Consistent estimation of low-rank spatial covariance matrix: A penalized random effects approach

Type 2 diabetes mellitus and sarcopenia are increasingly recognized as interrelated syndromes sharing pathophysiological pathways, yet empirical evidence for their reciprocal, time-dependent relationship in aging populations remains limited. Conventional analytical approaches fail to capture the dynamic, trajectory-based nature of their coprogression because of restrictive assumptions regarding state transitions and sojourn time. We conducted a prospective cohort study using harmonized data from 2 nationally representative Chinese longitudinal surveys: the China Health and Retirement Longitudinal Study and the Chinese Longitudinal Healthy Longevity Survey, with follow-up spanning 2008-2018. A semi-Markov multi-state model was employed to estimate sojourn-time-dependent transition hazards across 5 clinically defined states: diabetes-free/sarcopenia-free, diabetes-only, sarcopenia-only, comorbid diabetes-sarcopenia, and death (absorbing state). All models adjusted for time-varying sociodemographic, lifestyle, and functional covariates and accounted for competing risks. Population attributable fractions were derived via counterfactual simulation. The semi-Markov model demonstrated superior fit over the Markov specification (global ΔAkaike information criterion = 316.4), confirming strong sojourn-time dependence in transition risks. Incident diabetes was associated with an 82% higher hazard of progressing to comorbid diabetes-sarcopenia (hazard ratio = 1.82, 95% confidence interval [CI] = 1.47-2.25), while incident sarcopenia conferred a 65% elevated risk of subsequent diabetes onset (hazard ratio = 1.65, 95% CI = 1.35-2.02). These bidirectional associations remained robust across multiple sensitivity analyses. Counterfactual modeling revealed that 38.2% (95% CI = 31.5%-44.1%) of new comorbid cases could be prevented by blocking diabetes→sarcopenia progression, and 32.6% (26.8%-38.0%) by interrupting sarcopenia→diabetes progression. Strikingly, 58.9% (53.4%-64.1%) of all 5-year mortality was attributable to entry into any chronic disease state. These findings suggest that diabetes and sarcopenia are dynamically and bidirectionally associated in aging Chinese adults. Integrated assessment of glycemic status and muscle health may help identify older adults at elevated risk of comorbidity and mortality. Further interventional studies are needed to determine whether dual-domain prevention strategies can modify these trajectories.

Ruxolitinib pharmacokinetics (PK) has been characterized in clinical trials but remains poorly documented in real-world practice. This project aimed to investigate ruxolitinib PK in routine clinical practice, identify factors driving its variability, and explore exposure-response relationships to assess the potential role of therapeutic drug monitoring. In total, 221 steady-state ruxolitinib concentrations from 77 adult patients enrolled across several centers in Switzerland were analyzed. Demographic and clinical data were recorded at each visit. Population pharmacokinetic (popPK) analysis was performed with MonolixSuite® 2024R1 (Lixoft, France). Model-based simulations were used to predict trough concentration (Cmin) and the area under the concentration-time curve over 24 h (AUC24) for the recommended dosage regimens, as a function of covariates. An exploratory pharmacokinetic/pharmacodynamic analysis was conducted in the overall cohort. Ruxolitinib PK was characterized by a marked between-subject variability in clearance (45%). Strong cytochrome (CYP) 3A4 or dual CYP2C9/CYP3A4 inhibitors reduced clearance by 39%. At 10 mg twice daily, model-based simulations showed CYP inhibitors increased median Cmin and AUC24 by 2.9- and 1.7-fold, respectively. Still, no significant exposure-efficacy relationship was identified. However, higher ruxolitinib exposure tended to be associated with increased toxicity in the overall population (Cmin and AUC24, P≈0.02-0.03). In clinical practice, ruxolitinib exposure shows substantial variability and is strongly affected by strong CYP inhibitors that are frequently co-administered. Whilst no clear exposure-efficacy relationship was observed, higher exposure was linked to increased toxicity risk, arguing for careful dose adjustment.

Recurrent health events often involve complex inter-relationships between longitudinal biomarkers and time-to-event outcomes, further complicated by sparse, irregular data collection and time-dependent correlations among events. Traditional statistical methods frequently struggle with these complexities, resulting in biased estimates and suboptimal modeling performance. To address these challenges, we propose the Functional Regression with AutoregressIve fraiLTY (FRAILTY) method, a novel framework designed to jointly model longitudinal measurements and recurrent events, accommodating both scalar and functional covariates while capturing time-dependent correlations among events. The FRAILTY method employs a two-step estimation procedure. First, functional principal component analysis through conditional expectation (PACE) is applied to extract key temporal features from sparse and irregular longitudinal data. Second, the obtained scores are incorporated into a dynamic recurrent frailty model with an autoregressive structure to account for within-subject correlations across recurrent events. Simulation studies demonstrated that the FRAILTY method outperformed existing methods, such as those relying on B-spline basis functions and Bayesian joint modeling, by achieving lower integrated mean squared errors, higher concordance indices, and greater statistical power in detecting functional parameters. Its practical utility was further validated through applications to two datasets: the Systolic Blood Pressure Intervention Trial study and the Multicenter Collaboration to Study Treatment Outcomes in Nephrolithiasis Evaluation cohort.

In clinical trials with prioritized composite outcomes, win ratios are commonly employed to evaluate the efficacy of investigational interventions. Adjusting such win ratios with respect to covariates enhances the accuracy and precision of treatment effect estimates, by controlling for baseline clinical characteristics, demographics, etc. Effects of the covariates on composite outcomes are often of complex and non-linear nature, reflecting the complexity of underlying biological mechanisms. Parametric approaches that assume an additive effect of covariates on the log-hazard often fail to capture such complexities, resulting in unreliable inferences and reduced predictive accuracy. In this article, we introduce a flexible win fraction regression framework based on [Formula: see text]-splines, that is capable of assessing the extent and nature of the functional effects of covariates on the outcome adaptively. By leveraging the moment condition model framework equipped with the generalized method of moments (GMM) technique, we develop an efficient computational algorithm to carry out inference based on the proposed model. Using the asymptotic distribution of the estimated spline coefficients, we develop a large-sample test to assess the significance of the functional covariates. The favorable operating characteristics of the proposed methodology, compared to the current state-of-the-art, are assessed through extensive simulations. Finally, the practical utility of our proposal is demonstrated through the analysis of composite time-to-event datasets arising from cardiovascular and breast cancer clinical trials.

Interpersonal Communication and Maternal Behavioral Practice: A Case Study of Explanatory Causal Machine Learning in Nepal.

Optimizing dalbavancin dosing for complicated methicillin-resistant Staphylococcus aureus infections: a multicentre population pharmacokinetic study.

Study region: Pakistan exhibits pronounced spatial and seasonal variability in rainfall patterns, with extreme precipitation posing major challenges for flood risk management, agriculture, and infrastructure planning. Accurate estimation of return levels remains difficult due to sampling variability, threshold selection, and the presence of zero-inflation associated with dry days. The present study analyzes daily precipitation data from 135 districts across Pakistan for the period 2001–2023, encompassing diverse climatic changes. Study focus: A zero-inflated Extended Generalized Pareto Distribution (ziEGPD) model is introduced to characterize the full precipitation spectrum, including dry days, within a regional modeling framework. Within the regional model setting, homogeneous regions based on upper-tail behavior were constructed using the Δ ˆ ratio method. To capture spatial variation in the region, the parameters of ziEGPD were modeled as functions of covariates using Generalized Additive Models (GAMs). The developed model was estimated through maximum-likelihood and Bayesian frameworks and evaluated via cross-validation using accuracy and robustness measures. New hydrological insights: Results indicate that the Bayesian GAM–ziEGPD model with covariates provides the highest accuracy and consistently outperforms the MLE-based approach across all seasons and clusters. This model is therefore adopted as the optimal framework for estimating regional quantiles across a range of return periods. Regional analysis further shows that the monsoon period exhibits the highest 100-year return levels, with intense rainfall concentrated in southern and southeastern Pakistan. • Comprehensive modeling of rainfall including zero values and extremes. • Integration of regional frequency analysis and spatial GAM framework. • MLE along with Bayesian inference for robust and accurate estimation. • Return levels for climate resilience and flood risk management.

With the advancement of neuroscience and computer science, electroencephalography (EEG) has drawn increasing attention as a promising modality for biometric identification, owing to its universality, permanence, and security. However, existing studies have pointed out that maintaining stable and temporally robust inter-individual features remains a major challenge in EEG-based identification. Therefore, developing effective methods for cross-time EEG-based identity recognition is essential for achieving reliable and practical biometric systems. In this study, we propose a novel EEG-based identification framework grounded in symmetric positive definite (SPD) manifolds. Specifically, we utilize the spatial covariance matrices of EEG signals to represent individual differences and introduce an enhanced feature extraction method (E-SPD-M) that simultaneously captures temporal, spatial, and spectral characteristics. These matrices are embedded into the Riemannian manifold to construct a discriminative representation space. For each subject, we build a personalized classification model and integrate their outputs to achieve accurate identification. Furthermore, we construct a comprehensive multi-task, cross-time EEG dataset and validate our approach on both our dataset and a publicly available longitudinal EEG dataset (M3CV). Experimental results demonstrate that our method achieves superior cross-time identification performance. Overall, this work offers a novel pathway for improving EEG-based biometric algorithms and extending the application of Riemannian geometry in the field.

The reliable operation of wind turbines is critical for generating low-carbon electricity in renewable energy systems. To maximize turbine uptime and minimize maintenance disruptions, smart condition monitoring and early fault detection strategies are essential. Yaw pitch failures, a common cause of performance degradation in wind turbines, are challenging to detect due to the complex relationship between wind speed, yaw pitch current, and grid current. This study proposes a Gaussian Process (GP) regression framework with square exponential covariance functions for early detection of yaw pitch failures in wind turbines. By analysing Supervisory Control and Data Acquisition (SCADA) data from a 2.5 MW wind turbine over a six-month operational wind farm, we establish predictive models for three critical performance relationships: power curve (R² = 0.951, RMSE = 68.5 kW), yaw pitch current versus wind speed (R² = 0.893, RMSE = 0.82 A), and yaw pitch current versus grid current (R² = 0.908, RMSE = 0.74 A). The yaw pitch current versus grid current reference curve demonstrates superior fault detection performance, identifying faults 80 minutes after threshold exceedance with minimal false alarms, significantly outperforming power curve-based detection and wind speed-based detection. Fisher's combined probability test with an optimized threshold (p = 0.581) effectively balances detection sensitivity and false alarm minimization. The results demonstrate the model's ability to detect yaw pitch faults (>6 A) effectively with minimal false alarms, offering a cost-effective SCADA-based solution for wind turbine condition monitoring that leverages the strong correlation (r = 0.79) between grid current and yaw pitch current.

Data-driven emulation of peak storm surge has emerged as a popular strategy for overcoming limitations arising from the computational burden of high-fidelity hydrodynamic numerical models used within coastal risk assessment applications. The surrogate models (also known as metamodels) used for this emulation are developed using suites of synthetic storm simulations, and once calibrated, can replace the original high-fidelity model to establish predictions for new storms. These predictions pertain to the geographic domain, and therefore nodal locations, covered by the original high-fidelity simulation suite. This creates a two-dimensional space for the peak surge predictions, with one (primary) corresponding to the storm features (i.e., the storm parametric description) and the other (secondary) to the spatial domain. Gaussian Process (GP) techniques have emerged as a widely popular surrogate modeling technique for peak surge emulation. In all GP implementations so far, the spatial variability has been incorporated in the analysis through the metamodel output, considering a multi-output GP implementation. This approach fails to explicitly model spatial dependencies for the peak surge. To address this shortcoming, this study examines an alternative implementation that considers spatial and storm feature variability as part of the metamodel input, establishing a surrogate model that simultaneously predicts the peak storm surge (scalar output) across both the spatial domain and the storm features. For computational tractability, a separable covariance function is considered for the GP, establishing separate kernels for the spatial and storm feature spaces. Particularly for the spatial domain, an adaptive covariance tapering formulation, which infuses sparsity in the corresponding covariance matrix, is adopted to support applications with a large number (in the order of thousands) of nodal locations. A simultaneous calibration approach for the hyperparameters of the separate kernels is further proposed to improve emulation accuracy. Comparisons of computational efficiency and accuracy of the alternative GP implementations are established utilizing the Coastal Hazards System–North Atlantic (CHS-NA) database, with those employing the adaptive covariance tapering formulation evaluated under varying sparsity levels. The case study demonstrates that the simultaneous hyperparameter calibration is beneficial for the separable GP’s predictive accuracy, particularly as it relates to the worst-performing nodes in the domain, and that the imposed sparsity level impacts the separable GP’s ability to model non-stationary spatial trends in the domain. • Gaussian-Process based emulation of peak storm surge is examined • Spatial and storm feature variability are considered as part of the metamodel input. • Separable covariance kernel is adopted, establishing separate kernels for the spatial and storm feature spaces • Adaptive sparse covariance tapering is utilized to accommodate large spatial domain applications • The impact of the sparsity level on the established accuracy for the spatial interpolation is examined.

Quantile regressions offer several attractive features, including the ability to allow covariate effects to vary at different quantile levels and to effectively handle heteroscedasticity in data, which makes it a viable alternative for analyzing data with continuous outcomes in recent years. It has been used in modeling survival data with and without a cured fraction. In this work, we propose novel estimating equation approaches to estimate a mixture cure model where the latency survival time is modeled using a quantile regression. Our proposed estimation methods provide double robustness, meaning that a misspecification in one part of the mixture cure model will not affect the estimation in the other part. The methods relax the restrictive global log-linear assumption that is typically found in existing quantile regressions, and they allow for both quantile-varying and quantile-invariant effects in the regression when the log-linear assumption holds within a certain range of quantile levels. We established the asymptotic properties of the proposed estimators, and our simulation studies demonstrated their double robustness and efficiency gains. An application of the proposed model and methods to data from a lung cancer study revealed that uncured patients with adenocarcinoma have significantly longer quantiles in the survival time than uncured patients with squamous cell carcinoma, which had not been reported in previous analyses of the data due to the limitations of the existing methods.

Hand gesture recognition plays an important role in human–computer interaction, yet accurately modeling both spatial structure and temporal motion patterns in video-based vision systems remains challenging. Many existing approaches focus on either spatial appearance or motion information, which can limit their ability to capture the full complexity of dynamic hand gestures evolving over time. In this work, we present a unified feature representation framework that combines spatial descriptors modeled on the Symmetric Positive Definite (SPD) manifold with temporal motion features extracted from gesture video sequences using grid-based optical flow histograms in Euclidean space. Spatial covariance descriptors are mapped from the SPD manifold to a Euclidean space through the Log-Euclidean metric, enabling effective feature fusion while preserving intrinsic geometric properties. The resulting representation captures complementary spatial and temporal information in a compact and interpretable form. We evaluate the proposed framework on two publicly available video-based benchmark datasets for dynamic hand gesture recognition, the Cambridge Hand Gesture dataset and the Northwestern University Hand Gesture dataset. Experimental results demonstrate that the combined representation consistently improves classification performance compared to using spatial or temporal features alone, achieving 99.31% accuracy on the Cambridge dataset and 97.23% on the Northwestern dataset. These findings indicate that integrating manifold-aware spatial features with motion-based temporal cues provides a practical and effective solution for robust dynamic hand gesture recognition.

The need to explore and/or optimize expensive simulators with many qualitative factors arises in broad scientific and engineering problems. Our motivating application lies in path planning – the exploration of feasible paths for navigation – which plays an important role in robotics and assembly planning. For complex settings, the parameter space for path exploration can be discrete and high-dimensional, and the evaluation of path feasibility requires expensive virtual simulations. A carefully selected experimental design is thus essential for timely decision-making. We propose here a novel framework called QuIP, for experimental design of a Gaussian process (GP) surrogate with Qualitative factors via Integer Programming. QuIP leverages a GP surrogate with an exchangeable covariance function. For initial design, we show that its maximin design can be formulated as an assignment problem from operations research, which can be efficiently and globally optimized via state-of-the-art integer programming solvers. For sequential design (specifically, for active learning or black-box optimization), we show that its design problem can similarly be formulated as an assignment problem, which facilitates efficient and reliable optimization with state-of-the-art solvers. We demonstrate the effectiveness of QuIP over existing design methods in a suite of path planning experiments and an application to rover trajectory optimization.

Abstract Whittle–Matérn fields are a recently introduced class of Gaussian processes on metric graphs, specified as solutions to a fractional-order stochastic differential equation. Unlike previous covariance-based methods, these fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties, including the consistency and asymptotic normality of maximum likelihood estimators and the necessary and sufficient conditions for optimal prediction with misspecified parameters. The covariance function is generally unavailable in closed form, which makes statistical inference challenging. However, we show that for specific values of the fractional exponent where the fields exhibit Markov properties, likelihood-based inference and spatial prediction can be performed exactly and efficiently. This enables the use of Whittle–Matérn fields in large datasets without approximations. The results and methods are illustrated through simulation studies and through an application to traffic data modelling, where allowing for differentiable processes significantly improves results.

In this paper, we propose a new nonparametric method for estimating the regression operator of a scalar response given a functional covariate taking values in a semi-metric space. The estimator is obtained by minimizing the Least Absolute Relative Error (LARE) criterion, which provides a scale-invariant and equilibrated measure of prediction accuracy compared with classical regression loss functions. The antisymmetry property of the LARE rule ensures that overestimation and underestimation are penalized in a symmetric relative manner, which improves the robustness when the response variable varies in different scales. Next, the estimator is constructed using k-nearest neighbors (kNN). The combination of the two algorithms allows the procedure to benefit from both the robustness and scale-invariant nature of the LARE criterion and the flexibility and local adaptivity of the kNN smoothing approach, which is particularly suitable for functional or high-dimensional data. As an asymptotic result, we establish the uniform convergence with respect to the number of neighbors (UNN) of the proposed estimator under mild regularity conditions and derive its rate of convergence. We also discuss the selection of the optimal number of neighbors and their impact on performance. The practical effectiveness of the proposed kNN–FLARE regression estimator is illustrated through simulation experiments and an application to near-infrared (NIR) spectrometry data.

Abstract Repurposing of oil and gas wells for geothermal energy involves several critical steps, including assessing the well’s structural integrity, evaluating the reservoir’s thermal properties, and modeling the potential energy output. The development of geothermal systems is naturally uncertain due to sub-state conditions, thermal conductivity, and inequalities in fluid flow behavior. Reliable performance forecasting and optimal well design depend on this rigorous modeling and simulation. As a result, global interest in sustainable energy has accelerated the use of abandoned oil and gas wells as viable resources for geothermal energy production. This study investigates the feasibility of repurposing abandoned oil wells in the Volve oil field in the North Sea as geothermal energy sources. This research shows the effects of petrophysical properties (i.e., permeability and porosity) on enthalpy production derived from reservoir modeling, and examines the uncertainty in these properties to achieve more accurate reservoir characterization and modeling. The following methodological framework was employed: First, reliable and coherent three-dimensional models of petrophysical properties were developed using geostatistical sequential simulations to improve well log data and spatial patterns as revealed by spatial covariances and variograms. Next, these models were updated to align with historical production data. After validation, the models were utilized to forecast enthalpy production. Based on the results, the Volve reservoir can produce an average of 2,094 MWh of geothermal energy each year, yielding a total energy output of about 41,877 MWh over a 20-year operating period.

Wearable devices collect time-varying biobehavioral data, offering opportunities to investigate how behaviors influence health outcomes. However, these data often contain measurement error and excess zeros (due to nonwear, sedentary behavior, or connectivity issues), each characterized by subject-specific distributions. Current statistical methods fail to address these issues simultaneously. We introduce a novel modeling framework for zero-inflated and error-prone functional data by incorporating a subject-specific time-varying validity indicator that explicitly distinguishes structural zeros from intrinsic values. We iteratively estimate the latent functional covariates and zero-inflation probabilities via maximum likelihood, using basis expansions and linear mixed models to adjust for measurement error. To assess the effects of the recovered latent covariates, we apply joint quantile regression across multiple quantile levels. Through extensive simulations, we demonstrate that our approach significantly improves estimation accuracy over methods that only address measurement error, and joint estimation yields substantial improvements compared with fitting separate quantile regressions. Applied to a childhood obesity study, our approach effectively corrects for zero inflation and measurement error in step counts, yielding results that closely align with energy expenditure and supporting their use as a proxy for physical activity.

Habitat-based BART models for cetaceans in the western South Atlantic: current and future distribution under climate change scenarios.

Multimodal EEG study of emotional face processing in major depressive disorder.