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By utilizing a socioeconomic framework, this paper contributes to the water stress literature. Employing the instrumental variable quantile regression model on 81 countries from 2000 to 2019, our findings reveal financial development can alleviate high levels of water stress. Good governance consistently reduces water stress but human development exacerbates water stress, indicating a conflicting relationship between improving human development and reducing water stress. The moderating effect of good governance reveals that when combined with human development, it can effectively reduce water stress. [ABSTRACT FROM AUTHOR]

This article discusses the impact of different ways of measuring the duration of buprenorphine treatment on our understanding of continuity of care for opioid use disorder (OUD). The study analyzes buprenorphine prescription records and demonstrates how varying the operationalization of treatment duration can change conclusions about treatment continuity. The authors highlight the lack of consensus on how to measure treatment duration and emphasize the importance of choosing an empirically defined allowable gap. The article also discusses the limitations of the study and calls for future research to establish validated cut-off points for treatment outcomes. [Extracted from the article]

This article introduces customized screening ensemble with shape‐adaptive quantile regression (CseAQR), a novel probabilistic interval forecasting method built upon the quantile regression model. CseAQR utilizes ensemble learning to perform adaptive quantile regression prediction, which can handle the heteroscedasticity feature in time series data by using a weighted adaptive allocation loss function to enhance the adaptability of the basic quantile regression model on the dataset. The model performance predictor is used to select the optimal ensemble learner combination, assign reasonable adaptive weights to it, and obtain a preliminary prediction interval through weighted aggregation. Combining ensemble learners not only improves the accuracy and robustness of prediction intervals but also ensures the commutativity required for conformal prediction. Finally, the conformal prediction method is applied to locally adjust the prediction interval, constructing a more consistently aligned prediction interval with the actual data on a narrower basis. [ABSTRACT FROM AUTHOR]

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models. [ABSTRACT FROM AUTHOR]

In this article, we consider smooth estimators for the quantile density function (qdf) for a sequence { X n , n ≥ 1 } of stationary non negative associated random variables with a common marginal distribution function. The qdf is given by q (u) = Q ′ (u) , u ∈ (0 , 1) , Q (u) representing the corresponding quantile function. The smooth estimators of q (u) considered here are adapted from those of Q (u) considered in Chaubey, Dewan, and Li (2021). A few asymptotic properties of these estimators are established parallel to those in the i.i.d. case. A numerical study comparing the mean squared errors of various estimators indicates the advantages and a few limitations of various estimators. The smoothing parameter is selected based on the BCV and RLCV (a variation of likelihood cross-validation) criteria. It is concluded, based on the numerical studies, that the RLCV criterion may produce over-smoothing, hence BCV criterion may be preferable. The numerical studies also suggest that, overall, the estimator proposed by Soni, Dewan, and Jain (2012) seems to have some advantage over the other estimators considered in this article. [ABSTRACT FROM AUTHOR]

This article is concerned with the asymptotic theory of estimates of unknown parameters in autoregressive quantile processes. We assume random errors form a strictly stationary ϕ -mixing sequences. In view of the approach of argmins and blocking argument, we prove the parameter estimators satisfy the functional moderate deviation principle (MDP). Further, we give the law of the iterated logarithm under some standard conditions. Based on the contraction principle, the moderate deviation principles of L-estimators on the autoregression quantile (ARQ) and autoregression rank scores (ARRS's) are also discussed. This method can be extended to a fair range of different statistical estimation problems. [ABSTRACT FROM AUTHOR]

This research analyses the effects of natural resource rents on human development in selected African countries. It focuses on a panel of 41 countries and covers the period 1996–2019. We used the ordinary least squares, quantile regression, and the two‐step system generalized method of moments (GMM) estimates. The results show that total natural resource rents have a negative effect on human development. However, this negative effect is mitigated by food imports, which ensure food security for the countries concerned. Furthermore, taken individually, the effect of natural resource rents on human development is differentiated according to their type: forestry and natural gas rents have a positive effect on human development while, oil and coal rents have a negative effect. After controlling for per capita income and other macroeconomic, institutional, and environmental factors, these results are robust. The results suggest better management of natural resource rents, economic diversification and industrial development, environmental protection, food security, and the investment of the natural resources rents in human capital development to ensure long‐term human development in the region. [ABSTRACT FROM AUTHOR]

Natural resources have become the centerpiece of global discussion recently in an effort to achieving sustainable development. There are arguments that as much as natural resources are inevitable for development; they are disastrous to factors that promote sustainability of the global environment of which the transition to sustainable energy stands out. That notwithstanding, studies have been largely engrossed in verifying the roles natural resources on economic growth and the environment. Consequently, this research provides the first empirical evidence on the impacts of natural resources on sustainable environment in a global dataset for 73 countries from 1997 to 2019. To extend the frontier of knowledge, the roles of technological advancement, structural change, regulatory quality, and foreign direct investment are considered in the empirical model. The empirical evidence relies on cross‐sectional autoregressive distributed lag, augmented mean group; common correlated effects mean group and method of moment quantile regression. The preliminary analyses show that the world is presently in a renewable energy deficit while recording a gradual shift from relying on natural resources. Feedbacks from the model evaluated expose that natural resource dependence curses sustainable energy through its militating impacts. Besides, technological advancement, structural change, regulatory quality, and foreign direct investment significantly and positively drive sustainable energy. Besides, the quantile regression analysis provides diverging magnitudes. Policies that promote sustainable energy are suggested. [ABSTRACT FROM AUTHOR]

AbstractBoth insufficient and excessive iodine intake can lead to thyroid-related disorders. Although China has made progress in eliminating iodine deficiency over the past few decades, the incidence of thyroid cancer is increasing. Currently, there is a lack of relevant research on the tradeoff between the benefits and risks of salt iodization in China. In this study, we developed a method that combines the total probability algorithm and disease burden to evaluate the appropriate amount of salt iodization. Following the principle of minimizing the comprehensive disease burden and using the metabolic model of human iodine nutrition. Based on the average national iodine level in water, the optimal iodine content in Chinese salt is determined to be 17 mg/kg. However, iodine content in water is not evenly distributed in China. Approximately 3.23% of administrative villages have water iodine concentrations exceeding 80 ug/L, eliminating the need for iodine fortification in salt. Approximately 83.51% of administrative villages need to continue implementing the salt iodization policy, with the optimal iodine content in salt ranging from 15 to 18 mg/kg. In 13.16% of administrative villages, the iodine content in salt is determined based on the local water iodine concentration, ranging from 0 to 15 mg/kg. Our study cracks open a window of insight suggesting that the optimal iodine content for salt is lower than the existing benchmark dictated by the prevailing policy in China. Hence, there is an urgent need to refine and advance the iodine supplementation strategy in salt to pave the way for precision medicine and health-centric iodine supplementation strategies. [ABSTRACT FROM AUTHOR]

In regression analysis a single parametric form is assumed, over the whole domain of interest. However, this assumption might not be valid in some applications, such as existence of a change point in the functional form. In this case we need to detect and estimate such change point. Also, it is common to assume normality of the response variable when dealing with the change point problem. The normality assumption can be violated in many cases, such as heavy-tailed data or in the presence of outliers. In such cases, the quantile regression is a good candidate. It is known that the quantile regression is distribution free and robust to outliers. The CUSUM test has been used to detect the existence of a threshold effect (change point) to the quantile regression model in cross-sectional data. This article proposes and develops the CUSUM test, in longitudinal data setting, to investigate the existence of a change point in quantile regression model. Simulation study is used to assess the performance of the proposed test. Finally, the proposed test is used to detect any possible change points in a COVID-19 data. [ABSTRACT FROM AUTHOR]

Bayesian regularized composite quantile regression approaches with bridge and reciprocal bridge penalties are adopted to conduct variable selection in composite quantile regression. Two MCMC algorithms were developed for posterior inference using the normal-exponential mixture representation of the asymmetric Laplace distribution. Gamma prior is placed on the regularization parameter. The parameters of the Gamma prior are treated as unknowns and estimate them along with the other parameters. The performance of the proposed composite quantile regression methods over the existing methods is then illustrated via simulated studies and a real data set. [ABSTRACT FROM AUTHOR]

There is no consensus regarding optimal curricula to teach cognitive elements of general surgery. The American Board of Surgery In-Training Exam (ABSITE) aims to measure trainees' progress in attaining this knowledge. Resources like question banks (QBs), Surgical Council on Resident Education (SCORE) curriculum, and didactic conferences have mixed findings related to ABSITE performance and are often evaluated in isolation. This study characterized relationships between multiple learning methods and ABSITE performance to elucidate the relative educational value of learning strategies. Use and score of QB, SCORE use, didactic conference attendance, and ABSITE percentile score were collected at an academic general surgery residency program from 2017 to 2022. QB data were available in the years 2017-2018 and 2021-2022 during institutional subscription to the same platform. Given differences in risk of qualifying exam failure, groups of ≤30th and >30th percentile were analyzed. Linear quantile mixed regressions and generalized linear mixed models determined factors associated with ABSITE performance. Linear quantile mixed regressions revealed a relationship between ABSITE performance and QB questions completed (1.5 percentile per 100 questions, P < 0.001) and QB score (1.2 percentile per 1% score, P < 0.001), but not with SCORE use and didactic attendance. Performers >30th percentile had a significantly higher QB score. Use and score of QB had a significant relationship with ABSITE performance, while SCORE use and didactic attendance did not. Performers >30th percentile completed a median 1094 QB questions annually with a score of 65%. Results emphasize success of QB use as an active learning strategy, while passive learning methods warrant further evaluation. • Question bank (QB) use and score predicted American Board of Surgery In-Training Exam (ABSITE) performance. • The use of American Board of Surgery Surgical Council on Resident Education or didactic conference attendance did not predict the ABSITE score. • High ABSITE performers completed more QB questions than middle or low performers. • High and middle ABSITE performers had a higher QB score than low performers. • Our findings emphasize the success of QB use as an active learning strategy. [ABSTRACT FROM AUTHOR]

Understanding the factors that influence the intention to use vaccines is crucial for implementing effective public health policies. This study examined the impact of various cognitive, affective, normative, and sociodemographic variables on the intention to be vaccinated against COVID-19 with the first-generation AstraZeneca vaccine. A survey of 600 residents of Spain was used to assess the influence and hierarchy of the drivers of the intention to vaccinate via least-squares and quantile regressions. The most significant factors were the perceptions of efficacy and social influence, both of which had positive impacts (p < 0.0001). The positive influence of fear of COVID-19 and the negative influence of fear of the vaccine were also significant in shaping the central tendency toward vaccination. However, these fear-related variables, particularly the fear of COVID-19, lost importance in quantile adjustments outside the central tendency. Among the sociodemographic variables, only the negative impact of income was statistically significant. These results are valuable for the development of vaccination policies because they measure the sensitivity of attitudes toward vaccination to exogenous variables not only in the central values, as is common in similar studies, but also across the entire range of responses regarding the intention to vaccinate. This additional analysis, which is not commonly performed in studies on vaccine acceptance, allows us to distinguish between variables which are consistently related to the intention to vaccinate and those that influence only expected responses. [ABSTRACT FROM AUTHOR]

This paper compares the hedging and safe haven properties of clean (renewable) energy and unclean (non-renewable) energy stocks. Using around 20 years of monthly clean and unclean energy indices and applying the GARCH and quantile regression models with dummy variables, we find that likewise the unclean energy stocks renewable energy stocks provide a weak hedge and a safe haven against the global economic policy uncertainty and its extreme values even during the pandemic and low and high return environments. These findings suggest that clean energy stocks provide immunity to investors against unfavourable changes to economic policy uncertainty and market volatility. [ABSTRACT FROM AUTHOR]

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods. Plain Language Summary: This paper proposes a functional quantile autoregressive model of order one, which is used to predict the entire distribution of the realizations of river flow time series curve. The proposed model allows modeling the conditional quantiles of the response variable as a function of its past values of it. The proposed method for historical river flow curves is an excellent alternative to existing mean regression methods at the 0.5 quantile level (median regression). Also, as an advantage over existing methods, it offers a more thorough explanation of the connection among previous and future realizations of river flow curves at various quantile levels, providing a more extensive understanding of the relationship. Moreover, this feature of the proposed method allows for the effortless generation of pointwise prediction intervals for future realizations of river flow curves. The numerical results obtained by Monte Carlo experiments and empirical data analyses exhibit that, compared with existing methods, the proposed method produces competitive or even better forecasting results. The results also indicate that the future realizations of the river flow measurements are well covered by the prediction intervals constructed by the proposed method. Key Points: Predicting the mean and extreme values of the river flow curve is important for various applications in water resources managementThe FQAR(1) allows predicting the entire distribution of future realizations of the river flow curve as a function of its past values of itNumerical results based on river flow measurements collected from the Australia Continent confirmed the potential of the FQAR(1) [ABSTRACT FROM AUTHOR]

Quantile regression is increasingly encountered in modern big data applications due to its robustness and flexibility. We consider the scenario of learning the conditional quantiles of a specific target population when the available data may go beyond the target and be supplemented from other sources that possibly share similarities with the target. A crucial question is how to properly distinguish and use useful information from other sources to improve the quantile estimation and inference at the target. We develop transfer learning methods for high-dimensional quantile regression by detecting informative sources whose models are similar to the target and using them to improve the target model. We show that under reasonable conditions, the detection of the informative sources based on sample splitting is consistent. Compared to the naive estimator with only the target data, the transfer learning estimator achieves a much lower error rate as a function of the sample sizes, the signal-to-noise ratios, and the similarity measures among the target and the source models. Extensive simulation studies demonstrate the superiority of our proposed approach. We apply our methods to tackle the problem of detecting hard-landing risk for flight safety and show the benefits and insights gained from transfer learning of three different types of airplanes: Boeing 737, Airbus A320, and Airbus A380. [ABSTRACT FROM AUTHOR]

Healthcare financing is crucial for sustainable development and has been gaining attention, particularly since the COVID-19 pandemic. Although ample studies have explored the determinants of healthcare spending, the current study examines the determinants of health financing transition in 124 low- and middle-income countries (LMICs) for 19 years, from 2000 to 2018. The study first estimates the elasticity of health expenditure (i.e., government, private, and external) to per capita income, fiscal spending, age dependency, control of corruption, and time; second, it investigates crowding-out/in effects between domestic and external health financing. As the sample countries are heterogeneous in terms of development and universal health coverage (UHC) reform, our study employs a panel regression model with cluster-robust fixed effects and a bootstrapping quantile regression with fixed effects model to capture unobserved heterogeneity and produce robust estimates across quantile distributions. The results show that an increase in per capita government health expenditure (GHE) and per capita external health expenditure from foreign donors can reduce the share of out-of-pocket health expenditure to total health expenditure (OOPHE), thereby improving the health status and well-being of the people. The study also finds crowding-out effects of per capita external health financing on GHE in LIMCs. Interestingly, the results show that the control of corruption increases the per capita GHE in low-income countries. Thus, in low-income countries, improving governance would improve efficiency in fund utilization and strengthen the health system. Further, our study concludes that those countries that have passed UHC legislation are moving faster toward health financing transition by increasing public spending on health care. Healthcare policy in developing countries should prioritize (i) the adoption and implementation of UHC reform, (ii) alternative revenue mobilization strategy for financing public health care. [ABSTRACT FROM AUTHOR]

Introduction: The triglyceride–glucose index (TyG) has been identified as a dependable and simple indicator marker of insulin resistance (IR). Research has demonstrated a correlation between macrovascular complications and TyG. However, limited research exists regarding the relationship between TyG and diabetic microvascular complications. Consequently, the objective of this study is to investigate the association between TyG and diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN). Methods: This is a cross-sectional, observational study. A total of 2048 patients from Tongren Hospital, Shanghai Jiao Tong University School of Medicine were enrolled. The primary outcomes are DKD and DPN. Quantile regression analysis was employed to investigate the implicit factors of TyG quartiles. Subsequently, based on implicit factors, logistic regression models were constructed to further examine the relationship between TyG and DKD and DPN. Results: In the baseline, TyG exhibited higher values across patients with DKD, DPN, and co-existence of DKD and DPN (DKD + DPN) in type 2 diabetes (T2D). Univariate logistic regressions demonstrated a significant association between an elevated TyG and an increased risk of DKD (OR = 1.842, [95% CI] 1.317–2.578, P for trend < 0.01), DPN (OR = 1.516, [95% CI] 1.114–2.288, P for trend < 0.05), DKD + DPN (OR = 2.088, [95% CI] 1.429–3.052, P for trend < 0.05). Multivariable logistic regression models suggested a statistically significant increase in the risk of DKD (OR = 1.581, [95% CI] 1.031–2.424, p < 0.05), DKD + DPN (OR = 1.779, [95% CI] 1.091–2.903, p < 0.05) after adjusting the implicit factors of TyG quartiles. However, no significant relationship was observed between TyG and DPN in the multivariable regression analysis. Conclusions: Elevated TyG was significantly associated with an increased risk of DKD in T2D, but no significant relationship was shown with DPN. This finding provided further evidence for the clinical significance of integrating TyG into the initial assessment of diabetic microvascular complications. [ABSTRACT FROM AUTHOR]

This article proposes a new way to increase the flexibility of a family of statistical distributions by adding two additional parameters. The newly proposed family is called the alpha–beta power transformation family of distributions. A specific model, the alpha–beta power exponential distribution, was thoroughly investigated. The hazard rates of the proposed distribution can be decreasing, bathtub-shaped, and unimodal. The derived structural features of the proposed model include explicit formulations for the quantiles, the moments, the moment-generating function, and the incomplete and conditional moments. In addition, estimates of maximum likelihood, least squares, weighted least squares, and minimum distance of Cramér von Mises, von Anderson–Darling, and right-tail Anderson–Darling are obtained for the unknown parameters. Two real data sets were examined to demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Objective: Saugeye (Sauger Sander canadensis × Walleye S. vitreus) have been introduced in reservoirs for several purposes, including as a top‐down control to combat stunting in crappie Pomoxis spp. populations. However, no comprehensive diet evaluation has been completed in southern reservoirs. Our objectives were to assess variability in saugeye diet compositions, explore factors influencing crappie presence in saugeye diets, and investigate trends in prey size to inform management strategies regarding predatory control of crappie populations. Methods: We collected 2638 saugeye diets from six Oklahoma reservoirs. We used permutational multivariate analysis of variance to test differences in diet compositions among saugeye lengths, study reservoirs, and seasons, and we used logistic regression models to correlate the presence of crappie in diets with saugeye size, season, crappie abundance, productivity, and turbidity. Finally, we used bivariate plots of diet indices and quantile regression to explore trends in prey importance and prey size relative to saugeye size. Result: Saugeye diet compositions were best explained by saugeye size, followed by study reservoir and season. Saugeye exhibited an ontogenetic shift from Inland Silverside Menidia beryllina to shad Dorosoma spp., and crappie were eaten infrequently in comparison. The presence of crappie prey in diets was most correlated with saugeye size (primarily large saugeye), followed by higher crappie abundance, lower reservoir productivity, and season (highest in fall). Saugeye consumed larger prey than most piscivores relative to predator size. Conclusion: Our results indicated that dominant prey of saugeye transitioned from smaller‐bodied fishes to larger shad as they grew when both prey types were available, which may have implications for growth and recruitment. Ultimately, the low and variable use of crappie prey may lead to inconsistent predatory effects on crappie populations, and large saugeye may exert greater top‐down pressure on crappie in lakes with lower productivity or higher crappie abundance. Impact statementDominant prey type of saugeye in Oklahoma reservoirs transitioned from Inland Silverside to Gizzard Shad as they grew, and crappie were eaten infrequently in comparison. Large saugeye may exert greater top‐down pressure on crappie populations in lakes with lower productivity or higher crappie abundance. [ABSTRACT FROM AUTHOR]

Effective management of natural resources is crucial for diminishing carbon emissions. This research explores how economic, financial, and political risks influence the relationship between natural resources rents and carbon emissions. Analyzing data from 66 countries, this study utilizes methods such as quantile regression and dynamic threshold regression to thoroughly assess the data. The findings reveal: (i) Natural resources rents tend to increase carbon emissions consistently across different quantiles (0.1 to 0.9). The fact is confirmed by robustness checks, illustrating that increased natural resources rents lead to higher emissions. (ii) Economic, financial, and political risks affect how natural resources rents impact carbon emissions. Notably, reduced economic and financial risks lessen the propensity of natural resources rents to boost emissions at higher quantiles, while a decline in political risk decreases the exacerbating effect of natural resources rents on emissions from the 0.1 to 0.9 quantiles. (iii) This analysis uncovers threshold effects where economic, financial, and political risks act as threshold factors. Specifically, when economic and political risks are low, a rise in natural resources rents actually leads to a decline in carbon emissions. The findings underscore the importance of considering these risks in the formulation of policies aimed at reducing carbon emissions from natural resource exploitation. [ABSTRACT FROM AUTHOR]

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As a common semiparametric mode, the partially linear additive model has flexible structures, and it has been widely used in practice. In this paper, we study the quantile regression estimation of the model when its responses are censored. In particular, we consider a more general censoring framework, which allows different types of censoring (left, right, double, or interval censoring) simultaneously. Based on the general principles of data augmentation, we propose a unified iterative algorithm, where the censored data is imputed by sampling data from the quantile process, and regression parameters are refitted by using bootstrap samples. Monte Carlo simulations are conducted to verify the finite-sample properties of the method, and the results show its good performance. The application to Boston housing price data further illustrates the advantages of this method in practice. [ABSTRACT FROM AUTHOR]

In the dynamic smart grid landscape, accurate probabilistic forecasting of electric load is critical. This paper presents a novel 24-hour-ahead probabilistic load forecasting model by integrating quantile regression with a parallel convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) architecture. Carefully tuning hyperparameters can enhance model performance and generalization capability. Consequently, we propose an improved whale optimization algorithm for automatic hyperparameter tuning of the forecasting model. Case studies demonstrate the proposed method's superior performance over benchmark models in terms of average interval score and pinball loss. In addition, it exhibits valid coverage and tight interval bandwidths. The model provides precise short-term load forecasts to support robust smart grid planning and operations. [ABSTRACT FROM AUTHOR]

Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis. [ABSTRACT FROM AUTHOR]

In contrast to fairly good knowledge of seasonal and interannual variability in North Atlantic salinity, its long-term historical changes remain poorly characterized, making it difficult to assess the current state and possible future changes. To fill this gap, we present the results of applying a non-parametric method of regression analysis (quantile regression) to assess long-term changes in North Atlantic salinity (0°–70° N, 8°–80° W) based on multiple datasets. The features of quantile trends in monthly salinity for a median value in two periods (1948–2018 and 1961–2011) are considered. In 1948–2018, salinization was generally detected in North Atlantic tropical and subtropical latitudes, while desalination was found in subpolar latitudes. For the 71-year period under consideration, the median monthly salinity in subtropical latitudes increased by 0.07 ± 0.02 PSU. Over the period 1961–2011, pronounced long-term changes in the North Atlantic salinity are difficult to identify based on the datasets used. A consistency analysis of significant salinity trends across the most used datasets allowed us to detect five small areas with pronounced positive trends in the upper ocean salinity. These include the Guiana Current, the vicinity of 12° N, 48° W, the Canary upwelling area, the region of the Gulf Stream transition to the North Atlantic Current and the western part of the North Atlantic Subpolar Gyre. In these areas, over a 51-year period, salinity in the 10–400 m layer increased by an average of 0.10 ± 0.04 PSU. [ABSTRACT FROM AUTHOR]

This paper explores the role of the COVID-19 pandemic on the herding behaviour across market participants in the Croatian market. The analysis uses daily prices of the Croatian stock index, spanning the period January 2016 to December 2022. The hypothesis of the herding behaviour is tested through the quantile regression approach. The findings document no evidence of herding prior to the pandemic crisis. In contrast, herding is discovered during the COVID-19 period. The paper provides policymakers and investors with valuable information to draw significant measures in their investment portfolio management during crises and pandemics. [ABSTRACT FROM AUTHOR]

This article empirically investigates the effects of global shocks on EU‐28 countries' patterns of specialisation by focusing on the global financial crisis of 2008. In particular, it addresses the following two research questions: Did the financial crisis change the patterns of specialisation of EU member states? Was the impact of the crisis homogenous across countries and manufacturing products? Answering these questions is important because changes in specialisation generate inequalities, given the shift of the demand from one factor to another. Using an unconditional quantile regression approach, this study demonstrates that the financial crisis had a positive effect which was larger on products in which countries were not specialised before the crisis. Some heterogeneity also emerged at both sectoral and geographical levels. [ABSTRACT FROM AUTHOR]

The article is focused on studying how to predict the failure times of coherent systems from the early failure times of their components. Both the cases of independent and dependent components are considered by assuming that they are identically distributed (homogeneous components). The heterogeneous components' case can be addressed similarly but more complexly. The present study is for non‐repairable systems, but the information obtained could be used to decide if a maintenance action should be carried out at time t$$ t $$. Different cases are considered regarding the information available at time t$$ t $$. We use quantile regression techniques to predict the system failure times and to provide prediction intervals. The theoretical results are applied to specific system structures in some illustrative examples. [ABSTRACT FROM AUTHOR]

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning. [ABSTRACT FROM AUTHOR]

Climate change is destructive to both economic activity and life subsistence. The rising CO2 emission is continuously degrading the environment and altering the characteristics of multiple hazards that can adversely affect human life. Transition to energy sources and industrial processes can maximize the benefits of low transitioning carbon emission technologies while promoting equality and inclusivity. In this pursuit, the response of CO2 emission is provoked by renewable energy consumption, FDI, natural resource, interaction term of FDI and NR, medium–high-tech industry, and economic growth on APEC countries for the period 1996–2020. To draw clear and conclusive information about outcome variables at different quantiles, study applied panel quantiles regression with an optimization method, such as Markov chain Monte Carlo. Natural resource consumption, high-tech industry, and economic growth have a positive, heterogeneous, and significant effect on environmental degradation in all quantiles. It is worth noting that HT deteriorates the environment in APEC countries, generally technological progress increases energy efficiency and reduces emissions. But it confirms the existence of a cost effect, which increases the use of conventional low-cost products over high-tech products. FDI has a positive and significant effect in all quantiles except the first quantile and confirms the presence of PHH. The interaction term (FDI*NRR) and REC effects are negative and significant in all quantiles. The negative effects of interaction term revealed that domestic industry is the cause of over-consumption and emission of NR. The use of clean and green energy consumption is crucial for sustainable development. Policymakers need to design mechanism that promotes steward policies for domestic industry to discourage the use of NR, enhance local–international partnerships in low emission and energy-efficient technologies, and promote the use of renewable energy. These policies will help to achieve certain SDG's goals such as (SDG-7: clean energy sources), (SDG-8: sustainable development goal); and (SDG-9: climate action). [ABSTRACT FROM AUTHOR]

A sound understanding of the potency of tobacco control policies is key to tobacco prevention. This study exploits a Smoothed Instrumental Variables Quantile Regression estimator to gauge the effectiveness of these policies while addressing major methodological and data limitations plaguing the previous literature. Specifically, smoke-free indoor air laws and tobacco control expenditures are examined in a single framework, which has the promise of accounting for potential complementarities thereof. Further, endogeneity of price (a proxy for tax policy) and other tobacco control policies is addressed through a unique set of instruments while allowing for differential impacts across the conditional distribution of cigarette consumption. Finally, our use of the nationally representative individual-level price and consumption data is essential to precise estimation of price elasticities and policy effects. Results indicate that ignoring price and policy endogeneity leads to inconsistent estimates. Further, tobacco control expenditures appear to be effective only for relatively more addicted smokers. Meanwhile, state-level smoke-free indoor laws, whose primary goal is to reduce exposure to second-hand smoke, do not affect cigarette use among smokers. In contrast, tax policy appears to be most potent for less addicted individuals. Therefore, optimal policy responses should combine tobacco control expenditures with sin taxes. [ABSTRACT FROM AUTHOR]

Summary: Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two‐step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the United States demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions. [ABSTRACT FROM AUTHOR]

Governments internationally strive to balance environmental health and economic development. Modern economies, specifically emerging ones, emphasize the importance of eco-friendly progress, where the pace of economic growth limits the ecological footprint. The ecological footprint denotes both the trajectory of natural resource extraction in the economic process and how quickly these resources can be replenished, as well as the capacity of the ecological sector to absorb waste from this process. This study examines 38 countries from 1994 to 2020 to investigate the drivers of the ecological footprint and found that environmentally related technologies harmfully influence ecological deprivation but are positively affected by gross domestic product growth. Renewable energy diminishes pollution levels, while urbanization has an insignificant effect. Imports were only found to be significant with one econometric technique, and their impact on the ecological footprint was positive. Income level affects the influence of gross domestic product on the ecological footprint. Lower-income quantiles have a more significant impact than higher quantiles. The Granger causality test shows bidirectional causality between the ecological footprint and exogenous factors: eco-technologies, gross domestic product/capita, renewable energy, urbanization, and imports. [ABSTRACT FROM AUTHOR]

Land intensive use reflects the spatial structure, agglomeration characteristics, and internal mechanisms of urban economic, social, and ecological system development, significantly impacting urban resilience. Based on panel data from 287 cities in China from 2010 to 2020, this paper measures the levels of land intensive use and urban resilience, and empirically examines the impact mechanism of land intensive use on urban resilience through baseline regression and panel quantile regression. The results reveal that: (1) During the study period, China's urban land intensive use level has significantly improved. The land intensive use level shows a trend of "the strong become stronger, and the weak are always weak" and "high in the east and low in the west" spatial differentiation, while the urban resilience level showed a trend of accelerated "catching up" of low-resilience cities towards high-resilience cities and "high in the east and low in the west" spatial differentiation as well. (2) Land intensive use significantly promotes effect on urban resilience, and the effect depends on different conditions. (3) Among all dimensions of land intensive use, both land input intensity and land use benefits significantly promote urban resilience, while land use intensity shows an insignificant effect. (4) The impact of land intensive use on urban resilience demonstrates significant scale heterogeneity and geographic regional heterogeneity. Based on these findings, the paper proposes relevant policy suggestions for land intensive use aimed at improving urban resilience, offering guidance for promoting high-quality land use and sustainable urban resilience development. [ABSTRACT FROM AUTHOR]

Aims: To analyse the bibliometric data of nursing journals covering general fields, specializations, skills and practices and investigate the influence of selection factors on the one‐year improved SCImago journal rank (SJR). Design: A bibliometric analysis. Methods: The study sample included academic journals in the field of nursing indexed in the Scopus and SJR database using data from 2020 to 2022. Quantile regression were performed that investigated the influence factors over the one‐year improved SJR when variables were found to be non‐normally distributed, characterized by extremely high kurtosis. Results: Results from 539 nursing journals retrieved from the Scopus and SCImago databases revealed that citation index remains the main factor that positively affects differences in SJR, while citable articles only affect the early stages of quantile regression. In addition, an excessively high number of research articles may negatively affect SJR, and the influence of self‐citations can initially be positive but become negative the following year. Citations continue to be the dominant factor, while the rapid growth in the number of articles and self‐citations must be addressed with caution. No Patient or Public Contribution: No patient or public contribution. [ABSTRACT FROM AUTHOR]

This research conducts a comprehensive analysis of the relationship between green finance and the Sustainable Development Goals (SDGs), particularly focusing on SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Utilizing a novel SDG index based on United Nations criteria, the study evaluates the effect of green finance on sustainability across OECD economies between 2000 and 2020. Employing advanced econometric methods, including Driscoll–Kraay standard errors, FMOLS, DOLS, and the method of moments quantile regression, the findings reveal that green finance, along with environmental governance, fiscal policy, and population growth, significantly contributes to the advancement of SDGs. However, the study also highlights the detrimental impact of elevated geopolitical risks, which can hinder progress toward these goals and partially negate the benefits of green finance initiatives. These insights are vital for policymakers striving to develop effective strategies to promote the SDGs amidst current global uncertainties. [ABSTRACT FROM AUTHOR]

The article focuses on assessing the impact of water and sanitation improvements on economic development in selected African countries. Topics include the nuanced relationship between water and sanitation variables and economic outcomes, the role of these interventions in promoting economic growth, and policy implications for sustainable development.

This paper applies three robust approaches, namely, the MM estimation, the Theil–Sen estimation, and the quantile regression, to generate earnings forecasts in Chinese financial market and evaluates the forecast accuracy of these three methods based on three forecasting criteria. We examine six forecasting models where the predicted variables include earnings per share, net income, and three profitability measures. We show that the three robust methods significantly outperform the OLS method. Moreover, the MM estimation and the quantile regression have better forecast accuracy than the Theil–Sen approach. [ABSTRACT FROM AUTHOR]

The paper contains the comparative analysis of the efficiency of different qunatile estimators for various distributions. Additionally, we show strong consistency of different quantile estimators and we study the Bahadur representation for each of the quantile estimators, when the sample is taken from NA, φ , ρ ∗ , ρ -mixing population. [ABSTRACT FROM AUTHOR]

This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model is flexible, but leaner and simpler models are preferred for applications. Therefore, it is important to evaluate whether the estimated coefficient function varies significantly with covariates. If the effect of the non-linearity of the model is weak, the varying coefficient structure is reduced to a simpler model, such as a constant or zero. Accordingly, the hypothesis test for model assessment in the varying coefficient model has been discussed in mean and quantile regression. However, there are no results in tail index regression. In this study, we investigate the asymptotic properties of an estimator and provide a hypothesis testing method for varying coefficient models for tail index regression. [ABSTRACT FROM AUTHOR]

In previous decades, seminal theories were advanced addressing the problems of asymmetric information. These scholarly works confirmed a significant positive correlation between insurance coverage and risk in insurance markets. We showed that when regression models were used to analyze the asymmetric information problem, the sample selection bias and chosen regression methods could produce distinct results. Moreover, samples at different quantiles may lead to considerably different levels of information asymmetry. The present research suggests that quantile regression models be employed to better reveal the situation of the insurance market and reduce estimation errors. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is essential for the efficient operation of hydropower systems. This paper presented a runoff probability prediction model that utilizes an enhanced long short-term memory (LSTM) network. The model incorporates a combination of a long and short-term memory network, a quantile regression module and an interval correction module. The proposed model utilizes the LSTM network to effectively capture the time-series characteristics of the runoff data. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Furthermore, the inclusion of an interval correction module helps refine the prediction results, leading to improved accuracy and a narrower prediction interval. The integration of these three modules greatly enhances the precision of the predictions and brings the probability estimates closer to the true distribution. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Jinsha River and Lancang River were selected to evaluate the performance of the model because of the availability of long-term reliable data, geographical representation, and socioeconomic importance. The prediction results demonstrate superior performance compared with other existing models. Moreover, the model enables obtaining probabilistic predictions with appropriate prediction intervals and high reliability. [ABSTRACT FROM AUTHOR]