSS settings where the heterogeneity across cycles is non-ignorable, both classical and Bayesian approaches are adopted to estimate the unknown model parameters and associated reliability metrics. Likelihood-based point and interval estimates are derived for these parameters and reliability indices, and the existence and uniqueness of the maximum likelihood estimators for the Kies distribution parameters are rigorously established. Moreover, a hierarchical Bayesian framework is developed to accommodate cycle-specific variability, with a Metropolis–Hastings algorithm embedded within a Gibbs sampler proposed to facilitate posterior computation in complex scenarios. The performance of the suggested methods is assessed through extensive simulation studies, supplemented by two real-world data applications that demonstrate their practical utility. Numerical results show that the proposed estimators perform well overall, with the hierarchical Bayesian approach showing a particular advantage when uncertainty about the cycle effect is present. Full article

Induction motors constitute a major share of industrial drives, making reliable fault detection essential for maintaining operational continuity. This work develops a Bayesian classifier for identifying rotor-bar damage using start-up current measurements represented in the frequency domain. The spectra are modelled as smooth functional curves using a hierarchical B-spline formulation, and posterior sampling provides a generative mechanism for augmenting scarce labelled data. Classification is performed using a Bayesian Gaussian mixture model, where each prediction is obtained by averaging over thousands of posterior samples, yielding stable and interpretable probability estimates. In experimental evaluation, the proposed approach achieves consistent separation between healthy and faulty motors across repeated training runs, correctly identifying all test cases in the binary classification setting and exhibiting more stable probability estimates than logistic and soft-max regression under limited labelled data. The model additionally signals atypical responses for unmodelled faults, indicating potential for anomaly detection. These findings highlight the suitability of Bayesian functional modelling as a reliable tool for induction motor condition monitoring. Full article

The increasing scale and structural flexibility of modern wind turbine rotors have made real-time monitoring and active control of blade tip deflection a critical requirement for ensuring operational safety, particularly regarding blade-tower clearance. Since direct measurement through physical sensors is often impractical due to high costs, installation difficulties and maintenance challenges, this work proposes a data-based framework for out-of-plane blade tip deflection estimation. The approach introduces a systematic and hierarchical input selection framework that evaluates sensor signal groups, ranging from standard SCADA measurements to configurations including auxiliary nacelle/tower sensors and dedicated blade-root instrumentation. By combining Spearman correlation and spectral coherence, the proposed framework ensures consistent representation of key turbine dynamics across all operating regions. This framework provides a structured trade-off between implementation feasibility and estimation fidelity, enabling tailored solutions for applications such as structural health monitoring and safety-critical active control. Compact Feedforward Neural Network (FNN) and Time-Delay Neural Network (TDNN) architectures, whose hyperparameters are optimized via Bayesian optimization, are employed to achieve high estimation accuracy while preserving computational efficiency. Evaluated through high-fidelity aeroelastic simulations of the NREL 5 MW turbine using the industry-standard FAST (Fatigue, Aerodynamics, Structures, and Turbulence) tool across all operating conditions, the approach achieves $R^2=0.894$ using SCADA-only inputs, $R^2=0.973$ when augmented with nacelle and tower-top sensors and a peak fidelity of $R^2=0.989$ using blade-root bending moment data. These results demonstrate that high-fidelity virtual sensing is attainable without blade instrumentation, providing a viable pathway for real-time tip clearance monitoring and fatigue mitigation. This directly enhances the operational resilience of wind energy systems and their contribution to the stability of renewable-dominated power grids. Full article

High-dimensional survival analyses require calibrated risk and measurable uncertainty, but standard elastic net Cox models provide only point estimates. We develop a Bayesian elastic net Cox (BEN–Cox) model for high-dimensional proportional hazards regression that places a hierarchical global–local shrinkage prior on coefficients and performs full Bayesian inference via Hamiltonian Monte Carlo. We represent the elastic net penalty as a global–local Gaussian scale mixture with hyperpriors that learn the ${\ell }_1/{\ell }_2$ trade-off, enabling adaptive sparsity that preserves correlated gene groups; using HMC with the Cox partial likelihood, we obtain full posterior distributions for hazard ratios and patient-level survival curves. Methodologically, we formalize a Bayesian analogue of the elastic net grouping effect at the posterior mode and establish posterior contraction under sparsity for the Cox partial likelihood, supporting the stability of the resulting risk scores. On the METABRIC breast cancer cohort ($n=1903$; $p=440$ gene-level features after preprocessing, derived from an Illumina HT-12 array with ≈24,000 probes at the raw feature level), BEN–Cox achieves slightly lower prediction error, higher discrimination, and better global calibration than a tuned ridge Cox, lasso Cox, and elastic net Cox baselines on a held-out test set. Posterior summaries provide credible intervals for hazard ratios and identify a compact gene panel that remains biologically plausible. BEN–Cox provides an uncertainty-aware alternative to tuned penalized Cox models with theoretical support, offering modest improvements in calibration and providing an interpretable sparse signature in highly-correlated survival data. Full article

Climate change poses significant risks to Mediterranean aquaculture, with sea surface temperature (SST) identified as a critical stressor affecting cultivated species. This study aims to assess climate-related risks for coastal aquaculture in the Valencian Community (Spain) by analyzing SST spatiotemporal variability and predicting future trends. A multi-method approach was employed, combining ARIMA models for 10-year predictions at eight coastal locations, Bayesian hierarchical models (BHM) fitted via INLA for spatiotemporal analysis of maximum SST and temperature range (2000–2024), and Generalized Additive Models (GAM) to evaluate relationships with climate indices (NAO, AMO, ENSO). Results revealed a consistent warming trend since the 1990s, with ARIMA predictions indicating maximum SST values of 27.2 ± 0.1 °C in September over the next decade. The spatiotemporal model showed effective spatial correlation ranges of 246 km for maximum SST and 207 km for SST range. Anomalous warming years (2003, 2006, 2018, 2023–2024) coincided with documented marine heatwave events. The GAM explained 98.2% of deviance, with AMO showing significant influence ($p<0.001$), while ENSO was not statistically significant. Southern locations (Altea, Campello) currently experience the highest temperatures, but projections indicate Valencia and Sagunto will become the warmest areas. These findings provide essential information for marine spatial planning and recommend a precautionary approach when considering aquaculture relocation towards northern coastal areas. Full article

In recent years, the growing availability of large-scale data across a wide range of disciplines has created new opportunities for developing models that improve the predictive accuracy of statistical models. Although techniques such as regularization and Bayesian hierarchical methods are commonly used for building predictive models, substantial challenges remain, particularly when dealing with high-dimensional datasets that contain considerable noise. In this study, we propose a Bayesian hierarchical model that employs a spike-and-slab hierarchical elastic net prior that regularizes the Cox Proportional Hazards (Cox-PH) model. The method combines Bayesian modeling with the regularized partial log-likelihood of the Cox-PH framework, incorporating an Elastic Net penalty to estimate the joint posterior distribution under a hierarchical elastic net prior. We compute this posterior using an Expectation–Maximization Cyclic Coordinate Descent Algorithm (EM-CCDA), which streamlines feature selection and enhances overall predictive performance. We evaluate the algorithm’s performance through Monte Carlo simulations and apply it to three real-world datasets, comparing the results with those from established classical and Bayesian survival analysis approaches. The findings demonstrate notable gains in both feature selection and predictive accuracy, highlighting the model’s strong ability to predict patient survival and identify relevant genes in real biological datasets. Full article

A central debate in metacommunity theory concerns how the relative importance of ecological processes varies with spatial scale. We addressed this by integrating global soil fauna metacommunity datasets to analyze the effects of spatial extent (the specific dimension of scale examined) and environmental factors on metacommunity patterns using Bayesian models. Results suggested that increasing spatial extent was strongly associated with a higher prevalence of Clementsian patterns. Notably, this relationship was not explained by the concomitant environmental variables, which may be consistent with the influence of latent spatial properties (e.g., dispersal limitation) or unobserved environmental heterogeneity at broader scales. Conversely, environmental factors independently were associated with other patterns. Notably, the effect of soil nitrogen on checkerboard patterns was context-dependent: it suppressed species segregation under low spatial turnover (β_SIM) but potentially weakened or shifted to facilitation under high turnover. This suggests that resource enrichment alters the balance between niche-based and neutral processes. Although further verification in under-sampled climatic zones is required, our synthesis supports a hierarchical driver framework: spatial extent emerges as a key correlation of broad distributional order, whereas resource availability is suggested to regulate the prevalence of competitive exclusion at finer resolutions. Full article

Visible Light Positioning (VLP) is widely regarded as a promising technology for high-precision indoor localization due to its immunity to radio-frequency interference and compatibility with existing Light-Emitting Diode (LED) lighting infrastructure. Despite recent progress, current VLP systems remain fundamentally limited by nonlinear received signal strength (RSS) characteristics, unknown transmitter orientations, and dynamic indoor disturbances. Existing solutions typically address these challenges in isolation, resulting in limited robustness and scalability. This paper proposes SCENE-VLP (Sequential Deep Learning with Feature Compression and Optimal State Estimation), a structured positioning framework that integrates feature compression, temporal sequence modeling, and probabilistic state refinement within a unified estimation pipeline. Specifically, SCENE-VLP combines Principal Component Analysis (PCA) and Denoising Autoencoders (DAE) for linear and nonlinear observation conditioning, Gated Recurrent Units (GRU) for modeling temporal dependencies in RSS sequences, and Kalman-based filtering (KF/EKF) for recursive state-space refinement. The framework is formulated as a hierarchical approximation of the nonlinear observation model, linking data-driven measurement learning with Bayesian state estimation. A systematic ablation study across multiple scenarios, including same-dataset evaluation and cross-dataset generalization, demonstrates that each component provides complementary benefits. Feature compression reduces redundancy while preserving dominant signal structure; GRU significantly improves robustness over static regression; and recursive filtering consistently reduces positioning error compared to unfiltered predictions. While both KF and EKF improve performance, EKF provides incremental refinement under mild nonlinearities. Extensive simulations conducted on an indoor dataset collected from a realistic deployment with eight ceiling-mounted LEDs and a single photodetector (PD) show that SCENE-VLP achieves sub-decimeter localization accuracy, with P50 and P95 errors of 1.84 cm and 6.52 cm, respectively. Cross-scenario evaluation further confirms stable generalization and statistically consistent improvements. These results demonstrate that the structured integration of observation conditioning, temporal modeling, and Bayesian refinement yields measurable gains beyond partial pipeline configurations, establishing SCENE-VLP as a robust and scalable solution for next-generation indoor visible light positioning systems. Full article

Cardamom (Elettaria cardamomum (L.) Maton) is a major export crop in Guatemala; however, its genetic basis remains largely unexplored. This study aimed to evaluate the genetic diversity and differentiation among 288 cardamom accessions from the Northern Transversal Strip, the country’s primary production area. Eleven molecular markers (SSR, ISSR, and EST-SSR) were used to generate multilocus profiles analyzed under a dominant model. Genetic diversity revealed average values of Shannon’s index (I = 0.316) and expected diversity (h = 0.207), with SSR markers providing the highest values (I = 0.364, h = 0.233). Bayesian and hierarchical analysis identified three genetic groups (K = 3). The relatively low diversity observed is consistent with the introduction history of this crop in Guatemala, human-driven selection, and historical bottlenecks caused by Cardamom Mosaic Virus and thrips infestations. Despite these constraints, private and high-frequency bands were detected across genetic groups, offering potential for marker-assisted selection. These findings provide the first genetic baseline for Guatemalan cardamom, supporting future breeding strategies aimed at improving resilience, productivity, and adaptation to climate change. Full article

Transport affordability defined as the share of household income devoted to transport expenditure is a key dimension of urban equity and social inclusion, particularly in contexts characterised by spatial inequality and income disparities. This study examines provincial variation in public transport affordability across South Africa using a hierarchical Bayesian regression framework applied to province–year data from 2015 to 2022 (n = 72). Affordability is operationalised as a transport cost burden, with higher values indicating a greater proportion of household income spent on transport, and is modelled as a function of household income, trip frequency, household population, and total provincial employment, with province-level random intercepts capturing unobserved regional heterogeneity. The results indicate that household income is negatively associated with transport cost burden, suggesting that provinces with higher average income devote a smaller share of income to transport and therefore experience better affordability. In contrast, household population and aggregate provincial employment are positively associated with transport cost burden, reflecting higher overall mobility and commuting demands in larger and more economically active provinces rather than improved affordability. Trip frequency shows no statistically meaningful association with affordability once household composition and income capacity are accounted for. After accounting for observed characteristics, between-province variation is limited, indicating that affordability dynamics are broadly similar across provinces over the study period. Methodologically, the hierarchical Bayesian framework enables partial pooling across provinces and supports probabilistic inference through credible intervals, thereby improving the stability of estimates in a small-sample multilevel context. While the analysis is associational rather than causal, the findings provide policy-relevant evidence for monitoring transport affordability, including benchmarking the prevalence of affordability burdens relative to the commonly used 10% threshold. Full article

This paper proposes a physics-informed Bayesian framework for virtual testing and predictive modeling of train performance, specifically addressing stopping-distance prediction. The approach unifies physical simulation models with data-driven statistical inference to achieve uncertainty-aware predictions under limited or noisy measurements. By embedding governing equations of motion into a hierarchical Bayesian structure, the method systematically accounts for both model-form and data uncertainty, allowing explicit decomposition into aleatoric and epistemic components. A Gaussian process surrogate is employed to efficiently emulate high-fidelity physics simulations while preserving key dynamic behaviors and parameter sensitivities. The Bayesian formulation enables probabilistic calibration and validation, providing predictive distributions and confidence bounds. As a representative application, the framework is applied to the virtual prediction of train stopping distances, demonstrating how the proposed methodology captures nonlinear braking dynamics and quantifies uncertainty in safety-relevant performance metrics directly compatible with statistical verification standards such as EN 16834. The results confirm that the physics-informed Bayesian approach enables accurate, interpretable, and standards-aligned virtual testing across a wide range of dynamical systems. Full article

The reliability of Internet of Things systems is critical for industrial applications; however, operational reliability data are often heterogeneous and strongly right-skewed, exhibiting non-Gaussian behaviour, overdispersion, and production-level variability that challenge classical predictive maintenance models. Existing approaches frequently rely on pooled assumptions or simplified error structures, limiting their ability to identify latent batch-level degradation and to jointly interpret discrete failure events and continuous lifetime information. To address these limitations, this study proposes a hierarchical Bayesian framework based on Integrated Nested Laplace Approximation (INLA) to jointly model discrete reset counts and continuous failure times. Three Latent Gaussian Models are evaluated—ranging from pooled baseline specifications to a fully joint model with shared latent batch effects—using a synthetic dataset designed to mimic realistic industrial fault patterns. The analysis demonstrates that standard pooled models fail to capture the degradation dynamics of defective device batches. In contrast, the hierarchical joint model successfully recovers latent quality variations, accurately links high reset intensity with shortened lifetimes, and substantially improves model fit, achieving a DIC reduction of over 67% compared to baseline approaches. INLA provides a computationally efficient and rigorously calibrated alternative to MCMC-based methods for modelling skewed and heterogeneous reliability data. The proposed framework enables reliable identification of defective production batches and robust uncertainty quantification, offering a practical tool for data-driven predictive maintenance in Industry 4.0. Future work will focus on validating the proposed framework using real industrial IoT datasets. Full article

While conventional Bayesian compressive sensing exploits signal sparsity for accurate sound field reconstruction from under-sampled measurements, its practicality is limited by high computational complexity and slow convergence. To address these limitations, this paper proposes a variational Bayesian compressive sensing framework integrated with equivalent source modeling for sound field reconstruction. The approach first establishes a sparse representation of the sound field using the equivalent source method, and then assigns hierarchical prior distributions to the equivalent source strengths and the noise precision within this Bayesian model. Mean-field variational inference is adopted to derive an analytically tractable approximation to the true posterior distribution by minimizing the Kullback–Leibler divergence, thus enabling efficient estimation of the equivalent source strengths and subsequent high-accuracy sound field reconstruction. This proposed method retains the desirable statistical advantages of Bayesian modeling while enhancing computational efficiency. Numerical simulations and experiments validate that the proposed method achieves superior reconstruction accuracy compared with conventional Bayesian compressive sensing and orthogonal matching pursuit algorithm, with significantly reduced computational burden and enhanced robustness in low signal-to-noise ratio scenarios. Full article

For the hierarchical normal and normal-inverse-gamma model, we derive the Bayesian estimator of the variance parameter in the normal distribution under Stein’s loss function—a penalty function that treats gross overestimation and underestimation equally—and compute the associated Posterior Expected Stein’s Loss (PESL). Additionally, we determine the Bayesian estimator of the same variance parameter under the squared error loss function, along with its corresponding PESL. We further develop empirical Bayes estimators for the variance parameter using a conjugate normal-inverse-gamma prior, employing both the method of moments and Maximum Likelihood Estimation (MLE). Theoretical properties, including posterior and marginal distributions, two inequalities that relate two Bayes estimators and their corresponding PESLs, and consistencies of hyperparameter estimators and empirical Bayes estimators, are established. The simulation results demonstrate that MLEs outperform moment estimators in estimating hyperparameters, particularly with respect to consistency and model fit. Finally, we apply our methodology to real-world data on poverty levels—specifically, the percentage of individuals living below the poverty line—to validate and illustrate our theoretical findings. Full article

Mental illness and related health inequities are disproportionately concentrated in economically disadvantaged urban neighborhoods. The COVID-19 pandemic has been associated with a rise in mental illness prevalence, with women generally at greater risk than men. Urban areas facing multiple structural and socioeconomic challenges may have limited capacity to meet the mental healthcare needs of residents, leading to increased reliance on emergency departments (EDs) for acute care. This ecological study uses data over four years (2018–2021) and examines spatial variations in ED utilization at the census tract level, focusing on geographic areas with women of reproductive age diagnosed with mental illness to compare patterns before and during the COVID-19 pandemic. Of the 22,565 ED visits in the four-year period, 12,832 occurred before COVID-19 and 9733 during COVID-19. Our findings highlight persistent structural disparities in mental healthcare access across census tracts characterized by high concentrations of vulnerable women of reproductive age. Understanding these spatial disparities allows for geographically targeted interventions and the prioritization of resources for neighborhoods identified as most underserved. Full article