Search Results (1,517)

This study examines the relationship between managerial overconfidence and environmental, social, and governance (ESG) performance through firm-level financial policy channels in an emerging-market context. Using panel data from non-financial firms listed on the Indonesia Stock Exchange during 2015–2024, this study adopts a multidimensional channel-based perspective in which managerial overconfidence is indirectly reflected through financing, liquidity, and investment decisions. Fixed-effects estimation with Driscoll–Kraay standard errors is employed as the baseline approach and complemented by lagged specifications, system GMM estimation, alternative measurements, and quantile regressions to assess robustness. The findings suggest that managerial overconfidence does not exert a direct and uniform influence on ESG performance but operates indirectly through heterogeneous financial policy behavior. The financing channel provides weak and unstable evidence, whereas the liquidity channel shows a relatively stronger positive association with ESG performance. The investment channel appears most sensitive to measurement and model specification, indicating that different operationalizations may capture distinct dimensions of managerial overconfidence. This study contributes to the behavioral corporate finance and ESG literature by showing that managerial overconfidence influences sustainability outcomes indirectly through heterogeneous financial policy mechanisms in an emerging market setting while highlighting the importance of temporal dynamics, endogeneity, and measurement sensitivity. Full article

Count outcomes are commonly analyzed using Poisson regression, but empirical data often exhibit overdispersion, excess ties, heaping, or other departures from the Poisson distribution. This paper evaluates a rank-Poisson transformation, denoted poisrank, designed to map observed counts onto Poisson quantiles before fitting a Poisson regression model. Our goal is to test whether a rank-Poisson transformation offers a useful general-purpose strategy when count data do not satisfy Poisson assumptions. Using an empirical example and a Monte Carlo simulation study with Poisson, overdispersed, rounded, and gapped count distributions, we compared Poisson regression on raw counts, Poisson regression after the poisrank transformation, quasi-Poisson regression, and additional comparison approaches. Although the transformation made the marginal distribution more similar to a Poisson distribution, it generally did not outperform standard alternatives for inference. In particular, quasi-Poisson regression more consistently maintained appropriate rejection rates with overdispersion whereas poisrank tended to be conservative and often reduced power. These findings suggest that the rank-Poisson transformation is better understood as an exploratory robustness device than as a preferred replacement for established count-data methods. Full article

Accurate multi-horizon forecasting of reservoir water levels is essential for effective water resource management and flood risk mitigation. While deep learning models have demonstrated strong predictive capabilities, they often struggle to adequately represent uncertainty and extreme hydrological events, particularly at longer forecast horizons. This study proposes four variants of a Conv1D–LSTM–Temporal Attention (CLTA) architecture for probabilistic multi-horizon forecasting, differing exclusively in loss function design. The models incorporate non-crossing constraints, horizon-aware weighting, and event-aware weighting to address key limitations of standard quantile regression approaches. All models are trained on hourly water level data from May 2021 to October 2022 and evaluated on a fully unseen dataset spanning December 2022 to May 2023. The results demonstrate that horizon-aware weighting achieves the lowest average RMSE (0.0149) and the most stable performance across forecast horizons on unseen data, while event-aware weighting improves representation of extreme hydrological events and achieves the highest coefficient of determination ($R^2=0.9961$). However, a controlled experiment further reveals that model performance is strongly influenced by the data partitioning strategy, even when architecture and loss formulation are held constant. Overall, the findings indicate that loss function design, in interaction with data partitioning strategy, is a key contributing factor to model performance in deep learning-based hydrological forecasting. A Multi-Criteria Decision Analysis (MCDA) framework identifies the horizon-weighted model as the most robust general-purpose solution, while the event-aware model is preferable for applications focused on extreme event representation. These results highlight the importance of integrating domain knowledge into both model design and evaluation strategy, offering a scalable and computationally efficient alternative to increasing architectural complexity. Full article

Understanding how digital technology innovation affects the resilience of the forestry economy and the mechanisms at play is a critical step in resolving the “resource–development” contradiction and advancing the sustainable development of the forestry sector amid the ongoing technological revolution. This study uses panel data from 31 Chinese provinces, municipalities, and autonomous regions covering the period from 2004 to 2023. This study applies the entropy weight method to quantitatively assess forestry economic resilience and uses a two-way fixed effects model to empirically test the impact of digital technology innovation on resilience. It also examines how industrial structure upgrading and the digital divide influence this relationship. This study found that digital technology innovation significantly enhances forestry economic resilience. The industrial structure upgrading of forestry plays a minor mediating role in this effect. The first- and second-level digital divides negatively moderate the impact of digital technology innovation on resilience, while the third-level divide has no significant effect. Additionally, the impact of digital technology innovation varies across different quantiles and climate risk levels. Full article

We propose a new nonparametric estimator of the conditional mode in a regression framework where the covariates are functional in nature. The estimator is constructed through a quantile regression approach, which provides a robust alternative to classical density-based procedures. It is well documented that employing the $L^1$-structure in quantile regression, the estimation procedure improves robustness properties, particularly resistance to outliers and heavy-tailed error distributions. This feature makes the $L^1$estimation of the conditional mode more stable and reliable in complex and high-variability functional data settings. The main objective of this paper is to establish strong consistency, with explicit convergence rates, for the associated kernel estimators, uniformly over a range of bandwidth parameters. The latter is developed under general regularity conditions involving the concentration distribution of the functional regressor, smoothness assumptions on the structural components of the model, and entropy conditions ensuring adequate control of the functional class complexity. Uniformity in bandwidth is essential both from a theoretical and practical issues, as it guarantees stability of the estimator under data-driven smoothing parameter selection. Beyond its theoretical contribution, this paper has direct implications for applied statistics. Specifically, it provides mathematical support for the automatic bandwidth selection procedures in the high-dimensional data context. Furthermore, the main theoretical novelty is highlighted through simulation experiments and applications to real data. Full article

While the association between criteria air pollutants and sleep duration is well-documented, evidence on the impact of fine particulate matter (PM_2.5) chemical components on sleep remains limited. This study investigated the effects of intermediate- (6-month) and long-term (2-year) exposure to PM_2.5 and its five major components—black carbon (BC), organic matter (OM), sulfate (SO₄²⁻), nitrate (NO₃⁻), and ammonium (NH₄⁺)—on nocturnal sleep and daytime napping duration. We included 19,505 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018). Residential PM_2.5 and component concentrations were estimated via the Tracking Air Pollution in China dataset, and sleep data were collected through self-reported questionnaires. Linear mixed-effects models and quantile-based g-computation (qgcomp) were used to assess single- and multi-pollutant effects. Results showed that both intermediate- and long-term exposure to PM_2.5 components was associated with shorter nocturnal sleep and longer daytime napping. Subgroup analyses revealed greater susceptibility among rural residents, solid fuel users, and individuals without pensions. These findings emphasize the need for component-specific PM_2.5 control strategies and targeted public health interventions to reduce sleep-related health inequalities, especially in socioeconomically disadvantaged populations. Full article

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving point-prediction accuracy while overlooking effective multivariate dependency modeling and reliable uncertainty quantification, limiting both the informativeness and reliability of their forecasts. This study proposes a Fractional-order Momentum optimized Evidential iTransformer (FoM-EiT) for short-term wind power forecasting from multivariate time series. The proposed model integrates cyclic feature encoding for periodic variables, an inverted Transformer for variable-wise interaction learning, and an evidential output head that jointly produces point forecasts and uncertainty estimates from a shared representation. The proposed fractional-order momentum (FoM) optimization accumulates gradient history over an extended window, thereby smoothing oscillations caused by gradient competition and stabilizing the joint training process. Experiments on four real-world wind farms from different geographical regions show that FoM-EiT achieves competitive point forecasting performance, with $R^2$ values of 0.6342, 0.8211, 0.7844, and 0.9161, and the Wilcoxon signed-rank test indicates that its advantages over the baselines are statistically significant in the vast majority of comparisons. For uncertainty quantification, FoM-EiT achieves Prediction Interval Coverage Probability (PICP) values of 0.9492, 0.9682, 0.9709, and 0.9498, while the Winkler score results further show that its prediction intervals outperform the conformal prediction and quantile regression baselines in terms of overall interval quality. These results indicate that FoM-EiT provides both accurate forecasts and trustworthy uncertainty information, making it a practical tool for dispatch, reserve allocation, and risk-aware short-term power system operation. Full article

This paper introduces a novel continuous probability distribution on the unit interval called the unit Jamal distribution and explores its properties. The proposed distribution performs well in modeling bathtub-shaped data, effectively capturing its characteristic hazard rate behavior. Key mathematical characteristics such as moments, the moment generating function, order statistics, entropy, and the quantile function are thoroughly derived. Parameter estimation is conducted using maximum likelihood and Bayesian estimation methods. A simulation study is conducted to evaluate the accuracy of parameter estimates and to examine the distribution’s behavior. Additionally, the applicability of the proposed distribution is demonstrated through analysis of two real-world datasets, allowing for a comparison of its performance against existing models. Full article

Agricultural green development (AGD) is usually evaluated by its average productivity and environmental effects, but its value may be more visible when crop production is exposed to severe growing-season climate stress. This study examines whether AGD acts as a state-dependent buffer against climate-related output losses. Using a balanced panel of 56 prefecture-level cities in China’s Yellow River Basin from 2011 to 2024, we construct a Crop Extreme Stress Index (CESI) from daily meteorological records and estimate a two-stage least squares model with a crop-group shift-share instrument and crop-group price controls. The results show that AGD has a positive average association with crop output, but its marginal payoff is substantially larger in high-exposure years. In the preferred interaction specification, a one-standard-deviation increase in AGD is associated with approximately 2.8% higher crop output under high exposure. Quantile regressions further suggest that this protective effect is more visible in weaker output states. Channel consistency tests indicate that resilience capacity and crop diversification are more relevant under high exposure, although these results should not be interpreted as causal mediation. The findings suggest that AGD should be assessed not only by average productivity gains, but also by its capacity to reduce losses and stabilize output under growing-season climate extremes. Full article

Quantifying the mixture effects on humans exposed remains challenging because mixture components are correlated and may act bidirectionally by exhibiting nonlinear dose-response relationships, which may contribute to subclinical organ dysfunction. The liver is a vital organ in the body with broad functions, making it vulnerable to injury as it is the first organ exposed to circulating toxicants, which can precipitate hepatic damage. Our study’s objective was to evaluate the combined and component-specific associations of a multi-chemical exposure mixture of heavy metals, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (dioxins), and polychlorinated dibenzofurans (furans), with liver biomarkers, and to compare concentration-based results with the toxic equivalent (TEQ) potency of the weighted results for dioxin-like compounds. In an unweighted analytic sample of U.S. adults from NHANES 2003–2004 with 947 complete cases, we examined heavy metals (cadmium, lead, and mercury), PCBs (12 congeners), dioxins (7 congeners), and furans (10 congeners) in relation to eight liver biomarkers (albumin, ALP, ALT, AST, GGT, LDH, total bilirubin, and total protein). We applied multi-exposure linear regression, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR), with parallel TEQ-based models using WHO 2005 TEFs for dioxin-like PCBs, dioxins, and furans. Across mixture methods, the mixture structure was chemically sparse, with a limited set of recurring contributors. Total bilirubin showed the most consistent positive mixture association across qgcomp and BKMR and persisted under TEQ weighting, with prominent PCB- and dioxin-like contributions (notably PCB81/PCB TEQs and dioxin-related components). Albumin demonstrated inverse mixture patterns in BKMR and TEQ-BKMR, with dioxin-like components (notably Dioxin3 and Dioxin3_TEQ) repeatedly emerging as key drivers. For ALT, ALP, AST, GGT, LDH, and total protein, overall mixture effects were frequently attenuated or null in qgcomp despite structured component weights, indicating bidirectional sub-mixtures and internal counterbalancing. BKMR PIPs similarly concentrated on a small number of dominant predictors (e.g., lead for ALP, mercury for ALT, PCB28 for AST, and cadmium and PCB189 for LDH), while interaction summaries provided limited evidence of stable non-additivity. Using multiple complementary mixture methods, we identified outcome-specific mixture patterns suggesting hepatobiliary vulnerability. TEQ concordance supports toxicological relevance of the dioxin-like axis, while metals and non–dioxin-like mechanisms likely contribute additional pathways. Full article

This study examines the role of investor sentiment in asset pricing within a frontier market, focusing on Vietnam. Using a comprehensive dataset covering the period 2015–2025 with 4018 observations, sentiment indices are constructed from both market-based and survey-based indicators. The study employs a quantile causality approach and a Quantile Vector Autoregression (QVAR) model to capture nonlinear, asymmetric, and state-dependent relationships among investor sentiment, stock returns, and market volatility. The empirical results provide several important findings. First, investor sentiment significantly influences stock returns, with stronger effects observed at extreme quantiles corresponding to bearish and bullish market conditions. Second, the impact is heterogeneous across firm sizes, with small-cap stocks exhibiting greater sensitivity to sentiment fluctuations. Third, the impact of investor sentiment on volatility is proxy-dependent and state-dependent. The market-based sentiment measure is generally associated with lower volatility at middle and upper quantiles, whereas the survey-based sentiment proxy shows stronger effects at lower quantiles, particularly during distress periods. Finally, robust bidirectional causality is identified between sentiment and market variables, suggesting the presence of feedback mechanisms between investor behavior and market performance. These findings highlight the importance of behavioral factors in shaping market dynamics in frontier markets characterized by high retail participation and limits to arbitrage. The study contributes to the literature by providing new quantile-based evidence on the nonlinear and asymmetric effects of investor sentiment in Vietnam. Full article

To address the issues of insufficient robustness, large depth recovery errors, and poor scene adaptability currently present in the initialisation phase of event-based stereo visual inertial SLAM systems, we propose a global BA initialisation method based on an adaptive threshold robust kernel function, ES-ATRK. The algorithm first achieves spatio-temporal fusion of events and visual features. Event features are triangulated to obtain depth values that serve as the 3D map, whilst visual features provide 2D observations; both modalities jointly feed the Structure from Motion (SfM) pipeline, laying the foundation for global bundle adjustment (BA) optimisation. The core contribution lies in incorporating a robust kernel function into the global BA to suppress outlier interference and in designing an adaptive thresholding algorithm that dynamically determines the kernel threshold. Furthermore, the algorithm calculates an initial threshold based on the quantile distribution of residuals prior to BA optimisation, combined with validity checks and a multi-round iterative smoothing adjustment strategy, thereby achieving scene-adaptive thresholding. In over 85% of the test scenes on the VECtor dataset, its localisation accuracy improved by at least 10% compared to existing mainstream event-based SLAM methods, such as ESVIO and USLAM. In high-dynamic scenes, its ATE performance is approximately twice that of mainstream models such as ESIO, and it maintains excellent positioning accuracy and stability of three-axis errors in generalisation tests on the HKU and MVSEC datasets. Furthermore, in the large-scale outdoor testing scenarios of the DSEC dataset, ES-ATRK also demonstrates superior feature tracking and trajectory estimation performance. This method effectively enhances the robustness of initialisation and depth recovery performance in event-based stereo visual inertial SLAM systems, reduces overall positioning error, and offers greater adaptability in challenging scenarios such as low-texture, high-dynamic, and HDR environments. Full article

High penetration of distributed photovoltaic (PV) systems has significantly increased microgrid net load volatility and uncertainty, posing challenges for conventional point forecasting methods that fail to provide sufficient operational risk information. To address this, this study proposes a multi-step net load forecasting framework that explicitly accounts for renewable energy fluctuations and system dynamics. A multi-quantile model generates 90% confidence prediction intervals for 1 and 4 h horizons at 15 min resolution. To mitigate under-coverage caused by cumulative errors, a stepwise conformal calibration strategy is applied to adjust each forecasting step independently, enhancing interval reliability and consistency. Net load volatility scenarios derived from PV ramping intensity are used to analyze uncertainty evolution under low, medium, and high fluctuation conditions. Case studies based on a high-PV microgrid dataset from eastern China demonstrate that calibrated intervals improve coverage, particularly in high-volatility scenarios, and, when integrated into rolling energy management, enhance battery state-of-charge safety margins and reduce peak grid import with minimal additional cost. The approach maintains point forecast accuracy while providing interpretable net load risk bounds, supporting informed scheduling and demand management in high-renewable microgrids. Full article

Background: Dietary cystine may influence lipid metabolism, but epidemiological evidence in children is limited. This study aimed to investigate the association between dietary cystine intake and dyslipidemia and its subtypes in Chinese children. Methods: Data were derived from the China National Nutrition and Health Surveillance of Children and Lactating Mothers (CNNHSCLM). After propensity score matching (1:1, caliper = 0.2), 3676 children aged 6–17 years (1838 with dyslipidemia, 1838 controls) were included. The Quantile g-computation (qgcomp) model assessed the joint effect of 20 amino acids. Multivariate logistic regression, subgroup analysis, restricted cubic splines (RCS), and five machine learning models (including XGBoost with Shapley Additive Explanation (SHAP) analysis) were applied to evaluate the association between cystine intake and dyslipidemia. Results: The qgcomp model showed that cystine had a negative weighting contribution to reducing the risk of hypo-HDL cholesterolemia. Multivariate logistic regression revealed that cystine intake was significantly negatively correlated with hypo-HDL cholesterolemia (OR = 0.67, 95%CI: 0.53–0.86, p = 0.002) and total dyslipidemia (OR = 0.84, 95%CI: 0.74–0.96, p = 0.010), but not with other subtypes. Subgroup analyses indicated interactions with BMI and sex. RCS showed a non-linear dose–response relationship for hypo-HDL cholesterolemia and a linear negative relationship for total dyslipidemia. The XGBoost model achieved the best predictive performance (AUC = 0.902), and SHAP analysis identified cystine as the most important feature inversely associated with dyslipidemia. Decision curve analysis confirmed its clinical net benefit. Conclusions: Dietary cystine intake is negatively associated with the risk of hypo-HDL cholesterolemia and total dyslipidemia in children, and cystine is an important negative correlate of dyslipidemia. These findings provide new scientific evidence for dietary prevention of dyslipidemia in children. Full article

Researchers often develop ordinal hazard distributions, whether increasing or decreasing, into multi-parameter distributions to derive various forms of the hazard function. This process necessitates the formulation of a multi-parameter hazard function, which involves a more complex mathematical expression. In contrast, this study introduces a new one-parameter lifetime model, termed the Inverted Z–Lindley (IZL) distribution, which is capable of capturing an upside-down bathtub-shaped failure rate without sacrificing analytical simplicity. Fundamental distributional properties of the IZL model are rigorously established, including closed-form expressions for the probability density, cumulative distribution, reliability, and hazard rate functions. Theoretical analysis shows that the density is strictly positive, unimodal, positively skewed, and heavy-tailed, while the hazard rate is unimodal with vanishing limits at both extremes. Fractional moments are obtained, and the non-existence of classical moments is formally justified, motivating the use of quantile-based and inactivity-time reliability measures. Besides the quantile function, several key reliability measures, including the mean inactivity time and strong mean inactivity time functions, and order statistics, are also developed. Inferential procedures are constructed under Type-II censoring using both likelihood-based and Bayesian frameworks. The existence and uniqueness of the frequentist estimator are established, while Bayesian estimation is implemented via Markov chain Monte Carlo methods under informative gamma priors. Several interval estimation techniques—including asymptotic, bootstrap, Bayesian credible, and highest posterior density intervals—are developed and compared through extensive Monte Carlo simulations. The practical relevance of the proposed model is demonstrated using real datasets from environmental health and communication engineering, where the IZL distribution consistently outperforms fifteen well-established inverted lifetime models according to likelihood-based criteria, information measures, and goodness-of-fit diagnostics. Overall, the IZL model offers a powerful, interpretable, and computationally efficient alternative for modeling heavy-tailed lifetime data with non-monotone failure behavior, contributing meaningfully to modern distribution theory and applied reliability analysis. Full article