Bootstrap Approach Research Articles

Abstract 4137770: Development of a User-Friendly Self-Screening Tool for Assessing Metabolic Syndrome Risk in young adults from economically challenged regions

Background: Metabolic syndrome is a cluster of conditions that increase the risk of heart disease and diabetes. Early identification and management are crucial, particularly in economically challenged regions where access to healthcare may be limited. Research Questions/Hypothesis: User-friendly self-report data accurately predict metabolic outcomes. Aims: To develop and validate nomograms for individualized estimation of metabolic syndrome risk. Methods: Data from 521 college students (60.1% aged 17-20 years; 68.7% female; 28.0% white) were collected in 2022/2023 from two Brazilian cities. These cities are located in the country's poorest states, with Gini indices of 0.56 and 0.43. The potential predictors include demographic and economic variables, school-related factors, behaviors, and body weight. Based on predictors for abdominal obesity identified through multilevel logistic regression, we created a nomogram model. We performed the Hosmer-Lemeshow test to assess model calibration and used a bootstrapping approach (B = 150) for internal validation. To evaluate external validity, we assessed metabolic syndrome in a subset of 375 students. The area under the receiver operating characteristic curve (AUROC), with a threshold of 0.70, was used to evaluate the model's discrimination accuracy. Results: We identified 114 (23.0%) college students who were abdominally obese. We found ten variables associated with the primary outcome: age, biological sex, physical education facilities, enrollment in sports competition (during elementary school); grade retention, preferred subject, physical education classes per week; enrollment in sports training (during secondary school); adherence of 24-hour movement behaviors and body weight. The proposed nomogram showed acceptable performance in the AUROC (0.94 [95% CI: 0.92-0.96). The calibration assessment indicated reasonable consistency of our model (p > 0.05). In the internal validation, we observed a decreased predictive capability (AUROC = 0.86). Conclusion: The 24h-MESYN risk score offers an effective self-screening tool for college students from diverse racial and ethnic backgrounds in economically challenged regions to assess their risk of developing metabolic syndrome.

Bootstrap Inference for Group Factor Models

Abstract Andreou et al. (2019) have proposed a test for common factors based on canonical correlations between factors estimated separately from each group. We propose a simple bootstrap test that avoids the need to estimate the bias and variance of the canonical correlations explicitly and provide high-level conditions for its validity. We verify these conditions for a wild bootstrap scheme similar to the one proposed in Gonçalves and Perron (2014). Simulation experiments show that this bootstrap approach leads to null rejection rates closer to the nominal level in all of our designs compared to the asymptotic framework.

Improved tools for estimation of ammonia emission from field-applied animal slurry: Refinement of the ALFAM2 model and database

Ammonia volatilization from animal slurry applied to agricultural fields reduces nitrogen use efficiency in agriculture and pollutes the environment. This work presents new versions of a model and database focused on this route of N loss. The public ALFAM2 database (https://github.com/AU-BCE-EE/ALFAM2-data) was expanded with ammonia emission and ancillary measurements for >700 additional field plots. The ALFAM2 model (https://github.com/AU-BCE-EE/ALFAM2, https://zenodo.org/records/13312251) was extended with the addition of an ammonia sink for more plausible predictions over extended durations and to better reflect the expected reduction in emission rate several days after slurry application. A new parameter set was developed for the model taking into account the newly available measurement data. Model efficiency improved to 0.67 for the parameter estimation subset (0.52 for cross-validation) and mean absolute error was around 10% of applied total ammoniacal nitrogen. As in earlier versions, predicted emission is sensitive to application method, slurry dry matter and pH, air temperature, and wind speed. A collection of parameter sets for estimating uncertainty in average predictions was developed using a bootstrap approach. Predicted uncertainty is not trivial, and is high for some variable combinations, highlighting the challenge of making predictions based on available measurement data. Still, this work has resulted in more accurate, comprehensive, transparent, and flexible tools for emission inventory and related work on ammonia loss from field-applied slurry.

Noninvertible Symmetries, Anomalies, and Scattering Amplitudes.

We show that crossing symmetry of S matrices is modified in certain theories with noninvertible symmetries or anomalies. Focusing on integrable flows to gapped phases in two dimensions, we find that S matrices derived previously from the bootstrap approach are incompatible with noninvertible symmetries along the flow. We present consistent alternatives, which, however, violate standard crossing symmetry and obey modified rules dictated by fusion categories. We extend these rules to theories with discrete anomalies.

Mediating Effects of Resilience on the Relationship Between Stress and Professional Commitment Among Nursing Students: A Cross-Sectional Study.

Research on the relationship between stress, professional commitment, and resilience among nursing students is lacking. To examine the mediating effect of resilience on the relationship between stress and professional commitment among nursing students during their fundamental nursing practicum. A cross-sectional design was used. Nursing students who were aged 17 to 20years and had completed fundamental nursing practicum within 1week were recruited. The survey covered stress, resilience, and professional commitment. The mediation analysis was conducted using the PROCESS macro with a bootstrap approach. This study recruited 485 nursing students. Stress was significantly negatively correlated with resilience and professional commitment, whereas resilience was significantly positively correlated with professional commitment. Furthermore, resilience partially mediated the relationship between stress and professional commitment. Appropriate interventions should be implemented to help nursing students reduce stress and improve their resilience, which can thus enhance their professional commitment to the nursing profession.

Calibrated Equilibrium Estimation and Double Selection for High-dimensional Partially Linear Measurement Error Models

In practice, measurement error data is frequently encountered and needs to be handled appropriately. As a result of additional bias induced by measurement error, many existing estimation methods fail to achieve satisfactory performances. This paper studies high-dimensional partially linear measurement error models. It proposes a calibrated equilibrium (CARE) estimation method to calibrate the bias caused by measurement error and overcomes the technical difficulty of the objective function unbounded from below in high-dimensional cases due to non-convexity. To facilitate the applications of the CARE estimation method, a bootstrap approach for approximating the covariance matrix of measurement errors is introduced. For the high-dimensional or ultra-high dimensional partially linear measurement error models, a novel multiple testing method, the calibrated equilibrium multiple double selection (CARE–MUSE) algorithm, is proposed to control the false discovery rate (FDR). Under certain regularity conditions, we derive the oracle inequalities for estimation error and prediction risk, along with an upper bound on the number of falsely discovered signs for the CARE estimator. We further establish the convergence rate of the nonparametric function estimator. In addition, FDR and power guarantee for the CARE–MUSE algorithm are investigated under a weaker minimum signal condition, which is insufficient for the CARE estimator to achieve sign consistency. Extensive simulation studies and a real data application demonstrate the satisfactory finite sample performance of the proposed methods.

A nomogram for predicting postpartum post-traumatic stress disorder: a prospective cohort study

BackgroundPostpartumpost-traumatic stress disorder (PTSD), as a psychological stress disorder, has long-term and widespread harm. Still, compared with other postpartum psychiatric disorders, postpartum PTSD has received relatively littleattention in China. This study aims to investigate the risk factors of postpartum PTSD and to develop a convenient and rapid nomogram screening tool to help clinical staff identify high-risk pregnant womenin time and take preventative and management measures.MethodsRecruited pregnant women hospitalized for delivery in Qingdao Municipal Hospital and Jinzhou Maternal and Child Health Hospital from November 2022 to October 2023 as convenient samples for the questionnaire survey. Telephone follow-up was conducted 42 days after delivery. After univariate analysis, multicollinearity analysis, and logistic regression analysis, the risk factors of postnatal PTSD were obtained, a prediction model was established, and a nomogram was drawn by R software. G*power3.1.9.7 calculated the effectiveness of the test. The model was validated internally using the Bootstrap approach, and external validation was carried out using a verification group. The accuracy of the model's predictions and its clinical application value were evaluated by the area under the curve, calibration plot, and decision curve analysis.ResultsA total of 602 women were recruited in this study, and the incidence of postpartum PTSD was 11.1% (67/602). Multifactorial logistic regression analysis showed that poor self-assessment of sleep status in late pregnancy (OR = 5.336), cesarean section (OR = 2.825), instrumental delivery (OR = 5.994), having fear of labor (OR = 4.857), and a high score of Five Factors Inventory Neuroticism subscale (OR = 1.244) were independent risk factors for developing postpartum PTSD. A high Quality of Relationship Index score (OR = 0.891) was a protective factor for postpartum PTSD. In the training and validation sets, the nomogram model's area under the ROC curve was 0.928 and 0.907, respectively. The calibration curves showed that the nomogram model was well-fitted, and the Decision Curve Analysis indicated that the nomogram model had good value for clinical application.ConclusionsWith its strong predictive capacity, the prediction model built using postpartum PTSD risk factors can help clinical caregivers identify high-risk pregnant women early on and implement timely preventive intervention strategies.

The mediating role of kinesiophobia in pain intensity, physical function, and physical activity level in inflammatory arthritis.

Pain is the main symptom of inflammatory arthritis and it can impair physical functional performance and physical activity level. Some individuals can develop kinesiophobia and experience a vicious circle of worsening health. This study aimed to investigate the association between pain and physical functional performance/physical activity and determine whether kinesiophobia mediates this association. This was a cross-sectional study with individuals diagnosed with inflammatory arthritis (rheumatoid arthritis or spondyloarthritis) treated at a Rheumatology Outpatient Clinic. We assessed pain (Visual Analogue Scale), physical activity (International Physical Activity Questionnaire - long form), kinesiophobia (Tampa Scale for Kinesiophobia), and physical functional performance (Handgrip Strength Dynamometry, 30-second Sit-to-Stand test, Static Muscular Endurance test, and Timed Up and Go test). The mediating effect of kinesiophobia on the relationship between pain intensity and physical functional performance/physical activity was analyzed, and the significance of the mediating effect (kinesiophobia) was tested through a bootstrap approach. Thirty-three individuals with inflammatory arthritis (mean age: 48 ± 12 years) participated in the study. Kinesiophobia mediates the relationship between pain intensity and physical functional performance analyzed by the 30-second Sit-to-Stand test [indirect effect (IE)overall = -0.343, bootstrap 95% confidence interval (CI): -0.698 to -0.002] and by knee flexion at 90º of the dominant limb (IEoverall = -1.55, bootstrap 95% CI: -3.43 to -0.237). In conclusion, pain intensity can affect physical functional performance through kinesiophobia in individuals with inflammatory arthritis.

Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers

Outliers are typically identified using frequentist methods. The data are classified as “outliers” or “not outliers” based on a test statistic that measures the magnitude of the difference between a value and the majority part of the data. The threshold for a data value to be an outlier is typically defined by the user. However, a subjective choice of the threshold increases the uncertainty associated with outlier status for each data value. A cellwise outlier detection algorithm named FuzzyHRT is used to automate the editing process in repeated surveys. This algorithm uses Bienaymé–Chebyshev’s inequality and fuzzy logic to detect four different types of outliers resulting from format inconsistencies, historical, tail, and relational anomalies. However, fuzzy logic is not suited for probabilistic reasoning behind the identification of anomalous cells. Bayesian methods are well suited for quantifying the uncertainty associated with the identification of outliers. Although, as suggested by the literature, there exist well-developed Bayesian methods for record-level outlier detection, Bayesian methods for identifying outliers within individual records (i.e., at the cell level) remain unexplored. This paper presents two approaches from the Bayesian perspective to study the uncertainty associated with identifying outliers. A Bayesian bootstrap approach is explored to study the uncertainty associated with the output scores from the FuzzyHRT algorithm. Empirical likelihoods in a Bayesian setting are also considered for probabilistic reasoning behind the identification of anomalous cells. NASS survey data for livestock and major crop yield (such as corn) are considered for comparing the performances of the two proposed approaches with recent cellwise outlier methods.

Small Area Estimation of Household Economic Indicators under Unit-Level Generalized Additive Models for Location, Scale and Shape

Abstract We propose a small area estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS) for the estimation of household economic indicators. SAE-GAMLSS relax the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate the MSE is proposed. The SAE-GAMLSS estimator shows a largely better performance than the well-known Empirical Best Linear Unbiased Predictor (EBLUP) under various simulated scenarios. Per-capita consumption of Italian and foreign households in Italian regions, in urban and rural areas, is estimated using SAE-GAMLSS. Results show that the well-known Italian North–South divide does not hold for foreigners.

Confidence Interval Estimating the Mean of Normal Distribution and Skewed Distribution

The confidence interval is an important statistical estimator of population location and dispersion parameters. The purpose of this paper is to comprehend CI utilising various techniques. This includes classical CI, percentile bootstrap method, bootstrap-t and proposed bootstrap-t decile mean method. Distributions that are skewed and normal are used to generate data. The efficiency of the proposed method is evaluated on the basis of an extensive simulation study. The simulation findings show that the performance of the Student-t and three bootstrap approaches varies dramatically depending on sample size and skewness type. The coverage probability and length of the proposed confidence interval are compared with certain existing and widely used confidence intervals. For illustrative purposes, two real-life data sets are analysed, which, to some extent, support the simulation study conclusions. This paper's findings will be useful to a variety of researchers with practical experience in the fields of science and social sciences.

Testing Similarity of Parametric Competing Risks Models for Identifying Potentially Similar Pathways in Healthcare.

The identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transition intensities may be specified by a variety of parametric distributions, thus in particular being possibly time-dependent. We assess the similarity between two such models by examining the transitions between different health states. This research introduces a method to measure the maximum differences in transition intensities over time, leading to the development of a test procedure for assessing similarity. We propose a parametric bootstrap approach for this purpose and provide a proof to confirm the validity of this procedure. The performance of our proposed method is evaluated through a simulation study, considering a range of sample sizes, differing amounts of censoring, and various thresholds for similarity. Finally, we demonstrate the practical application of our approach with a case study from urological clinical routine practice, which inspired this research.

Striking the Balance: How Workplace Flexibility Enhances Engagement and Performance of Employees working in Public and Private settings

This study investigates the relationships between workplace flexibility, employee engagement, and performance of employees working in public and private settings. The purpose is to understand how flexible work arrangements influence employee engagement and overall performance across various industries. This research was conducted using a descriptive quantitative method. A structured questionnaire was used to collect data from 400 employees across finance, technology, healthcare, and manufacturing industries. The measurement model’s reliability was evaluated using the partial least square (PLS) structural equation modeling (SEM) approach (see table 3). Cronbach alpha (α) and composite reliability (CR) values were used. A prognosis model evaluation shows that all of the variables have SRMR values of 0.082 and NFI values of 0.914, suggesting that the model accurately matches with the experimental data. The Q2 predict value for employee performance (EP) of 0.352 shows excellent predictive relevance with a significant impact size. In comparison, the Q2 predict predictive value for Employee Engagement (EENG) is 0.185, indicating good predictive relevance with a modest impact size. These Q2Predict values are higher than the required minimum threshold of 0.00, indicating that the model is highly predictive for each variable. Furthermore, the Q2 effect sizes for EENG and EPC show that they have a significant impact on the endogenous variable (WPFX). SmartPLS 3.3.9 used the PLS algorithm as well as the bootstrapping approach to evaluate the presented hypothesis. Table 6 shows the beta values that demonstrate the intensity and significance of the positive link between the dependent and independent variables, where employee engagement acts as both a moderator and mediator. By including both mediation and moderation in the same model, you can gain a more comprehensive understanding of how workplace flexibility impacts employee performance, considering the mediating role of employee engagement and the moderating effect of its level. In addition to the above analysis, In addition, demographic factors are tested by Manova.

Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics

Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics

The dimensional structure of the Positive and Negative Syndrome Scale in first-episode schizophrenia spectrum disorders: an Exploratory Graph Analysis from the OPTiMiSE trial

The Positive and Negative Syndrome Scale (PANSS) is the most widely used rating scale to assess psychotic symptoms in patients with schizophrenia and other primary psychoses. However, a definitive consensus regarding its dimensional structure remains elusive. The present work aims to determine the number of dimensions of the scale through a network analysis approach in a sample of individuals experiencing first-episode schizophrenia spectrum disorder (FE-SSD) with minimal or no prior exposure to antipsychotic treatment. Baseline data of 446 participants (age 25.96 ± 5.99 years, 70% males) enrolled in the OPTiMiSE trial were analysed. Exploratory Graph Analysis (EGA) was conducted to evaluate the dimensionality of the PANSS, and a bootstrap approach (bootEGA) was employed to assess model stability. The analysis was replicated, excluding unstable items with stability values below 0.75, until a stable model was achieved. The analysis of the 30 items of the PANSS revealed inadequate structural consistency, resulting in the exclusion of 9 unstable items. The final model comprised 21 symptoms distributed across four communities (Positive, Cognitive/Disorganised, Excited/Aggressive and Negative) but lacked a depressive domain. In conclusion, we propose a concise version of the PANSS, incorporating 21 items, to better assess the core symptoms of the first episode of SSD. This revised version provides clinicians with a robust psychometric tool with reduced administration time, but the complementary administration of a dedicated instrument for evaluating affective symptoms is advisable.

World energy futures market efficiency and its determinants; evidence from white noise test based on block-wise wild bootstrap approach

ABSTRACT The study examines the time-varying efficiency of nine energy futures traded across different exchanges with varied trade settlement methods and pricing currency using the novel white noise test based on block-wsie wild bootstrap approach. The advantage of using this approach is that it relaxes the assumption of serial independence and the martingale difference sequence. Time-varying efficiency is observed for all nine energy futures consistent with the Adoptive Market Hypothesis (AMH). The three most (least) efficient energy futures based on efficiency ratio are Coal, Gasoline and NaturalGas (WTI, Brent and OmanCrude). Quantile regression analysis shows that the higher the level of illiquidity furthers the level of efficiency towards inefficiency (high level of efficiency) in the case of a less (more) efficient market. A mixed impact of volatility is observed across energy futures. No differential impact is found due to exchange, method of settlement and pricing currency. The robustness of the results is investigated, policy implications are discussed, and further research is recommended.

Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach

ABSTRACTThis paper proposes a novel hybrid multiscale decomposition and bootstrap approach for carbon price interval forecasting, aiming to overcome the limitations of traditional carbon price point forecasting. The original carbon price is decomposed into simple modes using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and various bootstrap methods are applied to perform a random sampling with a replacement on each mode, generating pseudo datasets forecasted using extreme gradient boosting (XGB). The forecasting values of all modes are then integrated into the original carbon price interval forecasting values. The empirical results, based on samples from China's Guangdong and Hubei carbon markets, provide compelling evidence of the effectiveness of our model. It achieves higher forecasting accuracy, higher interval coverage, and narrower forecasting intervals than currently popular prediction models, instilling confidence in its potential to enhance carbon price forecasting.

Simultaneous Confidence Intervals for Signal Detection and Ascertaining Precision of Adverse Event Rates in Clinical Trials

The marketing authorization of a medicinal product is contingent upon demonstration of safety and efficacy in support of the product’s labeled conditions of use. To demonstrate safety, one group of adverse events that requires detailed consideration is common adverse reactions (ARs). ARs and their frequency are reported in prescription drug labeling in the US and EU. The determination of these adverse reactions generally takes a simple approach—usually, inclusion is through the frequency of reporting and whether the adverse event (AE) rate for the drug exceeds the placebo rate. This standard method does not account for confounders or multiplicity. To overcome these limitations, we propose a Monte Carlo approach to detect drug safety signals in clinical trials. We fit regression models incorporating covariates to assess the drug effect on the rate of AEs. Adjustment for multiplicity is carried out through the construction of simultaneous confidence intervals accounting for arbitrary correlations. A computationally efficient multiplier bootstrap approach using the Rademacher sequences is developed to generate random samples from the joint distribution of the estimators for all the AE rates. Compared to Bonferroni-based methods, the proposed method leads to narrower simultaneous confidence intervals and is more powerful in detecting potential safety signals.

Exploring the impact of team engagement on patient satisfaction: insights from social support and transactive memory system

PurposeResearch on the impact of the engagement of online medical teams (OMTs) on patient evaluation, for example, satisfaction, remains insufficient. This study attempts to recognize the underlying mechanism of how OMTs’ engagement influences patient satisfaction by adopting social support as the mediator. This study also scrutinizes the moderating effects of the transactive memory system (TMS) on the link between OMTs’ engagement and social support.Design/methodology/approachWe utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5,000 samples was employed to test the mediation effect.FindingsOur results reveal that OMTs’ engagement improves informational and emotional support, thereby promoting patient satisfaction. Specialization and credibility strengthen the impact of OMTs’ engagement on informational and emotional support. Simultaneously, coordination has an insignificant influence on the link between OMTs’ engagement and social support.Originality/valueThis study contributes to the literature on OMTs, social support, and TMS, providing insights into patients’ perceptions of OMTs’ engagement during online team consultation. This study also generates several implications for the practice of online health communities and OMTs.

Typologies of Minority Stressors and Depressive Symptoms among LGBTQ Employees in the Workplace: A Moderated Mediation Model of Workplace Climate and Resilience

Abstract Introduction The pathways between minority stressors and depressive symptoms require a nuanced understanding of how these stressors affect the mental health of sexual and gender minority people. It remains unclear whether minority stressors are associated with depressive symptoms through perception of workplace climate and if resilience moderates these effects. Methods This study examined the direct and indirect effects of different minority stressors (experiences of discrimination, internalized homophobia, expectations of rejection, and concealment of identity) on depressive symptoms through the mediating factor of perception of workplace climate and the moderating factor of resilience. Chinese lesbian, gay, bisexual, trans, and queer (LGBTQ) employees (N = 706) completed a cross-sectional online survey in 2021–2022. We examined the moderated mediating effects using PROCESS macro with a bootstrapping approach. Results Mediation results showed that the four minority stressors were each negatively associated with perceptions of workplace climate, which were associated with higher levels of depressive symptoms among Chinese LGBTQ employees. Moderated mediation models demonstrated that the conditional indirect effects of different types of minority stressors on depressive symptoms through perception of workplace climate were only significant for those with lower levels of resilience. Conclusions Extending the minority stress model to work settings, this study offers new knowledge about the mediating role of perception of workplace climate in the relationship between minority stressors and depressive symptoms. Knowledge about the moderating role of resilience also informs targeted interventions. Policy Implications Interventions targeted at reducing minority stressors, enhancing perceived workplace climate, and promoting resilience are vital to enhance LGBTQ individuals’ mental health.