Search

This article examines the wage earnings of refugee immigrants in Sweden. Using administrative employer–employee data from 1990 onward, approximately 100,000 refugee immigrants who arrived between 1980 and 1996 and were granted asylum are compared to a matched sample of native-born workers. Employing recentered influence function (RIF) quantile regressions to wage earnings for the period 2011–2015, the occupational-task-based Oaxaca–Blinder decomposition approach shows that refugees perform better than natives at the median wage, controlling for individual and firm characteristics. This overperformance is attributable to female refugee immigrants. Given their characteristics, refugee immigrant females perform better than native females across all occupational tasks studied, including non-routine cognitive tasks. A notable similarity of the wage premium exists among various refugee groups, suggesting that cultural differences and the length of time spent in the host country do not have a major impact. [ABSTRACT FROM AUTHOR]

This paper revisits the ongoing discussion on the concept of industrial relatedness by applying it to manufacturing industries in urban areas. The analysis uses a sample of firms operating in the Brussels Capital Region area and observed over the period 2009–2015. Based on a two-step quantile regression, results show that industrial relatedness is the agglomeration force that mostly sustains the performance of urban manufacturing, whereas this is not the case for other types of agglomeration externalities. Moreover, among the measures of industrial relatedness, the input–output relationship matters more than product similarity in the perspective of a relocation of manufacturing firms in urban areas. [ABSTRACT FROM AUTHOR]

Background: Monitoring survey methods, as well as movement recommendations, evolves over time. These changes can make trend observations over time difficult. The aim of this study was to examine the differences between 2 computer-assisted survey administration methods and the effect of the omission of the 10-minute minimum bout requirement in physical activity (PA) questions on PA outcomes. Methods: We used data from the second Austrian PA Surveillance System for 2998 adults (18–64 y), applying computer-assisted personal interviewing and computer-assisted web interviewing. Within the computer-assisted web interviewing sample only, we added PA questions without the 10-minute requirement. Quantile and logistic regressions were applied. Results: Between computer-assisted web interviewing and computer-assisted personal interviewing, within the computer-assisted personal interviewing sample, we found lower PA estimates in the leisure domain and work and household domain, but not in the travel domain, and no significant difference in the proportion of people meeting the PA recommendations. In all 3 PA domains, the median minutes did not differ when assessed with or without the 10-minute requirement. However, the percentage participation in the travel domain and work and household domain performing >0 minutes per week PA was higher when there was no 10-minute requirement. The proportion of people meeting the Austrian aerobic recommendation for adults when computed with or without the 10-minute requirement did not differ. Conclusion: Our findings suggest that the omission of the 10-minute requirement does not seem to result in marked differences in PA estimates or the proportion of adults meeting the recommendations. [ABSTRACT FROM AUTHOR]

The inventory levels of pickup points play an important role for the same-day or next-day pickup and delivery services. The previous inventory optimisation research usually makes an assumption about demand distribution, does not use the real dataset or consider shipping strategies for this problem. In this study, we introduce a new strategy, mixture of anticipatory and emergency shipping, and propose forecasting-optimisation integrated approach to optimise multi-items' inventories in each pickup point based on big data analysis. We explore a real dataset including 23,808,261 records with 54 pickup points and 4018 items. We first cluster the dataset based on the distances between pickup points and the warehouse, then, implement the forecasting-optimisation integrated algorithms to select the more profitable strategy for each group. The result indicates that compared with the original algorithms, our proposed approach can effectively increase the profits, particularly, the novel algorithm, Long Short-Term Memory networks – Quantile Regression, performs better. Additionally, we find that the 100% anticipatory shipping is not necessarily superior to emergency shipment, when the pickup point is farther from the warehouse, the advantage of emergency shipment is more significant. However, the mixture of anticipatory and emergency shipping can contribute to higher profits for online retailers. [ABSTRACT FROM AUTHOR]

This study evaluates the effect of returnee directors on corporate tax avoidance by using data on publicly listed Chinese companies from 2000 to 2012. Returnee directors grow up in China and then study or work abroad before returning home to be listed firms' board directors. We use the introduction of provincial policies toward attracting skilled individuals with foreign experience as an instrumental variable for Returnee directors, which is the fraction of returnee directors divided by the total number of directors within a firm. Using quantile regression, we find a positive relation between Returnee directors and corporate tax avoidance for low levels of tax avoidance but a negative relation for high levels of tax avoidance. The result is robust to a battery of tests. The relation between returnee directors and tax avoidance is stronger for state-owned enterprises (SOEs) than non-SOEs and stronger for returnees who hold MBA degrees, possess a background in accounting or auditing, or are independent directors than other returnees. [ABSTRACT FROM AUTHOR]

In this article, we analyse the distributional effects of macroeconomic systematic shocks on firm-level investment as well as exploring structural characteristics and dynamic evolution patterns of firm-level investment vulnerability. Two main facts are established: (1) systematic shocks have positive impacts on firm-level investments at most quantiles in the top four global manufacturing economies, implying that most firms' investments follow the macroeconomic situation; the effect magnitude of systematic shocks on firm-level investments is much greater for firms with lower capital expenditures in the US, while the effect magnitude is much greater for firms with higher investments in China, the EU, and Japan; (2) under the impact of systematic shocks, investment vulnerability of upside firms is greater than that of downside firms; for China and Japan, their firms' investment vulnerability exhibits a quite similar pattern. [ABSTRACT FROM AUTHOR]

Summary: Changes to flowering phenology are a key response of plants to climate change. However, we know little about how these changes alter temporal patterns of reproductive overlap (i.e. phenological reassembly).We combined long‐term field (1937–2012) and herbarium records (1850–2017) of 68 species in a flowering plant community in central North America and used a novel application of Bayesian quantile regression to estimate changes to flowering season length, altered richness and composition of co‐flowering assemblages, and whether phenological shifts exhibit seasonal trends.Across the past century, phenological shifts increased species' flowering durations by 11.5 d on average, which resulted in 94% of species experiencing greater flowering overlap at the community level. Increases to co‐flowering were particularly pronounced in autumn, driven by a greater tendency of late season species to shift the ending of flowering later and to increase flowering duration.Our results demonstrate that species‐level phenological shifts can result in considerable phenological reassembly and highlight changes to flowering duration as a prominent, yet underappreciated, effect of climate change. The emergence of an autumn co‐flowering mode emphasizes that these effects may be season‐dependent. [ABSTRACT FROM AUTHOR]

Recently, quantile-based structural equation model has enjoyed increasing popularity in various fields and applications. As we know, structural equation model consists of structural model and measurement model in total. In our quantile-based measurement model, the observed variables are considered to be manifestations of an underlying construct with changing relationships going from the latent variable to its observed variables at different quantiles. Based on our model, we propose a quantile-based partial least square algorithm with bag of little bootstraps. All of our model and algorithm are compared to our quantile-based partial least square algorithms with traditional bootstrap in simulations, and applied to part of IMD (International Institute for Management Development) World Competitiveness Yearbook datasets. [ABSTRACT FROM AUTHOR]

This paper examines the problem of unobserved heterogeneity in urban water demand. It uses a panel quantile regression (QR) approach to focus on segments of consumers with different levels of water consumption. This estimation strategy is applied to a rich set of panel microdata capturing the consumption of water for 4,023 households in Valencia (Spain) between the years 2009 and 2011. To capture heterogeneity in a city's residential household water consumption, a QR approach is applied to the specified water demand model, enabling analysis for different quantiles (levels) of consumption. The QR shows the behaviour of the parameters for different consumption levels. It enables differentiation of consumer reactions to different independent variables at each quantile of the distribution of the dependent variable. The results provide strong evidence of unobserved heterogeneity at different levels. This approach is useful in that it can lead to better-informed tariff design by providing an understanding of heterogeneity in price elasticities. [ABSTRACT FROM AUTHOR]

The tourism sector is one of the fastest-growing services sub-sectors worldwide and promises to unlock job opportunities and improve social inclusion outcomes in developing countries. We focused on the Southern African Development Community (SADC) region, which houses developing countries with high income inequality and constitutes attractive tourist centres in sub-Saharan Africa. We employed both the disaggregated and composite indicators of tourism development to investigate the tourism-income inequality nexus in the SADC region from 2010 to 2019. Utilizing the panel quantile regression approach, our overall results suggest that tourism development is inequality-worsening, and this is robust to both the composite tourism index and the individual tourism indicators (except in a few instances). While we established that net FDI inflows improve the inequality outcomes in the region, less corruption worsens inequality (except in a few cases). Accordingly, we offer relevant policy options for the governments of the SADC region. [ABSTRACT FROM AUTHOR]

This study employs rolling quantile regression and non-parametric estimation methods, specifically Quantile-on-Quantile (QQ) and Causality-in-Quantiles (QC), to investigate the impact of uncertainty indices (Geopolitical Uncertainty (GPR), Climate Policy Uncertainty (CPU), and Economic Policy Uncertainty (EPU)) on China's agricultural commodity futures. The rolling window quantile regression coefficients vary over time, with CPU having the most significant impact on agricultural commodity futures. The empirical results from the non-parametric estimation QQ method reveal when agricultural commodities are in a prosperous market, CPU and EPU exhibit relatively strong correlation, while GPR shows weaker or insignificant correlation. According to the non-parametric estimation QC method, the evidence suggests that the second-order (variance) causality relationship is relatively stronger than the first-order (mean) causality relationship, and the first-order mean causal relationship between CPU and corn and between EPU and wheat is not significant across the entire range of quantiles. The results on Hedging Effectiveness reveal that the efficiency of uncertainty hedging is substantiated. The hedging effectiveness of EPU exhibits improvement post-COVID-19, whereas the effectiveness of GPR and CPU is more pronounced in the pre-COVID-19 period. [ABSTRACT FROM AUTHOR]

AbstractWe, in this article, focus on functional partially linear quantile regression, where the observations are missing at random, which allows the response or covariates or response and covariates simultaneously missing. Estimation of the unknown function is done based on reproducing kernel method. Under suitable assumptions, we discuss consistency with rates of the estimators, and establish asymptotic normality of the estimator for the parameter. At the same time, we study hypothesis test of the parameter, and prove asymptotic distributions of restricted estimators of the parameter and test statistic under null hypothesis and local alternative hypothesis, respectively. Also, we study variable selection of the linear part of the model. By simulation and real data, finite sample performance of the proposed methods is analyzed. [ABSTRACT FROM AUTHOR]

Background: Formal home and community-based care are often considered as the preferable option to institutional care, offering older individuals the convenience of receiving care in their homes. Although research has found that these services may alleviate the burden on informal caregivers, there is a lack of research on which specific types of formal home and community-based care influence informal care provision. Methods: Employing fixed-effects and quantile regression models, this study seeks to explore the effects that various formal home and community-based care services have on reducing the burden of informal care. This study draws data from the Chinese Longitudinal Healthy Longevity Survey 2005, 2008, 2011, 2014, and 2018. Results: Our findings indicate that two types of formal care substantially influence the provision of informal care. The availability of daily living assistance services correlates with reduced informal caregiving hours, especially for those with extensive care needs. The availability of community-based health care services is linked to a reduction in the direct expenses incurred from informal caregiving, especially for those incurring greater direct caregiving costs. These effects are more prominent among urban residents. Other services, such as mental health support and legal advice services, do not demonstrate significant effects on reducing informal care hours and costs. Conclusions: Daily living assistance and community-based health care services play a crucial role in benefiting informal caregivers. It is important to prioritize the expansion of these services, especially among those with greater care needs. [ABSTRACT FROM AUTHOR]

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min–Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635–0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC. [ABSTRACT FROM AUTHOR]

This study attempts to investigate how Germany's energy transition has affected environmental quality by considering natural resources, economic growth, financial development, and financial globalization for the period from 1980 to 2021. It is also aimed to discover that the interactions alter with different levels of load capacity factor and explanatory variables using novel quantile-based approaches. Thanks to the unique approaches utilized, the environmental efficacy of the energy transition process can be determined for different quantiles and time periods. The results reveal that although financial globalization and financial development enhance environmental quality, economic growth and increasing natural resource rents harms environment. In addition, the increase in energy transition in all quantiles also has a positive effect on the load capacity. This impact is weak in quantiles with high and low environmental quality, but it becomes more pronounced in middle quantiles of load capacity. Our study offers various policy recommendations to increase the short- and long-term low-level environmental effectiveness of the energy transition process in Germany. [ABSTRACT FROM AUTHOR]

The construction industry has long been regarded as a relatively conservative and less innovative sector. Adjusting corporate governance structure is an effective method to promote innovation in the construction industry, as it can internally address the reluctance to innovate within companies. While it is widely known that the chief executive officer (CEO) plays a crucial role in firm innovation, few studies have explored the effects of CEO duality on innovation from a corporate governance aspect, which serves as a pressing need for construction industry. This study investigates the influence of CEO duality and its important moderating variable, CEO overconfidence, on corporate innovation based on a sample of stock-listed China construction companies and their patent data from 2009 to 2022. Quantile regression is applied to deeply investigate the variation in the impact of CEO duality throughout different conditional quantiles of the construction innovation performance distribution. The results demonstrate a favorable correlation between CEO duality and corporate innovation performance, and the promotion effect of CEO duality is more significant for firms with lower innovation performance. Additionally, CEO overconfidence negatively moderates the impact of CEO duality on corporate innovation. These findings indicate that CEO duality plays a noteworthy role in promoting corporate innovation within the construction industry and more effective corporate governance mechanism is needed to control CEO overconfidence, which promotes the negative impact of CEO duality. The research provides a research framework for exploring the dynamic impact of corporate governance structures on firm performance in the future. The results of this study are of significant importance for both the theoretical understanding and practical implementation of corporate governance mechanisms in the construction industry. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow one to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post–peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large dataset, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance. [ABSTRACT FROM AUTHOR]

We propose a flexible functional varying-coefficient single-index quantile regression model where the functional covariates of the linear part have time-varying coefficients and the single-index component offers great model flexibility in data analysis while circumventing the curse of dimensionality. The proposed model includes many existing quantile regression models for functional/longitudinal data as special cases. We use B-splines to estimate the link and coefficient functions. Under some mild conditions, we establish the asymptotic normality of the estimated index parameter vector, and obtain the convergence rates of the estimated link and coefficient functions. Moreover, we propose a score test to examine whether the effects of some covariates on the functional response are time-varying. Finally, we provide some numerical studies including Monte Carlo simulations and an empirical application to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Abstract–In the regression analysis, ordinary least square techniques is commonly used. However, the data's outcomes may be untrustworthy if there is an outliers in it. In order to deal with the outliers problem, robust quantile regression methods have been frequently presented as alternatives to OLS for a long time. In this article, primarily a exponential ratio-type estimators is suggested. After that, robust quantile regression estimators are proposed, that is a useful strategy. The application of robust quantile regression empowered the efficiency of the estimators especially for outliers in the data. The MSE equations of the various estimators are computed and compared to OLS approaches. Numerical illustration and simulations studies are performed to support our theoretical findings. [ABSTRACT FROM AUTHOR]

• More than two-thirds of the world's population are affected by the escalating issue of rising income inequality. This phenomenon is intensifying the risk of societal divisions, provoking protests in numerous countries, and impeding economic and social development. • BioTrade refers to products or services derived from biodiversity, which are commercialized and traded in a manner that upholds respect for both people and nature. • There exists a robust positive correlation between BioTrade and income inequality. However, the impact of BioTrade on inequality exhibits heterogeneity based on sub-regions and the distribution of income disparities. • The level of preparedness for advanced technologies serves as one pathway through which BioTrade influences global inequality. While empirical studies exploring the impacts of international trade on income inequality have continued to expand, yielding mixed results, less attention has been devoted to the effects of BioTrade on income redistribution. This article addresses this gap by examining the impact of BioTrade on income inequality, with a specific emphasis on the role of technological innovations. We employ a panel model encompassing 131 countries over the period 2010–2019, utilizing instrumental variable two-stage least squares and smoothed instrumental-variable quantile regression. Overall, the results indicate a strong positive link between BioTrade and income inequality, yet its effects remain heterogeneous across sub-regions and the distribution of income inequality. To elucidate this positive link, the article presents evidence of the crucial role of the level of readiness for advanced technologies. Consequently, technological innovations play a pivotal role in mitigating the accelerating effects of BioTrade on inequality. The results are robust to a battery of robustness tests, including variations in estimation methodologies, alternative measures of inequality and BioTrade, and the inclusion of control variables. These results call upon policymakers to champion technological innovations, such as Internet access and research and development, as crucial strategies for effectively and sustainably alleviating the aggravation of inequalities induced by BioTrade. [ABSTRACT FROM AUTHOR]

• The relationship between ESGD factors and subjective well-being is complex and nonlinear across income levels. • Renewable energy and digital skills only increase well-being in higher quantiles, reflecting affordability and economic complexity barriers in developing nations. • Women's political participation has mixed effects, negatively impacting well-being at lower quantiles but positively benefitting it at higher quantiles. • The "digital economy paradox" shows more digital skills combined with limited digital economies decreases well-being in lower-income countries. This study examines the heterogeneous impact of environmental, social, governance, and digital (ESGD) factors on subjective well-being across 86 countries from 2005 to 2019, using Method of Moments Quantile Regression. The results reveal complex, nonlinear relationships between ESGD factors and well-being. CO2 emissions display an inverted U-curve, suggesting eventual negative impacts after initial gains. Our findings suggest that renewable energy only benefits higher quantiles, revealing affordability issues. Social and governance factors like labor participation and women's political participation relate nonlinearly to well-being across income levels, reflecting employment quality and social norms differences. Similarly, digital factors improve well-being in high-income countries but not lower-middle-income nations, due to economic complexity gaps and the digital divide. A "digital economy paradox" emerges where more digital skills combined with limited digital economies decrease well-being in lower-middle income countries signifying the need for tailored digital policies. This study enhances understanding of links between ESGD factors and well-being patterns. [ABSTRACT FROM AUTHOR]

One issue that has received little attention is how students factor distance from home into their decisions about college. In this study, we used data from the Education Longitudinal Survey of 2002 (ELS:02) to examine the distances between a student's home and the colleges to which they applied, and how far from home they enrolled. We focused on how demand- and supply-side factors were related to the distances applied and enrolled. We tested the sensitivity of our findings to alternative ways of measuring the supply of postsecondary education within commuting distance, and identified factors associated with differences between a student's application and enrollment distances. Finally, we used quantile regression analysis to determine if the associations between demand- and supply-side factors and distances applied and enrolled varied along the distance distributions. [ABSTRACT FROM AUTHOR]

This study examined the quadratic role of renewable energy and economic growth on environment quality in Asia's five most populous countries: China, India, Indonesia, Pakistan, and Bangladesh. Previous literature has scarcely addressed these economies and ignores the quadratic role of GDP per capita (GDPPC) and renewable energy (REN) together in a single study. Therefore, using STIRPAT model, we first added the quadratic term of GDPPC and then REN. In both cases, a quantile regression technique is utilised to identify the inclusive relationship between CO2 emissions and determining factors, considering different quantiles (0.25, 0.50, 0.75, 0.90, and 0.95) of CO2 emissions. The findings show that during the annual sample period of 1983–2019, urbanisation, GDPPC, non‐renewable energy, and REN all have an impact on CO2 emissions. Urbanisation and REN have nominal effects, with a 1% change in these variables leading to a 0.19% and 0.05% change in CO2 emissions, respectively. Non‐renewable energy and GDPPC are found to be main sources of CO2 emissions in the region. GDPPC is positively associated with CO2 emissions across all quantiles, but higher quantiles show a stronger correlation (i.e., GDPPC coefficients vary from 0.26 to 0.66). In addition, the results also revealed that the square term of GDPPC and REN significantly reduces CO2 emissions. This implies that our results support the environmental Kuznets curve (EKC) hypothesis; an inverted U‐shaped is established for both GDPPC and REN. These results encourage policy makers to adopt renewable energy that is both growth and environment friendly. [ABSTRACT FROM AUTHOR]

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history. [ABSTRACT FROM AUTHOR]

In this article, I propose a method to estimate the counterfactual distribution of an outcome variable when the treatment is endogenous, continuous, and its effect is heterogeneous. The types of counterfactuals considered are those in which the change in treatment intensity can be correlated with the individual effects or when some of the structural functions are changed by some other group's counterparts. I characterize the outcome and the treatment with a triangular system of equations in which the unobservables are related by a copula that captures the endogeneity of the treatment, which is nonparametrically identified by inverting the quantile processes that determine the outcome and the treatment. Both processes are estimated using existing quantile regression methods, and I propose a parametric and a nonparametric estimator of the copula. To illustrate these methods, I estimate several counterfactual distributions of the birth weight of children, had their mothers smoked differently during pregnancy. [ABSTRACT FROM AUTHOR]

Bitcoin is regarded as a remarkable achievement of the Fourth Industrial Revolution and ranks among the most intricate technological and financial creations. It has long been the focus of attention of investors who are looking for a safe-haven asset. The purpose of this study is to check whether Bitcoin plays the role of a safe-haven asset (hedge). To achieve this, the impact of economic and political uncertainty (EPU) on the return and variation of Bitcoin is investigated. It is being analyzed whether, in comparison with the development of EPU, the returns and variations of Bitcoin show characteristics typical of safe-haven assets or those of ordinary speculative assets. As EPU levels elevate, it is anticipated that safe-haven assets like gold will see a rise in both their returns and variation, whereas typical speculative assets will experience heightened variation and diminished returns. The study uses ordinary linear regression and quantile regression models that cover data for the period between February 2013 and July 2023. These models play a crucial role in ascertaining if Bitcoin functions as a safe-haven asset during turbulent times and if it holds the capacity to serve as a hedge against economic uncertainty. The results of the study are of paramount importance for investors, as they help them decide whether to include Bitcoin in their portfolios for diversification and protection of their capital during unstable economic conditions. [ABSTRACT FROM AUTHOR]

This research looks at how plausible the tourism-led growth hypothesis is in the midst of corruption and political unpredictability. For empirical investigation, we utilize panel data from 1995 to 2018 for across the world. A dynamic panel data estimation method is employed for the estimation purpose. The outcomes of our analysis show that tourism-led growth is a phenomenon, but this is contingent on institutional factors. Specifically, high levels of corruption, political instability, and violence pose a significant barrier to beneficial effects of tourism on economic prosperity. We also employ panel quantile regression to estimate the asymmetric effects of tourism. Results of the analysis show that tourism is particularly beneficial for low-income economies. However, results also confirm that institutional factors such as corruption and political instability prevent countries, especially developing nations, from taking advantage of tourism. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) first broke out in China in August 2018 and triggered a substantial shock to the Chinese hog industry. Based on daily data from August 3, 2018, to May 30, 2020, we construct ASF news textual sentiment as a measurement indicator for the ASF epidemic and use a quantile regression model to examine the impacts of ASF news textual sentiment on the stock returns of hog firms. To assess the sentiment effect across periods, we divide the whole sample into three periods according to the trend in hog prices. Our results indicate that ASF news textual sentiment has a significant positive effect on stock returns across the period, but the effect varies across the three periods. In the stationary period, the sentiment has a significant positive effect on stock returns; the effect is reversed in rising and fluctuating periods. In addition, the sentiment effect varies across quantiles. The sentiment has asymmetric effects on stock returns, with a greater impact on higher returns. Our results suggest that opportunities to expand in scale and upgrade biological prevention and control on Chinese hog firms have long-term positive impacts on the development of the hog industry by ASF. [ABSTRACT FROM AUTHOR]

The accurate short-term point and probabilistic load forecasts are critically important for efficient operation of power systems and electricity bargain in the market. Fuzzy systems achieved limited success in electric load forecasting. On the other hand, support vector regression models have seldom been part of a winning solution of the electric load forecasting competitions during the last decade. In this paper, we propose a methodology to integrate the fuzzy memberships with support vector regression (SVR) and support vector quantile regression (SVQR) models for short-term point and probabilistic load forecasting, respectively. One fuzzy membership function is proposed to efficiently calculate the relative importance of the observations in the load history. Three SVR and one SVQR models, including L1-norm based SVR, L2-norm based SVR, kernel-free quadratic surface SVR and SVQR models, are utilized to demonstrate the effectiveness of the proposed methodology. For point load forecasting, we compare the proposed fuzzy SVR models with a multiple linear regression, a feed-forward neural network, a fuzzy interaction regression, and four SVR models. For probabilistic load forecasting, the proposed fuzzy SVQR model is compared with a quantile regression model, a quantile regression neural network, and a SVQR model. The results on the data of global energy forecasting competition 2012, demonstrate that the proposed fuzzy component can improve the underlying SVR and SVQR models to outperform their counterparts and commonly-used models for point and probabilistic load forecasting, respectively. [ABSTRACT FROM AUTHOR]

Research Summary: While previous organizational impression management (OIM) research focuses on highlighting firms in a favorable light, we explore CEOs' use of "strategic negativity" to manage expectations. We draw on OIM's psychological roots to predict that despite pressure to "be positive," when CEOs perceive stakeholders are motivated to raise their expectations and have an opportunity to do so, CEOs strategically use negativity to counteract this anticipated expectation increase. We test our predictions on 7330 quarterly earnings calls from 370 publicly traded firms (2008–2019), examining how the "motive" of a positive material earnings surprise and "opportunity" of a new fiscal year jointly increase CEO negativity in prepared remarks. We elaborate the wide applicability of strategic negativity, the "other side" of the OIM phenomenon. Managerial Summary: In contrast to the prevailing view that CEOs usually "positively spin" the firm's situation to stakeholders, we investigate how CEOs strategically use negativity to counteract stakeholder optimism, provided CEOs perceive expectations are likely to rocket upward. We argue that positive news represents a "motive" and a chance to reflect represents an "opportunity," and that together they risk raising expectations. Analyzing 7330 quarterly earnings calls of 370 companies (2008–2019), we specifically examined how both (1) a positive earnings surprise and (2) a new fiscal year force CEOs out of their positivity comfort zone and encourage them to be strategically negative in earnings call remarks, to try to lower stakeholder expectations. Our results support this view and pave the way for future research. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered. [ABSTRACT FROM AUTHOR]

This paper investigates the interconnectedness patterns between agricultural commodities, crude oil, and ethanol, along with their determinants before and during the COVID-19 pandemic and the Russia–Ukraine conflict. We employ a time-varying parameter vector autoregression model to analyze interconnected behaviors among energy and agricultural commodities. Additionally, quantile regression is used to assess the impact of financial and economic fundamentals on transmission mechanisms in commodity markets. The empirical findings reveal time-varying and crisis-responsive linkages between energy and agricultural commodities, particularly during the COVID-19 pandemic and Russia–Ukraine conflict. Furthermore, economic and financial market uncertainties emerge as significant determinants of the interconnectedness between these commodity groups. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide new evidence on the relationship between trade openness and carbon dioxide (CO2) emissions in Africa. Based on recent data and an uncommon and more informative composite indicator of trade openness proposed by Squalli and Wilson, we use an augmented Stochastic Impact by Regression on Population, Affluence and Technology (STIRPAT) model. The empirical evidence using the Two-Stage Least Square estimation (2SLS) validates the "pollution haven" hypothesis and shows that trade openness increases CO2 emissions in Africa. However, the elasticity varies greatly depending on the different measures of trade openness used. Moreover, we find that trade openness is associated with an increase in CO2 emissions in North Africa, South Africa and West Africa, but rather has a negative effect on CO2 emissions in East Africa and in Central Africa. Furthermore, the quantile regression approach shows that the effect of trade openness is increasing on the 10th, 25th, 50th, and 75th quintiles, but decreasing at the 90th quintile, thus highlighting the "scale effect". The results obtained are robust even when using other indicators of environmental quality. The findings suggest that it is important to have a real measure of the level of trade openness of countries, which will allow African leaders in particular to take appropriate measures to strengthen barriers to the entry of technologies and polluting materials, and invest more in green technologies. [ABSTRACT FROM AUTHOR]

The rental market, notably within the UK, is facing increased scrutiny with the tightening of regulation in relation to energy performance and the increases in energy costs and rental unaffordability. Whilst a sizeable volume of research has examined the pricing effects of EPCs and house prices, scrutiny of this relationship within the private rental sector remains more embryonic. This study, using 2,914 transactions for Northern Ireland, extends the traditional analysis beyond the conditional mean estimate by examining the quantiles of the relationship between EPCs and rental prices. The findings provide evidence of a rental premium of 0.2% for a one-point improvement in energy efficiency. In terms of EPC ratings, we find premiums for B- and C-rated dwellings of 8.2% and 2.4% and also discount effects for E, F and G-rated properties ranging between 3.9%-5.5%. The quantile findings exhibited a parabolic effect across the price distribution demonstrating the lowest and highest priced properties to comprise higher price premiums of 13% and 16% for B-rated dwellings, however more pronounced discount effects for F- and G-rated properties at the lowest and highest deciles. The findings provide evidence to help alleviate the split-incentive problem between landlords and tenants within the UK. [ABSTRACT FROM AUTHOR]

A complete understanding of the interplay between environmental regulations and fiscal decentralization for the realization of health outcomes is crucial for policy formulation and decision-making. The aim of the study is to investigate both policy variables' separate and combined impacts using data on four BRICS economies from 2000 to 2020. The study has employed the novel method of moments quantile regression to quantify the effect. The findings of the study show (i) a significant impact of regulations on health outcomes in higher quantiles. Environmental regulations have a strong positive impact on all three-health proxies, that is, health expenditures, life expectancy, and the number of infant deaths. Total revenue and expenditure decentralization affect health outcomes positively, while tax revenue decentralization negatively impacts them, with the effect being stronger in the lower quantiles; (ii) the combined impact of decentralization and environmental regulations turned out to be negative and significant in our study; and (iii) all variables have unidirectional causality. However, with tax revenue decentralization, health expenditures, life expectancy, and infant deaths have bidirectional causality. This finding has a strong policy implication for the policymakers. Although both policies positively impact health indicators, their interaction leads to deteriorating health outcomes. From a policy point of view, it is suggested to strike a balance between regulations and fiscal decentralization to realize the full potential of this policy mix to get better health outcomes. This study adds to previous research by incorporating the interconnected impact of environmental regulations and fiscal decentralization on health outcomes in BRICS economies. [ABSTRACT FROM AUTHOR]

Background: Mild stroke affects more than half the stroke population, yet there is limited evidence characterizing cognition over time in this population, especially with predictive approaches applicable at the individual-level. We aimed to identify patterns of recovery and the best combination of demographic, clinical, and lifestyle factors predicting individual-level cognitive state at 3- and 12-months after mild stroke. Methods: In this prospective cohort study, the Montreal Cognitive Assessment (MoCA) was administered at 3–7 days, 3- and 12-months post-stroke. Raw changes in MoCA and impairment rates (defined as MoCA<24 points) were compared between assessment time-points. Trajectory clusters were identified using variations of ≥1 point in MoCA scores. To further compare clusters, additional assessments administered at 3- and 12-months were included. Gamma and Quantile mixed-effects regression were used to predict individual MoCA scores over time, using baseline clinical and demographic variables. Model predictions were fitted for each stroke survivor and evaluated using model cross-validation to identify the overall best predictors of cognitive recovery. Results: Participants' (n = 119) MoCA scores improved from baseline to 3-months (p<0.001); and decreased from 3- to 12-months post-stroke (p = 0.010). Cognitive impairment rates decreased significantly from baseline to 3-months (p<0.001), but not between 3- and 12-months (p = 0.168). Nine distinct trajectory clusters were identified. Clinical characteristics between clusters at each time-point varied in cognitive outcomes but not in clinical and/or activity participation outcomes. Cognitive performance at 3- and 12-months was best predicted by younger age, higher physical activity levels, and left-hemisphere lesion side. Conclusion: More than half of mild-stroke survivors are at risk of cognitive decline one year after stroke, even when preceded by a significantly improving pattern in the first 3-months of recovery. Physical activity was the only modifiable factor independently associated with cognitive recovery. Individual-level prediction methods may inform the timing and personalized application of future interventions to maximize cognitive recovery post-stroke. [ABSTRACT FROM AUTHOR]

We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated using a basis expansion approach. Consequently, we approximate the infinite-dimensional function-on-function linear quantile regression model using a multivariate quantile regression model constructed from these partial quantile regression components. To evaluate the efficacy of our proposed techniques, we conduct a series of Monte Carlo experiments and analyze an empirical dataset, demonstrating superior performance compared to existing methods in finite-sample scenarios. Our techniques have been implemented in the ffpqr package in . [ABSTRACT FROM AUTHOR]

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples validation strategies were employed to evaluate the accuracy of the predicted maps quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e., CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 during independent sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil properties and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024). [ABSTRACT FROM AUTHOR]

AbstractOur objective was to establish normative data and reliable change indices (RCI) for the Montreal Cognitive Assessment’s auditory items (MoCA-22). 4,935 cognitively unimpaired participants were administered the MoCA during an in-person visit to an Alzheimer’s Disease Research Center (Mage = 67.9, Meducation = 16.2, 65.8% women, 75.9% non-Hispanic-White), with 2,319 unimpaired participants returning for follow-up. Normative values and cutoffs were developed using demographic predictions from ordinary and quantile regression. Test-retest reliability was calculated using Spearman and intraclass correlations. RCI values were calculated using Chelune and colleagues’ (1993) formula. Education, age, and sex were all statistically related to MoCA-22 scores, with education having the strongest relationship. Notably, these relationships were not consistent across MoCA-22 quantiles, with education becoming more important and sex becoming less important for predicting low scores. These models were integrated into a calculator for deriving normative scores for an individual case. Furthermore, there was adequate-to-good test-retest reliability (ϱ = 0.56 95% CI [.54, .59]; ICC = 0.75, 95% CI [.73, .77]) and changes of at least 2-3 points are necessary to identify reliable change at 1-3-year follow-up. These findings add to the literature regarding utility of the MoCA-22 in the cognitive screening of older adults. [ABSTRACT FROM AUTHOR]

Introduction: Thyroid function during pregnancy fluctuates with gestational weeks, seasons and other factors. However, it is currently unknown whether there is a fetal sex-specific thyroid function in pregnant women. The purpose of this study was to investigate the fetal sex differences of maternal thyroid-stimulating hormone (TSH) and free thyroxine (FT4) in pregnant women. Methods: This single-center retrospective real-world study was performed by reviewing the medical records of pregnant women who received regular antenatal health care and delivered liveborn infants in Shanghai First Maternity and Infant Hospital (Pudong branch), from Aug. 18, 2013 to Jul. 18, 2020. Quantile regression was used to evaluate the relationship between various variables and TSH and FT4 concentrations. The quantile regression also evaluated the sex impact of different gestational weeks on the median of TSH and FT4. Results: A total of 69,243 pregnant women with a mean age of 30.36 years were included. 36197 (52.28%) deliveries were boys. In the three different trimesters, the median levels (interquartile range) of TSH were 1.18 (0.66, 1.82) mIU/L and 1.39 (0.85, 2.05) mIU/L, 1.70 (1.19, 2.40) mIU/L; and the median levels (interquartile range) of FT4 were 16.63 (15.16, 18.31) pmol/L, 14.09 (12.30, 16.20) pmol/L and 13.40 (11.52, 14.71) pmol/L, respectively. The maternal TSH upper limit of reference ranges was decreased more in mothers with female fetuses during gestational weeks 7 to 12, while their FT4 upper limit of the reference ranges was increased more than those with male fetuses. After model adjustment, the median TSH level was 0.11 mIU/L lower (P <0.001), and FT4 level was 0.14 pmol/L higher (P <0.001) for mothers with female fetuses than those with male fetuses during gestational weeks 9 to 12. Discussion: We identified sexual dimorphism in maternal thyroid function parameters, especially during 9-12 weeks of pregnancy. Based on previous research, we speculated that it may be related to the higher HCG levels of mothers who were pregnant with girls during this period. However, longitudinal studies are needed to determine if fetal sex differences impact the maternal thyroid function across pregnancy. [ABSTRACT FROM AUTHOR]

There are many studies aimed at promoting positive lifestyle behaviors to reduce lifetime risk of cancer and related diseases. However, assessing these modifiable behaviors through statistical modeling is challenging because of the multidimensionality of interrelated measurements that may dramatically differ between at-risk individuals. Taking into account this heterogeneity while considering the multidimensionality of behavior changes is fundamental to tailoring interventions to their needs. Biomarkers that identify high-risk individuals may help validate proximal measures, but the number of validated methods that link biomarkers to multiple behavioral measurements by determining their dynamic relations with disease risks is limited to just a few, since it requires an advanced statistical methodology to address challenges in analyzing biomarker data, including left-censoring due to limits of detection. To address these challenges, we propose a method that constructs a quantile-specific weighted index of multiple behavioral measurements. Under the quantile regression framework, the proposed method renders a multidimensional view of risk-specific behavioral patterns by connecting them with biomarker levels to provide better insights into heterogeneous behavioral profiles among at-risk populations. We evaluate performances of the proposed method through simulations, and illustrate its applications to the Tu Salud ¡Sí Cuenta! data by examining behavior changes among Mexican-American adults. [ABSTRACT FROM AUTHOR]

AbstractData transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN. [ABSTRACT FROM AUTHOR]

The Sysmex XN series haematopoietic progenitor cell (XN-HPC) is a novel tool for assessing stem cell yield before allogeneic haematopoietic stem cell transplantation. This study aimed to establish a reference interval (RI) for XN-HPC in peripheral blood allogeneic transplant donors following granulocyte colony-stimulating factor (G-CSF) stimulation and determine its clinical significance. All specimens were analysed using Sysmex XN-20. Samples were collected and analysed using non-parametric percentile methods to define the RIs. Quantile regression was used to explore the dependency of the RIs on sex and age. Samples were included in clinical decision limits for apheresis based on receiver operating characteristic curve analysis. The non-parametrically estimated RI for XN-HPC was 623.50 (90% confidence interval [CI90%] 510.00–657.00) to 4,144.28 (CI90% 3,761.00–4,547.00). The RIs for the XN-HPC were not age-dependent but were sex-dependent. The RI for males was 648.40 (CI90% 582.00–709.00)–4,502.60 (CI90% 4,046.00–5,219.00) and for females was 490.90 (CI90% 311.00–652.00)–3,096.90 (CI90% 2,749.00–3,782.00). Comparisons based on XN-HPC values between the poor and less-than-optimal groups, good and less-than-optimal groups, and good and non-good groups had areas under the curve of 0.794 (P < 0.001), 0.768 (P < 0.001), and 0.806 (P < 0.001), respectively, indicating a good predictive value for mobilisation effectiveness. XN-HPC data exceeding 3974 × 106/L suggested that a sufficient number of stem cells could be collected clinically. Values > 5318 < 106/L indicated 100% mobilisation effectiveness. We established an RI for XN-HPC in peripheral blood allogeneic transplant donors following G-CSF stimulation and determined clinical decision thresholds for mobilisation efficiency. [ABSTRACT FROM AUTHOR]

The enormous ensembles of energy resources have elevated Sub‐Saharan Africa (SSA) to a prominent position in the world energy scene as a leading region in forming a sustainable energy future. However, over half of the population in the region lives without electricity and less than 20% of the population has access to clean fuels. The excessive reliance on fossil fuels such as coal, kerosene, and solid biomass has implications for environmental outcomes in SSA. Nevertheless, the environmental impact of energy poverty from the SSA perspective remains an under‐explored terrain in the research landscape. In this context, to address the pressing challenges of energy poverty and environmental sustainability, this study aims to explore the threat energy poverty poses on the ecological footprints, focusing on “land‐use, carbon footprint, forestry resources, and fishing ground” across 35 SSA countries using disaggregated data covering the period 2000 and 2021. This study employs the novel Method of the Moments Quantile Regression following the Stochastic Impacts by Regression on Population, Affluence and Technology model as the reference theoretical and analytical framework. The findings disclose heterogeneous effects of rural and urban energy poverty on ecological footprint items. When comparing the magnitudes, we found that rural energy poverty has a more detrimental impact on land‐use than urban energy poverty. Energy poverty is found to have no reliable power to explain the variation in carbon footprint and fishing ground. In the meantime, urban energy poverty exerts a positive effect on forestry resource sustainability. Surprisingly, population density is found to have a significant and desirable impact on land‐use. Based on the obtained results, numerous policy suggestions have been discussed along with some prospects for future research. [ABSTRACT FROM AUTHOR]

AbstractOne compelling use of citation networks is to characterize papers by their relationships to the surrounding literature. We propose a method to characterize papers by embedding them into two distinct “co-factor” spaces: one describing how papers send citations, and the other describing how papers receive citations. This approach presents several challenges. First, older documents cannot cite newer documents, and thus it is not clear that co-factors are even identifiable. We resolve this challenge by developing a co-factor model for asymmetric adjacency matrices with missing lower triangles and showing that identification is possible. We then frame estimation as a matrix completion problem and develop a specialized implementation of matrix completion because prior implementations are memory bound in our setting. Simulations show that our estimator has promising finite sample properties, and that naive approaches fail to recover latent co-factor structure. We leverage our estimator to investigate 255,780 papers published in statistics journals from 1898 to 2024, resulting in the most comprehensive topic model of the statistics literature to date. We find interpretable co-factors corresponding to many statistical subfields, including time series, variable selection, spatial methods, graphical models, GLM(M)s, causal inference, multiple testing, quantile regression, semiparametrics, dimension reduction, and several more. [ABSTRACT FROM AUTHOR]

Surface reconstruction plays a pivotal role in various fields, including reverse engineering, and oil and gas exploration. However, errors in available data and insufficient surface morphology information often introduce uncertainty into the reconstruction. It is crucial to accurately characterize and visualize the uncertainty in surface reconstruction for risk analysis and planning further data collection. To this end, this paper proposes an uncertainty characterization method based on twin support vector regression. First, various modeling data are effectively integrated and the information contained in the high-confidence sample is efficiently utilized through the uncertainty interval generated by quantiles and upper/lower bound constraints. Second, well-path points are incorporated by imposing inequality constraints on the corresponding prediction points. Finally, in order to reduce computation time, the problem of uncertainty characterization is formulated as two smaller-scale quadratic programming. The results obtained from a real fault dataset and a synthetic dataset validate the effectiveness of the proposed method. When well data are available, the generated uncertainty envelopes are constrained by well data, which can partially mitigate reconstruction uncertainties. [ABSTRACT FROM AUTHOR]

ABSTRACT The relationship between educational level and the age at which women start families has been extensively researched. However, studies have primarily explored how additional schooling shifts the mean or, more broadly, only one point of the age at first union and first birth distributions. This ignores variation in the association between education and the timing of family formation, and the fact that schooling might shape behaviours of vulnerable and more privileged women differently. Using quantile regressions, this article examines heterogeneity in the relationship between education and the age at first union and first birth across the distribution of these events within 50 low‐ and middle‐income countries. It investigates whether additional schooling shifts relatively early union formation and childbearing (that is, lower parts of distributions) similarly or differently than it shifts other parts of the distributions. It finds that association between an additional year at school and the age at first union and birth is weaker in the lower than the upper parts of the distributions. Education has a relatively weak effect on the reduction of early first unions and births and plays an unequalizing role in shaping family formation within countries. These findings are key to understanding persistently high levels of early marriage and pregnancy, despite the expansion of education. [ABSTRACT FROM AUTHOR]