Search Results (48)

Accurately predicting the remaining useful life (RUL) of UAV bearings is challenging due to maneuver-shock-induced stochastic jumps during their two-phase degradation, while existing numerical methods are computationally too costly for UAV onboard computing. To address this, an analytical RUL prediction method considering stochastic jumps is proposed. A two-phase Wiener process incorporating stochastic jumps is constructed to model degradation processes involving shocks. Subsequently, a combined Kalman Filter–Rauch–Tung–Striebel Smoothing–Expectation Maximization (KF-EM-RTS) framework is developed for simultaneous online updating of drift and diffusion coefficients. Furthermore, utilizing Stein’s Lemma, an analytical expression under a fixed-change-point assumption for the RUL probability density function (PDF) of the proposed model is derived, thereby reducing the reliance on repeated numerical integration. Under the experimental settings used in this study, the analytical implementation reduces the single-point PDF calculation time by approximately 90% compared with the corresponding numerical integration implementation, which is important for compute-limited UAV platforms. Moreover, RMSE is decreased by 48% and 76% versus models ignoring jumps. This approach offers a lightweight solution for real-time predictive maintenance of UAVs. Full article

Multicollinearity reduces the reliability of ordinary least squares by increasing variances and creating unstable estimates. This issue has led to biased and penalized regression methods like ridge-, Liu- and Stein-type estimators. Here, we build existing ridge-type approaches by introducing improved ridge and Liu-type estimators, along with robust variants to handle outliers. We investigate their theoretical properties regarding bias, variance, and mean squared error. We also evaluate their performance through Monte Carlo simulations with different levels of multicollinearity and data contamination. By using several evaluation criteria, including mean squared error, akaike information criterion, mean absolute deviation, and mean absolute percentage error, along with an average-rank comparison framework applied here for the first time, we further validate our results with two health-related datasets. The findings show that the strong estimators provide more stable estimates and improved predictive performance, particularly when dealing with severe multicollinearity and outliers. Full article

Background: Missing data, particularly progression-driven dropout, introduces substantial bias in longitudinal oncology studies, directly impacting response classification based on RECIST criteria. While machine learning-based imputation methods are increasingly used, their performance is rarely evaluated in a clinically interpretable framework centered on patient-level endpoints such as Best Overall Response (BOR). Methods: We propose a clinically grounded evaluation framework based on RECIST 1.1 focused on patient-level Best Overall Response classification. Longitudinal tumor trajectories were simulated for 270 patients (1:1, HER2+ and HER2−) across nine follow-up visits using both Gompertz and Stein–Fojo growth models, resulting in 2700 patient-visit observations. Realistic missingness was introduced through a combination of random mechanisms and progression-driven dropout. Three machine learning imputation models, long short-term memory (LSTM), MissForest, and Multiple Imputation (MI) were evaluated under both direct (MAR-based) and non-responder imputation strategies. Performance was assessed using BOR classification metrics, including accuracy and Cohen’s kappa. Result: Across both simulation frameworks, imputation substantially improved BOR classification performance. Under the Gompertz model, accuracy increased from 0.84–0.89 with direct imputation to 0.94–0.99 with non-responder imputation, with corresponding kappa improvements from 0.73–0.82 to 0.90–0.99. Similar trends were observed under the Stein–Fojo model (accuracy: 0.82–0.84 vs. 0.91–0.96; kappa: 0.69–0.72 vs. 0.86–0.94). Across all evaluated methods, NRI improved classification performance by approximately 10 percentage points in accuracy and up to 17 percentage points in kappa. The improvement was observed consistently across both tumor growth models and different missingness scenarios, demonstrating the robustness of the findings. Conclusions: This study demonstrates that successful handling of missing data depends not only on the imputation method itself, but also on the choice of a clinically meaningful endpoint and appropriate estimand strategies aligned with the underlying missing data assumptions. In the METABRIC-derived simulations, clinically informed handling of progression-related missingness substantially improved RECIST-based BOR classification across all evaluated methods, suggesting that appropriate endpoint selection and the corresponding estimand strategy for missing data handling may have a greater influence on classification performance than the choice among the imputation models applied. Full article

Knowledge of the Oestroidea and Muscoidea fauna of Greece and Cyprus remains fragmentary, with substantial parts of the two countries having never been systematically surveyed. The present study verifies the presence of Scathophaga stercoraria (Linnaeus, 1758) in Cyprus and records 16 new species from Greece, belonging to five calyptrate families: [Anthomyia illocata Walker, 1856 (Muscoidea: Anthomyiidae); Scathophaga lutaria (Fabricius, 1794) (Muscoidea: Anthomyiidae); Fannia pallitibia (Rondani, 1866); Fannia pusio (Wiedemann, 1830) (Muscoidea: Fanniidae); and Coenosia sp. nov. 1, Coenosia sp. nov. 2, Lispe flavicincta Loew, 1847, Lispe nuba Wiedemann, 1830, Lispe orientalis Wiedemann, 1824, Lispe cf. sericipalpis (Stein, 1904), Potamia littoralis Robineau–Desvoidy, 1830 (Muscoidea: Muscidae); Apodacra radchenkoi Verves and Khrokalo, 2015, Craticulina tabaniformis (Fabricius, 1805), Miltogramma rutilans Meigen, 1824, Nyctia lugubris (Macquart, 1843) (Oestroidea: Sarcophagidae), and Linnaemya lithosiophaga (Rondani, 1859) (Oestroidea: Tachinidae)]. These records are based on the examination of 152 dry-pinned specimens from 58 localities, collected between 1978 and 2026 across Greece and Cyprus using a combination of passive (animal-baited traps, UV-bright pan traps) and active (hand collecting, net sweeping) sampling methods, together with insect material from the entomological collections of the National Museum of Natural History Goulandris and the Melissotheque of the Aegean. In addition, the first checklists of the family Fanniidae and the subfamily Scathophaginae for Greece and Cyprus are presented. Collectively, the findings presented expand the documented diversity of Greek and Cypriot Calyptratae and refine the current understanding of their biogeographic patterns, providing an updated framework for taxonomic, ecological, forensic, and other applied entomological research within the two countries. Full article

This paper proposes a Stein Variational Gradient Descent (SVGD) solution framework for Model Predictive Control (MPC) to enhance robustness in bipedal locomotion. While conventional simplified rigid-body dynamics (SRBD) based MPC with deterministic solvers such as the active-set method performs reliably during straight-line walking, its performance degrades significantly in agile maneuvers, where modeling simplifications become more pronounced. Simulations on the 43-DoF QingLoong humanoid demonstrate that SVGD-MPC exhibits improved robustness in dynamic locomotion tasks, including high-angular-velocity turning and circular walking. However, conventional MPC fails to maintain stability. Rather than relying on a single nominal solution, SVGD-MPC maintains an ensemble of candidate control trajectories that evolve jointly within the MPC optimization process. This ensemble-based formulation reduces sensitivity to modeling inaccuracies and allows the controller to retain feasible control candidates when the assumptions underlying the SRBD model are violated. The method sacrifices marginal tracking precision to maintain viability under model inadequacy. Moreover, our framework establishes a paradigm for robust locomotion control where the traditional optimization method fails to work. 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

The two-parameter Birnbaum–Saunders (B-S) distribution is widely applied across various fields due to its favorable statistical properties. This study aims to enhance the efficiency of modified moment estimators for the B-S distribution by systematically incorporating auxiliary non-sample information. To this end, we developed and analyzed a suite of estimation strategies, including restricted estimators, preliminary test estimators, and Stein-type shrinkage estimators. A pretest procedure was formulated to guide the decision on whether to integrate the non-sample information. The relative performance of these estimators was rigorously evaluated through an asymptotic distributional analysis, comparing their asymptotic distributional bias and risk under a sequence of local alternatives. The finite-sample properties were assessed via Monte Carlo simulation studies. The practical utility of the proposed methods is demonstrated through applications to two real-world datasets: failure times for mechanical valves and bone mineral density measurements. Both numerical results and theoretical analysis confirm that the proposed shrinkage-based techniques deliver substantial efficiency gains over conventional estimators. Full article

Ship acoustic signal classification is essential for vessel identification, underwater navigation, and maritime security. Traditional methods struggle with the non-stationary nature and noise of ship acoustic signals, reducing classification accuracy. To address these challenges, we propose an automated pipeline that integrates Empirical Mode Decomposition (EMD), adaptive wavelet filtering, feature selection, and a Bayesian-optimized Random Forest classifier. The framework begins with EMD-based decomposition, where the most informative Intrinsic Mode Functions (IMFs) are selected using Signal-to-Noise Ratio (SNR) analysis. Wavelet filtering is applied to reduce noise, with optimal wavelet parameters determined via SNR and Stein’s Unbiased Risk Estimate (SURE) criteria. Features extracted from statistical, frequency domain (FFT), and time–frequency (wavelet) metrics are ranked, and the top 11 most important features are selected for classification. A Bayesian-optimized Random Forest classifier is trained using the extracted features, ensuring optimal hyperparameter selection and reducing computational complexity. The classification results are further enhanced using a majority voting strategy, improving the accuracy of the final object identification. The proposed approach demonstrates high accuracy, improved noise suppression, and robust classification performance. The methodology is scalable, computationally efficient, and suitable for real-time maritime applications. Full article

Background: Maxillary sinus floor augmentation (MSFA) is commonly used to increase posterior maxillary bone volume prior to implant placement. Although generally successful, late complications can impact long-term outcomes. The purpose of the study was to estimate the incidence and timing of atypical late complications following (MSFA) using bovine xenohybrid bone grafts. The study also aimed to evaluate whether preoperative bone volume is associated with the risk of complications. Methods: This retrospective cohort study was conducted at the Center of Oral and Maxillofacial Surgery, Danube Private University, Krems-Stein, Austria, and included patients who underwent MSFA with bovine xenohybrid bone grafts and either simultaneous or staged implant placement between January 2020 and December 2023. Preoperative bone volume of the posterior maxilla measured via cone beam computed tomography (CBCT) in the planned implant insertion position. The primary endpoint was the time (days) from MSFA to the occurrence of a graft-related complication (defined as atypical if occurring more than 6 months after MSFA and not related to peri-implantitis) The covariates included subjects’ age, sex, the quantity of graft used for MSFA, timing of dental implant insertion (simultaneous vs. staged) and implant dimensions. Kaplan–Meier analysis and Cox proportional hazards regression were used to evaluate time-to-event data. Only one graft site per patient was analyzed. Results: Atypical complications occurred in 9 out of 47 patients (19.1%), with an average time to onset of 645 days. In a multivariable analysis, a lower preoperative bone volume was found to be an independent predictor of an increased risk of complications (hazard ratio [HR]: 0.972; 95% confidence interval [CI]: 0.925–1.021; p = 0.252). However, the quantity of graft used for MSFA was not found to be a predictor (p = 0.46). Conclusions: Within the limitations of a retrospective study, reduced native bone volume appears to increase the risk of atypical late complications following MSFA with bovine xenohybrid grafts. This makes closer clinical and radiologic follow-up of patients over a longer period very necessary. Full article

A Bayes estimator for a mean parameter of an exponential distribution is calculated using Stein’s loss, which equally penalizes gross overestimation and underestimation. A corresponding Posterior Expected Stein’s Loss (PESL) is also determined. Additionally, a Bayes estimator for a mean parameter is obtained under a squared error loss along with its corresponding PESL. Furthermore, two methods are used to derive empirical Bayes estimators for the mean parameter of the exponential distribution with an inverse gamma prior. Numerical simulations are conducted to illustrate five aspects. Finally, theoretical studies are illustrated using Static Fatigue 90% Stress Level data. Full article

This study includes a comprehensive evaluation of six penalty estimation strategies for partially linear models (PLRMs), focusing on their performance in the presence of multicollinearity and their ability to handle both parametric and nonparametric components. The methods under consideration include Ridge regression, Lasso, Adaptive Lasso (aLasso), smoothly clipped absolute deviation (SCAD), ElasticNet, and minimax concave penalty (MCP). In addition to these established methods, we also incorporate Stein-type shrinkage estimation techniques that are standard and positive shrinkage and assess their effectiveness in this context. To estimate the PLRMs, we consider a kernel smoothing technique grounded in penalized least squares. Our investigation involves a theoretical analysis of the estimators’ asymptotic properties and a detailed simulation study designed to compare their performance under a variety of conditions, including different sample sizes, numbers of predictors, and levels of multicollinearity. The simulation results reveal that aLasso and shrinkage estimators, particularly the positive shrinkage estimator, consistently outperform the other methods in terms of Mean Squared Error (MSE) relative efficiencies (RE), especially when the sample size is small and multicollinearity is high. Furthermore, we present a real data analysis using the Hitters dataset to demonstrate the applicability of these methods in a practical setting. The results of the real data analysis align with the simulation findings, highlighting the superior predictive accuracy of aLasso and the shrinkage estimators in the presence of multicollinearity. The findings of this study offer valuable insights into the strengths and limitations of these penalty and shrinkage strategies, guiding their application in future research and practice involving semiparametric regression. Full article

Biomedical researchers typically investigate the effects of specific exposures on disease risks within a well-defined population. The gold standard for such studies is to design a trial with an appropriately sampled cohort. However, due to the high cost of such trials, the collected sample sizes are often limited, making it difficult to accurately estimate the effects of certain exposures. In this paper, we discuss how to leverage the information from external “big data” (datasets with significantly larger sample sizes) to improve the estimation accuracy at the risk of introducing a small amount of bias. We propose a family of weighted estimators to balance bias increase and variance reduction when incorporating the big data. We establish a connection between our proposed estimator and the well-known penalized regression estimators. We derive optimal weights using both second-order and higher-order asymptotic expansions. Through extensive simulation studies, we demonstrate that the improvement in mean square error (MSE) for the regression coefficient can be substantial even with finite sample sizes, and our weighted method outperformed existing approaches such as penalized regression and James–Stein estimator. Additionally, we provide a theoretical guarantee that the proposed estimators will never yield an asymptotic MSE larger than the maximum likelihood estimator using small data only in general. Finally, we apply our proposed methods to the Asia Cohort Consortium China cohort data to estimate the relationships between age, BMI, smoking, alcohol use, and mortality. Full article

Let G be a random variable of functionals of an isonormal Gaussian process X defined on some probability space. Studies have been conducted to determine the exact form of the density function of the random variable G. In this paper, unlike previous studies, we will use the Stein’s method for invariant measures of diffusions to obtain the density formula of G. By comparing the density function obtained in this paper with that of the diffusion invariant measure, we find that the diffusion coefficient of an Itô diffusion with an invariant measure having a density can be expressed as in terms of operators in Malliavin calculus. Full article

The problem of testing the equi-correlation coefficient of a standard symmetric multivariate normal distribution is considered. Constrained Bayesian and classical Bayes methods, using the maximum likelihood estimation and Stein’s approach, are examined. For the investigation of the obtained theoretical results and choosing the best among them, different practical examples are analyzed. The simulation results showed that the constrained Bayesian method (CBM) using Stein’s approach has the advantage of making decisions with higher reliability for testing hypotheses concerning the equi-correlation coefficient than the Bayes method. Also, the use of this approach with the probability distribution of linear combinations of chi-square random variables gives better results compared to that of using the integrated probability distributions in terms of providing both the necessary precisions as well as convenience of implementation in practice. Recommendations towards the use of the proposed methods for solving practical problems are given. Full article

This paper studies the optimal asset allocation problem of a defined contribution (DC) pension plan with a stochastic salary and value under a constraint within a stochastic volatility model. It is assumed that the financial market contains a risk-free asset and a risky asset whose price process satisfies the Stein–Stein stochastic volatility model. To comply with regulatory standards and offer a risk management tool, we integrate the dynamic versions of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and worst-case CVaR (wcCVaR) constraints into the DC pension fund management model. The salary is assumed to be stochastic and characterized by geometric Brownian motion. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. By using the Lagrange multiplier method and the dynamic programming method to maximize the constant absolute risk aversion (CARA) utility of terminal wealth, we obtain closed-form expressions of optimal investment strategies with and without a VaR constraint. Several numerical examples are provided to illustrate the impact of a dynamic VaR/CVaR/wcCVaR constraint and other parameters on the optimal strategy. Full article