Search Results (555)

In multivariate statistical analysis, accurate modeling of the covariance structure is critical for high-dimensional data analysis, variable selection, and regularization. In high-dimensional settings, strong inter-variable correlation and redundancy are key factors limiting the performance of classical sparsity-based methods. While LASSO and its variants provide effective tools for coefficient shrinkage and variable selection, they may select redundant variables and produce unnecessarily complex models in highly correlated settings. In this study, a Correlation-Sensitive Adaptive LASSO (CDA-LASSO) method is proposed to address these limitations. The proposed approach is based on a hybrid weighting mechanism that makes the penalty term sensitive not only to initial coefficient magnitudes but also to the correlation structure between variables. This structure incorporates correlation-based redundancy information and imposes stronger penalties on predictors with higher directed redundancy scores. Under fixed-dimensional regularity conditions, the bounded correlation multiplier is shown to preserve the selection consistency and oracle limiting distribution of Adaptive LASSO. The method was evaluated through 14 high-dimensional simulation scenarios covering different sample sizes, dimensionalities, sparsity levels, correlation strengths, support structures, and normal or heavy-tailed errors. The results indicate that the Max and kMean variants generally reduce the false discovery rate and model size relative to LASSO and Elastic Net while maintaining broadly comparable predictive performance. Numerical improvements over Adaptive LASSO were also observed in several scenarios, although these differences were not uniformly statistically significant. Under very high correlation, reductions in false discoveries were sometimes accompanied by modest decreases in the true positive rate. The real-world Riboflavin analysis further showed that the CDA-LASSO variants produced smaller models than LASSO and Elastic Net while retaining comparable prediction errors. Overall, CDA-LASSO directly incorporates the internal correlation structure of the data into the penalty weights without requiring a predefined graphical structure and provides a practical methodological extension for more controlled and parsimonious variable selection in high-dimensional correlated settings. Full article

Testing multinormality in two-level structural equation models (SEMs) presents a fundamental challenge because observations from the same level-2 unit are correlated, violating the independence assumption required by classical normality tests. In this paper, we develop a novel generalized Shapiro–Wilk ($GW$) test that explicitly accounts for this dependence. The proposed method rearranges the dependent observations into a random matrix and employs principal component analysis (PCA) to project this matrix onto a set of principal directions, achieving effective dimension reduction. On each projected direction, the scale-invariant Shapiro–Wilk statistic is applied to test for sphericity, leveraging the property that spherical distributions preserve the null distribution of such statistics. The Johnson $S_B$-transform is then used to approximate the null distribution of the combined test statistic. A Monte Carlo study demonstrates that the proposed $GW$ test controls type I error rates satisfactorily and exhibits strong power against a range of non-normal alternatives, including heavy-tailed and asymmetric distributions. The method is further illustrated using real alcohol use data from nested families, highlighting its practical utility. Comparative evaluation indicates that the $GW$ test performs favorably relative to a recently proposed approach. The procedure is applicable to balanced level-1 designs and provides researchers with a necessary diagnostic tool for assessing multinormality assumptions in two-level SEMs. Full article

In this paper, we introduce a new three-parameter lifetime distribution that is obtained via a power transformation of the modified bimodal exponential model. The inclusion of an additional shape parameter significantly enhances the flexibility of the baseline distribution, allowing it to capture a wide range of distributional characteristics, including skewness, heavy tails, and varying hazard rate shapes such as increasing, decreasing, and non-monotonic forms. Several important structural properties of the proposed model are derived, including explicit expressions for the probability density function, cumulative distribution function, moments, and moment generating function. Entropy measures such as Rényi entropy, Shannon entropy, and cumulative residual entropy are also obtained. Key reliability characteristics, including the survival function, hazard rate function, cumulative hazard function, reversed hazard rate, and mean residual life function, are investigated in detail. A theoretical result on the modality of the distribution is established, demonstrating its ability to exhibit both unimodal and bimodal shapes. Parameter estimation is carried out using maximum likelihood estimation along with several alternative methods. A comprehensive simulation study is conducted to evaluate the performance of the estimators under different parameter settings. Finally, the applicability and effectiveness of the proposed distribution are demonstrated through the analysis of real datasets from reliability and environmental studies. Comparative results based on goodness-of-fit measures indicate that the proposed model provides a superior fit compared to several existing competing distributions. Full article

Offshore wind energy droughts are quantified at eight strategic sites using a Standardized Renewable Energy Production Index referenced to an 86-year-ERA5 baseline. Events are extracted via multi-threshold run theory and projected to 2100 using bias-corrected CMIP6 models under SSP2-4.5 and SSP5-8.5. Drought climatology shows that mean drought duration and severity exhibit spatial heterogeneity, peaking in the Aegean–Cretan sector. Under future warming, a robust, false-discovery-rate-controlled intensification is predominantly concentrated in the central–western basin, associated with structural shifts toward weaker, heavy-tailed wind distributions. The Sicily Channel and Gulf of Lion emerge as hotspots for drought intensification, exhibiting consistent annual total duration increases of up to 20% and 15%, respectively, under the SSP5-8.5 scenario. Winter droughts across the basin are associated with a complex interplay of the AO, NAO, EA, and EA/WR teleconnections, alongside a pronounced winter MOI influence in the west. Summer droughts in the Aegean–Cretan sector are strongly coupled with the weakening of the MOI, reflecting the collapse of the basin-scale pressure gradient that sustains the Etesian winds. The central–western Mediterranean emerges as a key region for adaptive, long-duration energy storage planning, whereas the climatology of the eastern basin remains a defensible baseline for future capacity design. Full article

For lithium-ion batteries, reliable fault detection for charging voltage is essential for operational safety and thermal failure prevention. However, existing battery monitoring solutions face a dual challenge: task-specific models are primarily challenged by limited transferability, while powerful foundation models impose prohibitive computational demands that preclude their integration into resource-constrained edge devices. To address these challenges, this paper proposes a lightweight foundation model for fault detection built upon the IBM Granite TinyTimeMixer (TTM) foundation model. Firstly, we fine-tune the pre-trained TTM backbone with a hybrid loss using only few-shot normal charging sequences, enabling the model to learn the healthy voltage dynamics of batteries. Secondly, a dual-track data pipeline is proposed to adapt to irregular data, where a regular inference grid is generated in parallel with raw asynchronous measurements being retained for preserving vital high-frequency components. Thirdly, a vertical residual alignment mechanism is introduced to align irregular measurements with a continuous prediction curve derived from the TTM model’s grid prediction, enabling precise residual computation despite sampling mismatches. Finally, an empirical 99.99th quantile extreme threshold is calibrated using normal residual distributions to suppress false alarms caused by heavy-tailed sensor noise. Experiments on a lab dataset of 174 battery cells demonstrate that the proposed foundation model detects all fault batteries with zero false positives, which validates its effectiveness and robustness in battery fault detection. Full article

Protection levels for autonomous vehicle localization are traditionally derived from estimator covariances under Gaussian assumptions. These approaches fail in complex urban environments where sensor anomalies produce heavy-tailed, non-Gaussian error distributions. This paper presents a fundamentally different paradigm that reformulates integrity monitoring as a closed-loop control problem. The method computes an instantaneous error rate from three sources: inertial sensor noise, kinematic drift between filter-based and dead-reckoned displacement, and LiDAR scan-map registration quality weighted by a sensitivity factor. This rate drives a saturation-controlled setpoint dynamics, then an adaptive PID controller with entropy-based gain scheduling produces the final protection level. Asymmetric update laws enforce rapid expansion but cautious contraction of safety bounds. Experiments on three UrbanNavDataset sequences (medium-urban, low-urban, deep-urban) demonstrate that traditional covariance-based methods exhibit high integrity risk, while the proposed framework achieves $0.0\%$ risk in moderate environments and $2.3\%$ under extreme degradation. The resulting protection levels are smooth and well-behaved, compatible with modern motion planners. This control-theoretic approach offers a viable alternative to statistical integrity paradigms in challenging real-world conditions. Full article

We introduce a robust nonparametric regression framework for functional covariates that combines functional principal component analysis (FPCA), marginal copula-scale normalization, bounded-score M-estimation, and multivariate Bernstein smoothing. The proposed procedure reduces the infinite-dimensional functional predictor to a low-dimensional score representation, transforms the retained scores onto the compact unit cube, and estimates a conditional M-functional through a smoothly aggregated system of local estimating equations. This construction is designed to accommodate nonlinear regression structure, heavy-tailed score distributions, and response contamination while limiting the influence of extreme observations. Under suitable regularity and undersmoothing conditions, we establish pointwise and uniform consistency, derive explicit convergence rates, and prove asymptotic normality. The limiting variance contains an explicit Bernstein concentration factor that plays a role analogous to the integrated squared kernel in classical nonparametric regression. The analysis also clarifies the interaction among the projection dimension, the Bernstein resolution, the empirical copula transformation, and the effective local sample size. The finite-sample performance of the method is examined through simulations involving heavy-tailed functional scores, Student-t errors, nonlinear regression effects, and increasing response contamination. The proposed estimator exhibits strong overall predictive performance and good robustness, with particularly favorable behavior under absolute-error criteria. Full article

Traveling-wave fault location in distribution networks confronts two fundamental challenges: insufficient robustness of the cost function against heavy-tailed synchronization noise, and the location-resolution bottleneck imposed by the discrete search step. Starting from first physical principles, we identify that faulty-branch identity is encoded in the ordering pattern of multi-terminal TW arrival times rather than in their absolute values. Building on this insight, we propose a faulty-branch identification and precise fault-location method that integrates amplitude-assisted rank correlation (AAC) features with random forest (RF). At the theoretical level, we employ Hampel’s finite-sample breakdown-point framework to quantitatively establish that the $L_2$ cost function has an asymptotic breakdown point of zero, whereas the Spearman rank correlation coefficient attains an asymptotic breakdown point of 0.5—providing a rigorous robustness justification for replacing the $L_2$ residual with a rank-consistency cost. At the algorithmic level, the method consists of a three-stage inference pipeline: AAC computes a joint rank correlation cost for every line section across the network and extracts a 42-dimensional feature vector encompassing cost statistics, timing residuals, and topological attributes; feature selection is performed via fused ranking, which combines Pearson correlation, point-biserial correlation, and RF out-of-bag permutation importance through a weighted harmonic mean; the RF classifier directly performs branch identification over the full edge space, and the RF regressor predicts the coarse-location residual from local cost-terrain statistical features along the correctly identified branch, breaking through the 20 m search-step resolution bottleneck. We construct a five-layer physical noise model covering wavefront detection, time synchronization, wave-velocity deviation, reflected-wave misdetection, and terminal failure. Experiments on three structurally distinct 10 kV radial distribution network topologies, each with 5000 independently generated fault samples, demonstrate that branch identification accuracy remains stably above 94%, and residual correction reduces the mean location error from approximately 60 m to approximately 40 m—an improvement exceeding 30%—confirming the effectiveness of the physics–data hybrid framework for TW fault location in distribution networks. Full article

Volatility regime identification underpins risk management and portfolio allocation in quantitative finance, yet standard mixture models fail in heavy-tailed environments: components are consumed by outliers rather than genuine persistent regimes. We propose the Bayesian Spike-and-Slab Finite Mixture with Adaptive Tail Regularisation (BSS-FM-ATR), which resolves this at the component level via a spike-and-slab prior on the degrees-of-freedom parameter ${\nu }_k$. A latent binary indicator assigns each component to a slab state (data-driven tail adaptation for genuine regimes) or a spike state (inert heavy-tail absorber for artefacts). Applied to 19 JSE blue-chip securities over December 2019 to December 2025—spanning the COVID-19 crash and Eskom load-shedding episodes—BSS-FM-ATR achieves the highest silhouette score (0.3809 on the full dataset), regime persistence (0.9391), and interpretability (0.800) across nine standard baselines, including Gaussian HMM, MS-AR, MS-GARCH(1,1), and Bayesian Changepoint detection, plus three outlier-component comparators (Contaminated–Normal Mixture, TCLUST, and robust Bayesian mixture). A complete rerun after removing the contaminated observations confirms that ARI ($\Delta =-0.0283$) and persistence ($\Delta =+0.0105$) remain stable; the silhouette reduction (from 0.3809 to 0.3773) is a positive finding: it confirms that the spike-state component R3 was correctly identified as a compact, well-separated artefact cluster whose removal reveals the genuine regime structure. This dual validation establishes BSS-FM-ATR’s role as both a regime identifier and a data quality filter. The method isolates a Yahoo Finance data-contamination artefact ($n=21$, January–February 2025) through a principled spike-and-slab mechanism, providing an explicit posterior probability $p({\gamma }_3=0\mid \mathbf{X})>0.99$ of artefact status: a structural identifier that contaminated-normal and robust Bayesian alternatives cannot supply, establishing its value as both a regime identifier and a data quality filter for financial monitoring systems. Full article

In this paper, we introduce the type II exponentiated half logistic-odd log-logistic-G power series class of distributions for modeling symmetric, skewed and heavy-tailed data with diverse hazard rate shapes. The proposed class of distributions is obtained by compounding the generalized family of distributions involving the type II exponentiated half logistic-G and odd log-logistic-G families with a discrete power series distribution. Various statistical properties of the proposed class of distributions, including moments, survival and hazard rate functions, order statistics, probability weighted moments, and Rényi entropy are derived. The model parameters are estimated using different estimation methods, and their performance is evaluated through Monte Carlo simulation studies. Finally, the flexibility and applicability of the proposed class of distributions are illustrated using real data sets. The results demonstrate that the proposed model provides a better fit than several existing competing models. Full article

Laplace factor models (LFMs) provide a heavy-tailed alternative to Gaussian factor models by representing high-dimensional observations through a low-rank common component and Laplace-distributed idiosyncratic errors. This paper develops an assumption-consistent finite-sample analysis of matrix concentration, covariance estimation, and Monte Carlo integration under this model. We first formulate the model with explicit dimensional, independence, covariance, and identifiability conditions. Standard matrix Laplace-transform and matrix Bernstein inequalities are then recalled with their precise applicability conditions. Because untruncated Laplace variables are neither almost surely bounded nor strongly log-concave, these standard results cannot be applied directly in the forms commonly used for bounded or Gaussian-like observations. To address this issue, we analyze a coordinatewise truncated covariance estimator and derive an operator-norm bound that separates the stochastic estimation error from the truncation bias. The resulting rate depends on the effective rank and the logarithm of the ambient dimension and is therefore not dimension-free. For Monte Carlo integration, we replace strong-log-concavity arguments by a sub-exponential concentration analysis that is compatible with independent Laplace errors and yields non-asymptotic absolute- and relative-error bounds. Simulation studies compare empirical tails with the classical matrix Bernstein bound, evaluate ordinary, truncated, winsorized, PCA, POET-type, and Huberized covariance estimators, and we compare Laplace-based and Studentized confidence intervals. The results show that the classical Bernstein bound can be conservative, and truncation involves a substantial bias–variance trade-off. In a Wine chemical-analysis application, three factors explain 66.53% of the standardized variance, and POET-type covariance estimation attains a cross-validated balanced accuracy of 0.9901. These findings clarify both the scope and the limitations of finite-sample analysis for LFMs. Full article

Traditional Bayesian SAR detection methods suffer poor adaptability to speckle noise, fail to handle severe class imbalance within large-scale multi-target datasets, and incur prohibitive training overheads. To address these drawbacks, this paper develops a lightweight Bayesian detection and recognition framework built upon heavy-tailed Laplacian priors and variational inference. We adopt ResNet-50 as the feature extraction backbone and design a four-stage pipeline: First, a noise-aware Laplacian heavy-tailed prior is proposed to strengthen resistance against speckle outliers. Second, a multi-class variational inference module is constructed to eliminate detection bias induced by uneven sample distribution across target categories. Third, a lightweight uncertainty feedback strategy is introduced to cut computational costs for large-batch training. Evaluated on the MSAR-1.0 dataset, our approach achieves an mAP@0.5 of 94.98% and a macro balanced accuracy ($BA$) of 93.34%. Compared with existing Bayesian detectors, the mAP metric rises by 5.44–6.53%. The model only consumes 4.33 ms per inference frame and completes full training within 1.53 h on a single GPU. Ablation tests validate the independent and combined efficacy of all three core modules. This integrated architecture balances detection precision, classification reliability, and training efficiency, offering a promising prototype for multi-class SAR target interpretation under the evaluated benchmark constraints. Full article

Accurate rainfall information is essential for water-related hazard assessment in mountainous regions, where complex terrain and sparse gauge networks limit monitoring reliability. This study evaluated long-term rainfall characteristics and the performance of three satellite precipitation products—CHIRPS, GPM IMERG, and GSMaP—along the National Highway 6 corridor in northern Vietnam. Daily gauge observations from 11 meteorological stations with station-dependent records between 1961 and 2024 were used to characterize rainfall variability and heavy-rainfall frequency. Matched gauge–satellite records from 2001 to 2024 were evaluated using continuous statistical indicators, contingency-table metrics, empirical cumulative distribution functions, and percentile-based analysis. Data from 2025 were additionally examined as an extreme-rainfall case study, supplemented by visual gauge–satellite comparisons; however, these data were not used as an independent satellite-validation period. The long-term gauge records showed marked spatial variability in rainfall magnitude and threshold-exceedance frequency. In 2025, all 11 stations recorded daily rainfall exceeding 50 mm, while eight stations recorded events exceeding 100 mm. Satellite-product performance varied substantially among stations, rainfall thresholds, and evaluation metrics, and no product was uniformly superior. At the 100 mm/day threshold, the probability of detection ranged from 0.024 to 0.276, whereas the false alarm ratio ranged from 0.781 to 0.916. Upper-tail analysis showed contrasting product-specific behavior: at the 99th percentile, CHIRPS and GPM IMERG underestimated gauge rainfall by 20.95 and 8.95 mm, respectively, whereas GSMaP overestimated it by 35.57 mm. These findings demonstrate that local validation, uncertainty assessment, and application-specific adjustment are necessary before satellite precipitation products are used for flash-flood, rainfall-induced landslide, drainage-risk, and transportation-infrastructure assessments in mountainous regions. Full article

Financial tail-risk observations are non-exchangeable: serial dependence and regime shifts make their joint law depend on the time ordering, invalidating the exchangeability that standard conformal guarantees assume, and expected shortfall is not elicitable on its own, so a forecaster cannot be calibrated to it as a quantile is to its coverage. We ask whether a black-box value-at-risk and expected-shortfall forecaster can be calibrated under such dependence while retaining finite-sample guarantees. We tune a single inflation parameter by conformal risk control on a bounded monotone loss that couples value-at-risk breach frequency with breach magnitude normalised by the model’s predicted value-at-risk–expected-shortfall gap; the guarantee is thus for a tail-gap-normalised exceedance-severity surrogate, and its expected-shortfall reading depends on the predicted gap being a sound tail-gap estimate. Under exchangeability, the method gives finite-sample expected-risk control; for dependent data we invoke a non-exchangeable swap-distance bound and add, for separated calibration points, a regime-drift bound with an explicit cumulative $\beta$-mixing cost, plus a high-probability realised-path statement and a heavy-tail rate of order $D^{(p-1)/p}$. Building regimes causally from previous-month FRED-MD vintages across eight exchange rates, a Bitcoin series, and the GIFT-Eval finance domain, the weighted controller attains a $2.51\%$ violation rate and a Fissler–Ziegel score of $0.431$ against $0.441$ and $0.439$ for the strongest conformal baselines—an incremental gain, not significant at the $5\%$ level, that concentrates in turbulent regimes and at matched capital, supporting calibration of a joint frequency-and-normalised-severity budget rather than distribution-free control of the expected-shortfall forecast itself. Full article

Ion-type rare earth tailings are mechanically weak and may release Pb and Zn, posing both geotechnical and environmental risks. Basalt-fiber-reinforced microbially induced carbonate precipitation (BF-MICP) was investigated as a combined solidification/stabilization treatment for these tailings. By integrating peak and post-peak mechanical responses, heavy-metal leaching, the spatial distribution of calcium carbonate (CaCO₃), and microstructural characterization, this study distinguishes the respective contributions of microbial mineralization and fiber reinforcement. Tailings specimens were treated with basalt fiber contents ranging from 0 to 0.8% and evaluated using unconfined compression tests, leaching tests, CaCO₃ measurements, X-ray diffraction, Fourier-transform infrared spectroscopy, and scanning electron microscopy with energy-dispersive spectroscopy. The unconfined compressive strength first increased and then decreased with increasing fiber content, reaching 1.72 MPa at 0.4% fiber, approximately 90% higher than that of the MICP-only group. At the same fiber content, compressive total energy absorption increased from approximately 18 to 68 kJ·m⁻³, indicating a marked improvement in post-peak toughness. BF-MICP treatment increased the CaCO₃ content to approximately 2–3 times that of untreated tailings, although the deposits remained more abundant in the outer region than in the core, and the total CaCO₃ content varied little with fiber dosage. The leached concentrations of Pb and Zn decreased by 79–81% and 81–84%, respectively, with no clear additional reduction as the fiber dosage increased. Microstructural analyses showed that calcite-dominated deposits connected tailing particles and fiber surfaces. These results indicate that MICP primarily governed mineral cementation and heavy-metal immobilization, whereas basalt fibers mainly improved load transfer, crack bridging, and post-peak structural integrity. A fiber content of 0.3–0.4% provided the best overall balance between mechanical performance and leaching control. Full article