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Using data sets consisting of cross-sectional surveys drawn from nationally representative samples of adolescents in the U.S. and official sources of crime statistics, a portrait of aggression and violence among adolescents is presented. Fluctuations in self-reported and official sources of data are examined by year, gender, grade, and race. Both distal and contemporary risk factors for aggression and violence are discussed. Distal risk factors for violence in adolescence are presented using longitudinal studies. The General Aggression Model provides the framework for exploring which contemporary personal and situational factors increase or decrease risk for aggression and violence. Dating aggression in adolescence is placed in the context of normal development and variable according to individual partner and relationship factors. This book presents rigorously tested scientific prevention programs for adolescents with violent and aggressive behavior.

In many countries, document and border procedures create trading barriers, thereby impairing economic growth. These can range from insufficient transshipment facilities to unsupportive institutional arrangements. To address this, countries have taken reforms to improve their procedures by introducing electronic documentation systems, strengthening border infrastructure, and enhancing customs procedures. However, the efficiency of the document and border procedures in each country remains unclear, as well as how new reforms can affect these. This study investigated the efficiency of document and border procedures in each country, defined as the trade volume and value per required cost, time, and documents in the trading procedures. The efficiencies were calculated through a data envelopment analysis with cross-sectional data from 2019 and a window analysis with panel data from 2014 to 2019. The study found a positive change in export procedure efficiency after all three types of reforms were instituted in a country, but a positive change in import efficiency only after the introduction of electronic documentation. All countries were classified according to their document and border procedure strengths and weaknesses.

Objective To explore and validate the association between the oxidative balance and prevalence of diabetic kidney disease (DKD) and mortality in patients with diabetes. Study design A large and representative sample from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016 was analyzed to study the potential association between Oxidative Balance Score (OBS) and prognosis of DKD in adult diabetic patients. Weighted multivariate logistic regression analysis was conducted to examine the relationship between OBS and DKD risk. Subgroup analysis, sensitivity analysis, and mediation effect analysis were conducted to explore the effect of the covariates and assess the robustness of the findings. Mendelian randomization (MR) was employed to evaluate the correlated relationship between mitochondrial reactive oxygen species (ROS) levels and DKD at the genetic level. Result The highest OBS quartile showed the most significant negative correlation with DKD compared to the lowest OBS quartile (OR = 0.62, 95% CI 0.41–0.92, P = 0.017). Higher OBS was associated with a reduced risk of DKD (OR = 0.96; 95% CI = 0.93, 0.98; P < 0.001) and mortality (P = 0.021 by log‐rank) in diabetic patients. This association remained robust even after excluding individual OBS components. Subgroup analysis revealed the interaction of metabolic syndrome on OBS was significant. Mediation analyses revealed that OBS's effect on DKD was independent of blood uric acid and cholesterol. Restricted cubic spline (RCS) analysis indicated a typical L‐shaped relationship between OBS and DKD risk. The physical activity was identified as the core variable predicting DKD risk by two machine learning algorithms. MR showed a potential correlated relationship between ROS and microalbuminuria in DKD. Conclusions The high level of oxidative balance score was negatively correlated with the risk of DKD and mortality in diabetic patients. The low level of oxidative balance was positively correlated with the risk of DKD and mortality in diabetic patients.

The paper deals with the estimation of a high dimensional covariance with a conditional sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the principal orthogonal complement thresholding method 'POET' to explore such an approximate factor structure with sparsity. The POET-estimator includes the sample covariance matrix, the factor-based covariance matrix, the thresholding estimator and the adaptive thresholding estimator as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the effect of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

Background Precise identification of risk factors for suicide has been found as fundamentally challenging for the stakeholders. In search of that, the determination of potential biological markers of suicide has been scrutinizing in recent days. However, replicative studies across the culture and time are warranted to utilize the biomarkers in decision‐making while considering suicide prevention. Objectives We aimed to measure the neutrophil‐to‐lymphocyte ratio (NLR) and C‐reactive protein (CRP) in non‐fatal suicidal attempts. Methods This cross‐sectional study was conducted from August 2020 to May 2021. Data were collected from 26 hospitalized patients after an immediate non‐fatal suicidal attempt. We collected blood samples and assessed the complete blood count, NLR, and CRP. Data were analyzed by Statistical Package for the Social Science version 24.0 and Microsoft Excel software. We performed the independent Mann‐Whitney U test to determine the variations between the groups. A value <.05 was considered as the level of significance. Results The mean age of the study population was 24.1 (±7.3), more than half of them (n = 19, 73.1%) were female. The mode of attempt was either hanging (n = 13, 50.0%) or poisoning (n = 13, 50.0%). The previous attempt was noted in 12 (46.2%) cases. The mean CRP level was 16.8 (±30.2) mg/L and the mean NLR level was 8.3 (±6.8) for the total sample. Both the CRP and NLR were significantly higher among those who attempted hanging (P = .019 and .001). Conclusions The current study revealed preliminary findings on CRP and NLR among non‐fatal suicides in Bangladesh where both of the parameters were significantly higher in non‐fatal hanging.

We propose a novel technique to boost the power of testing a high-dimensional vector H : θ = 0 against sparse alternatives where the null hypothesis is violated by only a few components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high-dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a \"power enhancement component,\" which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. The proposed methods are then applied to testing the factor pricing models and validating the cross-sectional independence in panel data models.

We review and evaluate the methods commonly used in the accounting literature to correct for cross-sectional and time-series dependence. While much of the accounting literature studies settings in which variables are cross-sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2 statistic and Newey-West corrected Fama-MacBeth standard errors do not correct for both cross-sectional and time-series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the implied cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well-specified test statistics.

A central focus of empirical research in strategic management has been to understand the relationships associated with the structure-strategy-performance paradigm. To examine these relationships, investigators have relied extensively on cross-sectional methods that embody the implicit assumption that model parameters are stable across firms and over time. Yet, many of the theoretical constructs used in strategic management have clear firm- and time-specific components. Hence, it might be expected that the parameters of the relationships investigated empirically will vary across firms and over time. Whereas recent research has raised concerns about the use of cross-sectional analysis when parameters vary over time, little attention has been given to the issue of parameter variability across firms. Given the focus of strategy researchers on firm-level effects and the predominant reliance on cross-sectional analysis, accounting for across-firm variability is a significant methodological issue. Failure to account for such variability can lead to biased parameter estimates and incorrect inferences. This paper argues for the adoption of alternative methods that can overcome the limitations of a cross-sectional analysis and it offers guidance on how researchers can proceed to use these alternative methods to explicitly incorporate or test for variation in model parameters across firms or over time.

The essentials of our paper of 2002 are briefly summarized and compared with other criteria for model comparison. After some comments on the paper's reception and influence, we consider criticisms and proposals forimprovement made by us and others.

We study the effect of US food aid on conflict in recipient countries. Our analysis exploits time variation in food aid shipments due to changes in US wheat production and cross-sectional variation in a country's tendency to receive any US food aid. According to our estimates, an increase in US food aid increases the incidence and duration of civil conflicts, but has no robust effect on interstate conflicts or the onset of civil conflicts. We also provide suggestive evidence that the effects are most pronounced in countries with a recent history of civil conflict.