Modeling and correction of sensitivity thresholds determined by best EstimateThreshold (BET)

Bootstraps for dynamic panel threshold models

ABSTRACT Shallow landslides threaten infrastructure, ecosystems, and human life in mountainous and hilly regions. Accurate forecasting remains challenging due to complex environmental interactions and spatial heterogeneity. We develop a probabilistic multi-factor model based on a generalized linear mixed-effects framework (GLMM) to predict shallow landslide occurrence. The model incorporates lithology, soil type, normalized difference vegetation index (NDVI), slope, slope aspect, elevation, rainfall intensity and duration as fixed effects, while capturing spatial and temporal grouping via random effects. We apply the model to an extensive inventory in Guangdong Province, China, encompassing 2,167 shallow landslide events and 2,166 control cases from 2013 to 2022. Compared to rainfall intensity–duration (I–D) and event–duration (E–D) threshold models, the GLMM approach delivers significantly higher predictive performance, achieving over 80% accuracy while maintaining a strong balance between sensitivity and specificity, enabling more reliable alerts and fewer unnecessary evacuations. Rainfall intensity and duration exert the strongest positive predictors, while NDVI displays a marked negative effect. GLMM offers interpretable coefficients and accommodates grouped environmental data, making it suitable for regional-scale landslide risk assessment. Our results highlight the value of integrating multi-dimensional predictors and offer a reliable, interpretable tool for operational landslide early warning systems.

ABSTRACT Resource-rich economies often face the carbon curse, where heavy reliance on natural resources leads to higher emissions and slower environmental progress. Policy tools such as technological innovation, green growth, and green finance are widely promoted to address these challenges. However, their effectiveness varies, and linear models may overlook important threshold effects, where these tools only work after reaching critical levels of development or investment. This study interrogates the existing evidence and offers some masterpiece pathways for escaping the carbon curse of natural resources. Using a dataset from 2000–2023 and Hansen (1999) threshold model, the findings are as follows: The linear model shows that technological innovation and green finance are positively associated with carbon emissions and energy intensity. Green growth appears more beneficial, showing negative effects on emissions across models. However, threshold analysis reveals that all three policy instruments – technological innovation, green growth, and green finance – only yield significant environmental benefits above specific critical levels. Below these thresholds, their impact is either negligible or harmful, reinforcing the idea that policy maturity and scale are essential. These findings highlight the need for resource-rich countries to exceed key policy and investment thresholds to achieve effective decarbonization. A targeted approach to scaling innovation, strengthening green growth frameworks, and strategically allocating green finance is crucial to escaping the environmental traps of resource dependence.

The study examines the relationship between FDI spillover and green total factor productivity (GTFP) at the provincial level, as well as exploring the roles of digital transformation and human capital upgrading in this relationship using the global Malmquist-Luenberger index, system generalized methods of moments (S-GMM) combined with threshold model. Using a panel data from 63 Vietnamese provinces from 2011 to 2022, the research findings show that the FDI spillover has contributed significantly to province-level GTFP enhancement and both digital transformation, human capital upgrading positively moderate the effect of FDI spillover on provincial GTFP. Besides, the positive influence of FDI spillover on GTFP has a significant double threshold effect when advanced human capital is used as the threshold variable. These findings emphasize the significance of encouraging FDI spillover, enhancing digital transformation, and strengthening high-quality human capital as viable strategies for improving GTFP.

ABSTRACT This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing in a common risk factor. We provide simple and interpretable conditions on conditional default probabilities that imply a comparison of credit portfolio losses in convex order. In the case of threshold models, the ranking of portfolio losses is based on a pointwise comparison of the underlying copulas. Our setting includes as a special case the well known Gaussian copula model but allows for general tail dependencies, which are crucial for modeling credit portfolio risks. Moreover, our results extend the classical parameterized models, such as the industry models CreditMetrics and KMV Portfolio Manager, to a robust setting where individual parameters or the copula modeling the dependence structure can be ambiguous. A simulation study and a real data example under model uncertainty offer evidence supporting the effectiveness of our approach.

The study examined the effect of external debt on economic growth; evaluate the role of institutional quality; determine the direction of causality; and establish the threshold of external debt relative to GDP for the Nigerian economy. Secondary annual time series data with key variables like economic growth (proxied by GDP), external debt (proxied by external debt outstanding), and institutional quality (proxied by six World Governance Indicators, including Voice & Accountability and Control of Corruption), financial development, human capital, and trade openness were included. The data were sourced from the CBN, World Bank, and World Governance Indicators, and were analyzed using advanced econometric techniques, specifically the Vector Error Correction Model (VECM) and a Nonlinear Threshold model. The analysis revealed that external debt has a positive and significant effect on economic growth in both the short and long run in Nigeria. Additionally, institutional quality also has positively impacts on economic growth. Causal analysis showed unidirectional causality running from economic growth to external debt, from institutional quality to external debt, and from economic growth to institutional quality. Crucially, a critical external debt-to-GDP ratio of 5.4822% was established as the threshold for the Nigerian economy. From the results, external debt can stimulate growth in Nigeria, particularly when combined with improved institutional quality (e.g., better rule of law and corruption control). However, to achieve rapid and sustainable economic development while avoiding debt servicing burdens that constrain future investment, the Nigerian government should explore alternative infrastructure financing mechanisms. It is recommended to shift reliance from huge external debt to Public-Private Partnership (PPP) models such as Build-Operate-Transfer (BOT) and similar arrangements.

To investigate deep-learning (DL) model accuracy in quantifying multifidus (MF) and erector spinae (ES) fat fraction (FF) compared to Dixon MRI, and to explore the indirect effect of muscle function between muscle degeneration and disability outcomes in chronic low back pain (CLBP). 96 CLBP and 86 healthy participants underwent 3 T MRI, muscle function assessment, Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), and Short Form 36-Health Survey (SF-36). A DL-Otsu thresholding model quantified muscle FF and functional muscle volume from 3D T2_WI images, validated against Dixon-FF. Lin's concordance correlation coefficient (CCC), Bland-Altman, and Passing-Bablok analyses assessed the concordance between Otsu-FF and Dixon-FF. Partial correlations and mediation analysis examined associations among muscle degeneration, muscle function, and disability outcomes. Otsu-FF showed agreement with Dixon-FF (MF: CCC = 0.96, 95% CI: 0.95, 0.97; ES: CCC = 0.95, 95% CI: 0.94, 0.96; bias: MF = 0.009; ES = 0.021). Partial correlations revealed MF and ES FF correlated with disability scores (ODI/RMDQ: r = 0.25 to 0.49; SF-36: r = -0.42, -0.28, p < 0.01). Muscle endurance negatively correlated with ODI (r = -0.57, 95% CI: -0.65, -0.45) and RMDQ (r = -0.49, 95% CI: -0.61, -0.35), positively with SF-36 (r = 0.51, 95% CI: 0.38, 0.63) (p < 0.01). Muscle endurance showed indirect effects on associations between muscle FF and disability outcomes (mediation proportion: 27.12% to 100%). DL method accurately quantified muscle FF, closely matching Dixon results. Muscle FF correlated with disability outcomes in CLBP, with muscle endurance demonstrating a statistically indirect association within this relationship. Question What are the associations between the deep learning-derived paraspinal muscle degeneration index, muscle function, and lumbar disability outcomes among patients with chronic low back pain? Findings In chronic low back pain, deep learning-quantified higher fat fraction of paraspinal muscles correlated with worse lumbar disability outcomes, with muscle endurance demonstrating an indirect effect in this association. Clinical relevance Incorporating the fat fraction of multifidus and erector spinae muscles and muscle endurance assessment is helpful for targeting rehabilitation training in chronic low back pain, improving disability outcomes.

This article investigates the relationship between financial liberalization and financial development in Pakistan, using human capital as a threshold variable over the period 1972–2023. The research finds that human capital must reach a value of 1.66 for overall financial liberalization to effectively take place. To gain deeper insights, the study decomposes overall financial liberalization into internal and external components. The estimated threshold values of human capital are 1.66 for internal and 1.53 for external financial liberalization. The results show that financial development is hindered when external financial liberalization remains below the human capital threshold, whereas the opposite effect is observed for internal financial liberalization. Once the human capital surpasses the threshold, both internal financial liberalization and external financial liberalization, as well as overall financial liberalization, positively contribute to financial development in Pakistan. These findings confirm that the positive relationship between financial liberalization and financial development is conditional upon reaching a specific threshold level of human capital. However, the relatively small magnitudes associated with each type of financial liberalization suggest that further reforms in financial sector policies are necessary. A reformed financial sector will then be better positioned to play a leading role in achieving sustainable economic growth. To this end, more focused efforts are required to improve the quality of human capital in Pakistan, ensuring its productive contribution to the finance–growth nexus. Additionally, macroeconomic variables also support financial development. Therefore, policymakers must ensure the effective utilization of physical capital stock, government expenditures and the maintenance of moderate inflation levels to foster the development of the financial sector.

This study explores how economic complexity shapes carbon dioxide (CO₂) emissions in the Gulf Cooperation Council (GCC) countries from 1990 to 2022. Using a dynamic panel threshold model, we investigate whether rising economic complexity creates a turning point that changes how economic growth, energy use, trade openness, and renewable energy adoption affect environmental outcomes. The analysis uncovers a clear threshold level of economic complexity that divides the sample into two distinct regimes. In the low-complexity regime, CO₂ emissions are highly sensitive to economic growth and energy consumption, while neither renewable energy nor trade openness plays a meaningful role in reducing emissions. In contrast, once countries cross into the high-complexity regime, the picture changes: energy intensity declines, renewable energy becomes a powerful tool for lowering emissions, and trade openness supports cleaner production and greener technologies. This shift reflects a partial decoupling of economic growth from environmental damage. Overall, the findings highlight how structural transformation and technological upgrading can support climate goals in hydrocarbon-dependent economies. By prioritizing innovation, diversification, and strong policy frameworks, GCC countries can advance toward carbon neutrality while maintaining economic momentum.

Energy consumption structure transformation (ECST) is essential to decrease pollutant emissions and realize green development. By constructing fixed effects, spatial Durbin, and threshold models, this paper adopts the panel data of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration to investigate the impact and spatial spillover effect of ECST on urban green development efficiency (GDE). The study also reveals the moderating effect of industrial digitization level (IDL) on the main effect and the varying trend of effect intensity across different threshold intervals. This study represents the first comprehensive investigation into the impact of energy consumption structure transformation on urban green development efficiency, broadening the research perspective on energy consumption structure. The innovative incorporation of spatial dimensions and industrial digitization threshold effects provides theoretical support and practical guidance for regional collaborative development and precise policy regulation. We found the following: (1) ECST can enhance BTH’s urban GDE; (2) The effect of ECST on BTH’s urban GDE shows a U-shaped relationship of first inhibition and then enhancement; (3) ECST has a spatial spillover effect and significantly contributes to enhancing BTH’s urban GDE; and (4) IDL can significantly increase BTH’s urban GDE and has the moderating effect of strengthening ECST for increasing urban GDE. As IDL increases, its moderating effect on the main effect displays a steady and gradual decreasing trend.

Agricultural insurance, as a stabilizer, is crucial for the promotion of agricultural modernization. Therefore, exploring the impact mechanism of agricultural insurance on agricultural modernization and seeking ways to promote it has important practical significance. This study uses China’s provincial panel data from 2008 to 2023 to empirically analyze the direct effect of agricultural insurance on agricultural modernization. The mediation effect, spatial Durbin, and threshold models are used to further explore the internal mechanism of agricultural insurance on agricultural modernization. Results reveal that (1) agricultural insurance plays a significant role in promoting agricultural modernization, with its robustness verified across various models and endogeneity tests. (2) Agricultural insurance can promote agricultural modernization effectively by expanding the scale of agricultural operations, increasing agricultural capital input, enhancing agricultural technology input, and promoting green agricultural production. (3) Agricultural insurance has a positive spatial spillover effect on the development of agricultural modernization in neighboring provinces. Furthermore, there is a threshold effect of agricultural insurance in promoting agricultural modernization, showing stronger effects in rural areas where the human capital level exceeds the single threshold or where the economic development level falls between the single and triple thresholds. (4) Heterogeneity analysis reveals that agricultural insurance exerts stronger promotional effects on agricultural modernization in non-grain-producing regions, in eastern and central areas, and during the initial stages of insurance development. The study proposes recommendations such as the differentiated promotion of agricultural insurance, enhancing the directionality of agricultural insurance policies, and improving the linkage mechanism between agricultural insurance and credit.

Objective: Dementia causes degeneration of neurons in brain. This disorder prognosis is difficult to understand in a non-invasive manner. This study is an attempt to understand CSF texture variations across different dementia severity stages using optimized machine learning model. Since this biomarker has not been fully explored for different severity changes of brain diseases. Methods: In this study, ADNI database is considered to attain normal (HC), EMCI, MCI, LMCI and AD images. The skull stripped images are segmented using multilevel Tsallis based moth flame optimization. Further, GLCM, ZM, LBP, ST and PHOG features are extracted to study pathological texture variation in CSF. Multi-subspace randomization and collaboration (MSRC) based feature selection method is used to attain significant feature. Finally, SVM performs multiclass classification. Findings: Results indicate that the proposed method precisely segmented CSF region for all considered images with high correlation values of 0.9. The MSRC method identifies appropriate rank for the considered feature vectors based on the performance in multiple subspaces. ANOVA test identifies optimal features set with a p-value &lt; 0.001 for classification. It is noticed that SVM classifier classify HC (99%) , EMCI (97%), MCI (96%), LMCI (98%) and AD (96.1%) images with better prediction. Novelty: The novelty of this work is to integrate optimized thresholding model, texture analysis, and statistical validation to establish a measurable link between CSF biomarkers and dementia severity stages. Hence, it could offer better clinical insights for clinicians to make appropriate decisions regarding the progression of dementia severity stages and its underlying changes. Keywords: Dementia, Cerebrospinal fluid (CSF), Moth flame optimization, Texture feature, Multi-subspace randomization and collaboration (MSRC)

Purpose The main objective of this research is to investigate empirically the nonlinear dynamic panel public debt threshold impacts on economic growth in European emerging countries from 2008 to 2022. The study seeks to determine the threshold level of public debt below which it will have a beneficial influence on economic growth and beyond which it will have a harmful influence. Design/methodology/approach To estimate the dynamic non-linear relationships between public debt and economic growth, the study employs a dynamic panel threshold model developed by Kremer et al. (2013) that expands on Hansen’s (1999) original static threshold specification. Findings The findings indicate that in the economies of European emerging countries, 79.40% is the turning point for the public debt-to-GDP ratio. Hence, the results indicate that European emerging economies that already have public debt beyond the threshold value should take measures to reduce their debt level in order to promote long-term economic growth. Practical implications The research offers insightful evidence and cautionary warnings to the European emerging countries about the level of public debt in terms of targeting public debt to stimulate growth. Originality/value The paper contributes to the existing literature by identifying a dynamic threshold effect of the public debt-to-GDP ratio and the role of government in the European emerging countries in determining the levels of public debt in these countries.

Genetic background of calving ease in beef-on-dairy.

Background/Objectives: The progression of chronic kidney disease (CKD) is influenced by multiple factors, complicating the determination of the optimal timing for hemodialysis preparation. The aim of this study was to identify predictive factors and develop a model to guide this timing in patients with CKD. Methods: This retrospective study included patients who progressed to end-stage kidney disease (ESKD) and initiated hemodialysis after at least one year of follow-up at a single tertiary hospital between January 2011 and June 2024. The estimated glomerular filtration rate at 6 months before hemodialysis initiation (eGFR_6M), indicating timing for vascular access creation, and its decline trajectory were retrospectively analyzed according to underlying diseases and clinical conditions. A regression model was developed, and its performance was evaluated in internal and external validation cohorts. Results: Among 507 patients, the mean eGFR_6M was 11.7 ± 4.9 mL/min/1.73 m2, with higher values observed in patients with diabetes mellitus (DM), cardiovascular disease (CVD), stroke, dementia, liver cirrhosis (LC), nephrotic-range proteinuria, or hypoalbuminemia. The mean eGFR_6M decline rate was 8.3 ± 9.6 mL/min/1.73 m2/year, with more rapid declines observed in patients with DM, LC, nephrotic range proteinuria, and hypoalbuminemia. The model was developed using significant predictors—sex, impaired mobility, DM, CVD, left ventricular ejection fraction, blood urea nitrogen, and phosphate levels—and showed acceptable performance in both validation cohorts, with P30 ranging from 70% to 75%. Conclusions: This study provides nephrologists with an objective reference to guide the timing of dialysis preparation, supporting personalized ESKD life planning and improving patient outcomes.

A logistic threshold model for asphalt-aggregate adhesion based on mineral composition

Enhancing female longevity through regular calvings improves herd replacement rates and reduces the costs associated with replacing low reproductive efficiency animals. Stayability (STAY), defined as a cow's ability to remain productive in the herd, is a categorical trait that challenges traditional genetic evaluation due to its non-normal distribution. This study aimed to estimate genomic predictions for different STAY definitions-based on the number of calvings at specific ages in Nellore females-and to compare the predictive ability of linear and threshold models using the linear regression (LR) method. Phenotypic and genotypic data from 187 herds provided by the Nellore Brazil breeding program (ANCP, Ribeirão Preto, Brazil) were used. Four STAY definitions (STAY48-2, STAY48-3, STAY54-2, STAY54-3, STAY72-3) were evaluated. Genomic estimated breeding values (GEBV) were obtained using univariate linear and threshold models implemented in the BLUPF90 software family. Variance components were transformed from liability to observed scale. Heritability estimates ranged from 0.16 to 0.22 on the liability scale and 0.07 to 0.09 on the observed scale. Threshold models showed superior predictive ability compared to linear models, with higher accuracies (0.531 to 0.698 vs. 0.451 to 0.532), lower bias (-0.0004 to 0.008 vs. 0.027 to 0.096) and dispersion values closer to the ideal (0.932 to 1.000 vs. 0.811 to 0.848). Among the definitions, STAY48 with at least two or three calvings demonstrated the most consistent performance, representing a promising criterion for genetic evaluation in Nellore cattle.

Growth as a boundary condition: a threshold model of corporate diversification strategy

Understanding interactions between ecological resilience and biodiversity is critical for sustainable ecosystems and coordinated regional development. This study examines prefecture-level cities in Guangdong Province—characterized by diverse ecological conditions and rapid urbanization—to explore how ecological systems respond to biodiversity dynamics. We construct an ecological resilience framework based on resistance–adaptability–recoverability, quantify biodiversity using species occurrence data from the Global Biodiversity Information Facility, and apply a panel threshold model to detect nonlinear couplings. To identify key drivers of resilience, we employ XGBoost and SHAP analyses for interpretable machine learning insights. Results show clear threshold behavior: ecological resilience is weak or negative at low biodiversity and improves once biodiversity exceeds critical levels; in 2015 and 2020, thresholds were approximately 99.73 and 232.01 with a significant high-biodiversity effect. Machine learning results align with the threshold findings and indicate forest coverage ratio is the dominant driver of ecological resilience across years. The integrated findings highlight pronounced spatial heterogeneity in ecological resilience and identify critical biodiversity thresholds influencing ecosystem stability, providing targeted evidence for biodiversity conservation and resilience-oriented management. This study advances understanding of nonlinear ecological–biodiversity interactions and offers practical guidance for strengthening ecological security in rapidly developing regions.