Bootstrap Confidence Intervals Research Articles

Takeaway food consumption and depressive symptoms in Chinese university students: mediating effects of physical activity

BackgroundThe consumption of takeaways is becoming increasingly prevalent. Despite this, the relationship between takeaway food consumption and depressive symptoms in Chinese populations has not been clarified. Furthermore, the factors that mediate the association between takeaway frequency and depressive symptoms are unknown.MethodsQuestionnaires were employed to collect data from 6,417 new students at Chongqing Medical University in the autumn of 2023, including sociodemographic information, takeaway frequency, physical activity levels (measured by the International Physical Activity Questionnaire Short Form), and depressive symptoms (measured by the Patient Health Questionnaire-9). Multiple linear regression and mediation analysis were performed. Multiple imputations were used to fill in missing data through sensitivity analyses.ResultsAmong 6417 participants, 2,606 (40.6%) students ordered takeaway at least once a week, with 235 (3.7%) of them ordering takeaway food every day. Takeaway frequency was significantly associated with depressive symptoms (β=0.034, P=0.006), and physical activity partially mediated this relationship (95% bootstrap confidence interval=0.0024, 0.0371).ConclusionsThe study highlights the negative relationship between takeaway frequency and emotional well-being, emphasizing the need to focus on the emotional health of frequent takeaway food consumers. Moreover, our study suggests that increased physical activity may alleviate takeaway-induced mood-related outcomes.

Estimating percentiles of time-to-failure distribution obtained from a Weibull accelerated degradation model

We propose a nonparametric kernel estimation method to estimate the percentiles of the time-to-failure distribution under the usual use condition obtained from a Weibull accelerated degradation model. We discuss some of the well-known parametric method that used to estimate the time-to-failure distribution and its percentile under the usual use condition including ordinary least square method and maximum likelihood method. The different exiting methods were compared with the kernel method through simulation by using the mean square error and the bootstrap confidence interval length. In general, when the distributional assumption is available, the maximum likelihood estimator performs better than the other two estimators, while the kernel estimator performs better than the other two estimators when the distributional assumption is not available. Application to real data set was disscuced.

From prediction to explanation: Is the relationship between youth psychopathy traits and continued offending in adulthood mediated by social environment?

Studies that focus on whether psychopathy statistically predicts reoffending are not informative of the process that connects the putative cause (psychopathy) to the expected outcome (offending). Understanding the causal mechanisms responsible for the relationship between psychopathy and offending has received minimal empirical attention even though fourth-generation risk assessment protocols and treatment strategies regularly require a specific focus on psychopathy. Theory can help guide an improved understanding of the causal mechanisms underlying the relationship between psychopathy and offending. Cumulative disadvantage theories anticipate that the relationship between psychopathy and reoffending is mediated by a person's social environment. Propensity theories anticipate that psychopathy is a common cause of both a person's negative social environment and reoffending. These two theoretical perspectives were compared using longitudinal data covering the transition from adolescence to adulthood among 490 male and female participants from the Incarcerated Serious and Violent Young Offender Study. Psychopathy was measured in adolescence using the Psychopathy Checklist: Youth Version. Social environment was measured in adulthood using an informal social control scale from the Community Risk Needs Assessment. Conviction frequency was measured over a 3-year period after the Community Risk Needs Assessment rating. Bias-corrected bootstrapped confidence intervals indicated that informal social control partially mediated the relationship between Psychopathy Checklist: Youth Version scores and conviction frequency. The mediating effect was robust to unobserved confounders. Findings supported the philosophy of risk management and intervention strategies that target a person's social environment when aiming to reduce reoffending for persons with psychopathy traits. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Confidence Intervals for the Difference between Two Coefficients of Variation of the delta-Birnbaum-Saunders Distributions

The delta-Birnbaum-Saunders distribution is a relatively novel concept that combines the Birnbaum-Saunders with the binomial distributions. As a result, datasets containing both positive and zero values conform well with this distribution, making it particularly intriguing. Additionally, coefficients of variation are among the important statistics for comparing the dispersion of data. Therefore, there is an interest in proposing methods for constructing confidence intervals, which play a crucial role in statistical inference for the difference between two coefficients of variation in delta-Birnbaum-Saunders distributions. There are four methods proposed: the method of variance estimates recovery, the bootstrap confidence interval, the generalized confidence interval based on the variance stabilized transformation, and the generalized confidence interval based on the Wilson score method. All the methods are compared in terms of performance using coverage probability and average width through Monte Carlo simulations. The simulation results show that the bootstrap confidence interval performs similarly to the method of variance estimate recovery, except in cases where the shape parameter is large. In addition, it is shown that the generalized confidence interval based on variance stabilized transformation and the generalized confidence interval based on the Wilson score method yield similar results and demonstrate the highest efficiency compared to other methods. Finally, two datasets are used to illustrate the application of the proposed confidence intervals.

Comparison of two processes based on quantile-based process capability indices by using fiducial generalized confidence interval

Selection of the better of two suppliers or assessing whether a process has improved after taking some corrective actions are important tasks in the manufacturing industry. Process capability indices (PCIs) are often used in such situations. In this paper, we propose a method to compare the performance of two processes or two suppliers based on PCIs. Estimation of fiducial generalized confidence interval (FGCI) of the difference and ratio of two well-known quantile-based process capability index (PCI) of two processes are discussed when the underlying distribution is a location-scale distribution or transformable to a location-scale distribution such as log location-scale distribution. We discuss the case of four important distributions namely normal, lognormal, Weibull and gamma distributions. Fiducial quantities (FQ) are estimated using the maximum likelihood estimator (MLE). The performance of the estimated FGCI is compared with three non-parametric bootstrap confidence intervals (BCIs) namely, standard bootstrap (SB), percentile bootstrap (PB) and bias-corrected percentile bootstrap (BCPB) in terms of coverage probability (CP) and average width (AW) via Monte Carlo simulation. It is observed through simulation that our proposed method performs better even for small sample sizes. Two examples are given to illustrate the applicability of our proposed method.

Confidence Intervals for the Zeghdoudi Distribution Parameter: Applications in Precipitation and COVID-19 Data Analysis

This paper proposes four confidence intervals (CIs) for estimating the parameters of the Zeghdoudi distribution, which is commonly used in the analysis of lifetime data. We introduce and evaluate the likelihood-based, Wald-type, bootstrap-t, and bias-corrected and accelerated (BCa) bootstrap CIs using Monte Carlo simulation studies and apply them to two real datasets. We assess the effectiveness of these CIs by evaluating their empirical coverage probability (CP) and average length (AL), which offer valuable insights into their performance in various scenarios. Furthermore, we have developed an explicit formulation of the Wald-type’s CI formula, simplifying its computation. The results demonstrate that the CPs of likelihood-based and Wald-type CIs converge towards the nominal confidence level of 0.95 for all cases. However, when the sample size is small, the bootstrap-t and BCa bootstrap CIs have CPs less than 0.95. On the other hand, as sample sizes increase, the CPs of all CIs tend to approach 0.95. However, when the sample sizes are small, the CPs of the bootstrap-t and BCa bootstrap CIs tend to decrease. We verified the efficacy of CIs by applying them to precipitation data and COVID-19 mortality rate data, and the results matched those from the simulation study.

Medicaid‑Insured Children with Medical Complexity in a Rural State.

To describe children with medical complexity (CMC) in Montana according to their clinical characteristics, rurality and distance from specialty care, and to assess for disparities in geographic access to specialty care for American Indian children. In this cross-sectional study we categorized children in 2016-2021 Montana Medicaid claims data using the Pediatric Medical Complexity Algorithm and compared the associations of medical complexity and demographic traits using Chi-squared tests. Using a database of providers we calculated drive times from children's residences to the nearest pediatric subspecialist and calculated bootstrap confidence intervals for the difference in median driving distances by complexity and race. Among 126,873 children, 23% lived in rural areas and 20% were reported as American Indian. In all, 10,766 children (8.5%) had complex chronic conditions (CMC) and 27431 (21.6%) had non-complex chronic conditions. Medical complexity was associated with age, race, ethnicity, sex, CHIP enrollment, disability, and rurality. CMC had shorter median drive times to care than children with noncomplex medical conditions and children without chronic conditions (28 vs 34 and 43 minutes, 95% CI of differences 4-9 and 6-11). At each level of medical complexity, the median distance from care was greater for American Indian children than children of other races. Although children with medical complexity tend to live closer to specialists than other children, many CMC live far from subspecialty care. American Indian children live farther from specialists than other children, regardless of complexity. Future work should support access to care for rural and American Indian CMC.

Air pollution is linked to cognitive decline independent of hypersensitive C-reactive protein: insights from middle-aged and older Chinese

BackgroundLong-term air pollution exposure and inflammation are considered to be associated with cognitive decline. However, whether air pollution exposure related cognitive decline is dependent on inflammation remains uncertain.Materials and methodsThe present study collected data from China Health and Retirement Longitudinal Study (CHARLS) at baseline in 2011, with a follow up period in 2015. Concentration of air pollutants (particles with diameters ≤ 1.0 μm [PM1], ≤ 2.5 μm [PM2.5], ≤ 10 μm [PM10], nitrogen dioxide [NO2] and ozone [O3]) were obtained from China High Air Pollutants (CHAP) dataset. Hypersensitive C-reactive protein (hs-CRP), a systemic inflammation marker, was measured in blood of subjects and cognitive function was assessed by standardized questionnaire.ResultsA total of 6434 participants were included in the study. Lower exposure to PM2.5, PM1, PM10 and NO2 were associated with mitigated cognitive decline. The odds ratios (ORs) for air pollutants changes and cognitive decline and 95% confidence intervals (CIs) were as follows: PM2.5-0.934(0.925, 0.943), PM1- 0.945 (0.935,0.955), PM10-0.977(0.972,0.982) and NO2-0.962(0.950,0.975), respectively. Hs-CRP showed no significant correlation with cognitive decline or change in levels of air pollution. The interaction regression analyses, both unadjusted and adjusted, did not uncover any significant correlation between hs-CRP and air pollution with respect to cognitive decline. Bootstrap test exhibited no significant mediating effect of hs-CRP on the relationship between any air pollutants and cognitive decline, the indirect effects of hs-CRP in conjunction with exposure to different air pollutants were all found to be non-significant, with the following bootstrap CIs and p-values: PM2.5-1.000([1.000,1.000], P = 0.480),PM1-1.000([1.000,1.000], P = 0.230),PM10-1.000([1.000,1.000], P = 0.650), O3-1.000([1.000,1.000], P = 0.470), ΔNO2-1.000([1.000,1.000], P = 0.830) .ConclusionAmbient air pollution exposure was linked to cognitive decline independent of hs-CRP level.

Nurses who co-create care with clients experience lower levels of burnout through the perception of fewer emotional demands: an observational study

BackgroundClients are especially satisfied about treatment of nurses providing person-centered care. Such care benefits from the co-creation of care, in which nurses and clients together shape the care process by investing in effective communication and supportive nurse-client relationships. Co-creation of care can especially benefit longitudinal collaboration between clients and their nurses in long-term care settings. However, it is unknown how nurses in these settings perceive the co-creation of care to affect their job demands, and ultimately, their levels of job burnout. We studied whether nurses’ perceptions of job demands (workload and emotional demands) mediated the association between the co-creation of care and job burnout in long-term care settings.MethodsA web-based survey was completed by 256 nurses from two long-term care facilities in the Netherlands. Nurses were sent an email that included information about the study and a link to the web-based survey, consisting of items from validated questionnaires on the co-creation of care, job demands, and job burnout. Resulting data were analyzed using mediation analyses.ResultsThe co-creation of care was associated with lower levels of burnout through the perception of fewer emotional demands (ß = –0.07; 95% percentile bootstrap confidence interval (PBCI) –0.12 to –0.02). Workload was also associated with lower burnout levels, but workload did not mediate the association between the co-creation of care and job burnout (ß = –0.01; 95% PBCI –0.07 to 0.05).ConclusionsThe co-creation of care supports nurses’ perception of their work in long-term care as less emotionally demanding, which helps them to experience less burnout. Thus, long-term care facilities could consider the promotion of the co-creation of care in efforts to reduce emotional demands and job burnout among nurses. Ultimately, investing in the co-creation of care can facilitate well-being of nurses as well as facilitate their person-centeredness in caring for their clients.

Stress-Strength Reliability of Two-Parameter Exponential Distribution Based on Progres- sively Type-II Censored Data

Introduction: Stress-strength models has achieved considerable attention in recent years due to its applicability in various areas like engineering, quality control, biology, genetics, medicine etc. This paper investigates estimation of the stress-strength reliability parameter R=P(Y<X) in two-parameter exponential distributions under progressively type-II censored samples. Methods: The maximum likelihood and the best linear unbiased estimates of P(Y<X) are obtained, and the Bayes estimates of P(Y<X) are computed under the squared error, linear-exponential, and Stein loss functions. Also, confidence intervals of stress-strength reliability such as the bootstrap confidence intervals, highest posterior density credible interval, and confidence interval based on the generalized pivotal quantity are obtained. Results: Using a simulation study, the point estimators and confidence intervals are evaluated and compared. A set of real data is presented for better clarification of the issue. Conclusion: The results demonstrated that with increasing the sample size, in almost cases the estimated risk of all the estimators decrease. Also, in almost all cases the Bayes estimator under the linear-exponential loss function has smaller estimated risk than the other estimators. Based on our simulation, the expected lengths of all intervals tend to decrease when the sample size increases. Moreover, the highest posterior density confidence intervals are shorter than the others intervals for all the values of P(Y<X).

Estimation of a~New Asymmetry Based Process Capability Index 𝐶𝑐 for Gamma Distribution

Abstract In this work, we provide five different classical estimation methods for the gamma distribution and unknown parameters, which are used to estimate the newly constructed process capability index (PCI) C c C_{c} . Next, we use the aforementioned estimation techniques to consider five bootstrap confidence intervals: standard bootstrap (SB), percentile bootstrap (PB), student’s 𝑡 bootstrap (STB), bias-corrected percentile bootstrap (BCPB), and bias-corrected accelerated bootstrap (BCAB). These allow us to construct confidence intervals of the PCI C c C_{c} . Using the suggested techniques of estimate for small, moderate, and large sample sizes, Monte Carlo simulations are run to compare the performances of the bootstrap confidence intervals’ average widths and coverage probabilities. Simulation results indicate that MLE gives the least AW for all BCIs (SB, PB, BCPB, TB, and BCAB) when compared to all other estimation approaches evaluated in this paper. Further, simulation results demonstrate that, for almost all sample sizes and all of the examined estimation approaches, BCAB CI generates smaller AW and larger CP than their counterparts. Finally, for illustrative purposes, three real data sets are analysed.

Using cluster-based permutation tests to estimate MEG/EEG onsets: How bad is it?

Localising effects in space, time and other dimensions is a fundamental goal of magneto- and electroencephalography (EEG) research. A popular exploratory approach applies mass-univariate statistics followed by cluster-sum inferences, an effective way to correct for multiple comparisons while preserving high statistical power by pooling together neighbouring effects. Yet, these cluster-based methods have an important limitation: each cluster is associated with a unique p-value, such that there is no error control at individual timepoints, and one must be cautious about interpreting when and where effects start and end. Sassenhagen and Draschkow (2019) provided an important reminder of this limitation. They also reported results from a simulation, suggesting that onsets estimated from EEG data are both positively biased and very variable. However, the simulation lacked comparisons to other methods. Here, I report such comparisons in a new simulation, replicating the positive bias of the cluster-sum method, but also demonstrating that it performs relatively well, in terms of bias and variability, compared to other methods that provide pointwise p-values: two methods that control the false discovery rate and two methods that control the familywise error rate (cluster-depth and maximum statistic methods). I also present several strategies to reduce estimation bias, including group calibration, group comparison and using binary segmentation, a simple change point detection algorithm that outperformed mass-univariate methods in simulations. Finally, I demonstrate how to generate onset hierarchical bootstrap confidence intervals that integrate variability over trials and participants, a substantial improvement over standard group approaches that ignore measurement uncertainty.

A bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance

This paper introduces a nonparametric bootstrap method for estimating the causal effects of public policy under the circumstances of imperfect compliance and overlap. It focuses on business investment subsidies in Sardinia by comparing firms eligible for the 1999 subsidies to those not, amid issues of imperfect compliance and overlapping programs. Bootstrap confidence intervals (CI) are proposed for the average effect of treatment on the sub-population of compliers. The obtained CIs are consistent across nominal levels and robust against data nonnormality; they show coverages of credible intervals close to nominal, suggesting effectiveness for assessing causal effects. Compared to other methods, the results of the new combination of a specific estimator for incompliance and the bootstrap align with those of more modern approaches such as Bayesian Additive Regression Trees and Causal forest.

Bayesian and Non-Bayesian Inference to Bivariate Alpha Power Burr-XII Distribution with Engineering Application

In this research, we present a new distribution, which is the bivariate alpha power Burr-XII distribution, based on the alpha power Burr-XII distribution. We thoroughly examine the key features of our newly developed bivariate model. We introduce a new class of bivariate models, which are built with the copula function. The statistical properties of the proposed distribution, such as conditional distributions, conditional expectations, marginal distributions, moment-generating functions, and product moments were studied. This was accomplished with two datasets of real data that came from two distinct devices. We employed Bayesian, maximum likelihood estimation, and least squares estimation strategies to obtain estimated points and intervals. Additionally, we generated bootstrap confidence intervals and conducted numerical analyses using the Markov chain Monte Carlo method. Lastly, we compared this novel bivariate distribution’s performance to earlier bivariate models, to determine how well it fit the real data.

On the assessment of the ability of measurements, nowcasts, and forecasts to track changes

BackgroundMeasurements, nowcasts, or forecasts ideally should correctly reflect changes in the values of interest. In this article, we focus on how to assess the ability of measurements, nowcasts, or forecasts to correctly predict the direction of changes in values - which we refer to as the ability to track changes (ATC).MethodsWe review and develop visual techniques and quantitative measures to assess ATC. Extensions for noisy data and estimation uncertainty are implemented using bootstrap confidence intervals and exclusion areas.ResultsWe exemplarily illustrate the proposed methods to assess the ability to track changes for nowcasting during the COVID-19 pandemic, patient admissions to an emergency department, and non-invasive blood pressure measurements. The proposed methods effectively evaluate ATC across different applications.ConclusionsThe developed ATC assessment methods offer a comprehensive toolkit for evaluating the ATC of measurements, nowcasts, and forecasts. These techniques provide valuable insights into model performance, complementing traditional accuracy measures and enabling more informed decision-making in various fields, including public health, healthcare management, and medical diagnostics.

Prediction of electroconvulsive therapy outcome: A network analysis approach.

While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach. We combined individual patient data from two randomized controlled trials (total N = 161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD≤7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios. Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR = 0.75; bootstrapped confidence interval (bCI) = 0.44-1.00) whereas retardation (OR = 1.21; bCI = 1.00-2.02) and hypochondriasis (OR = 1.31; bCI = 1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found. By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.

Optimal Estimation of Reliability Parameters for Modified Frechet-Exponential Distribution Using Progressive Type-II Censored Samples with Mechanical and Medical Data

The aim of this research is to estimate the parameters of the modified Frechet-exponential (MFE) distribution using different methods when applied to progressive type-II censored samples. These methods include using the maximum likelihood technique and the Bayesian approach, which were used to determine the values of parameters in addition to calculating the reliability and failure functions at time t. The approximate confidence intervals (ACIs) and credible intervals (CRIs) are derived for these parameters. Two bootstrap techniques of parametric type are provided to compute the bootstrap confidence intervals. Both symmetric loss functions such as the squared error loss (SEL) and asymmetric loss functions such as the linear-exponential (LINEX) loss are used in the Bayesian method to obtain the estimates. The Markov Chain Monte Carlo (MCMC) technique is utilized in the Metropolis–Hasting sampler approach to obtain the unknown parameters using the Bayes approach. Two actual datasets are utilized to examine the various progressive schemes and different estimation methods considered in this paper. Additionally, a simulation study is performed to compare the schemes and estimation techniques.

Confidence intervals of difference and ratio between two means of delta-Birnbaum-Saunders distributions

During the dry season, the northern region of Thailand consistently grapples with compromised air quality, notably an excessive level of particulate matter with a diameter of less than 2.5 microns (PM2.5), which poses implications for public health. Wind speed emerges as the primary factor that diminishes the concentration of PM2.5, making the evaluation of wind speed changes crucial in understanding and potentially mitigating the impact of air pollution on public health in the region. The wind speed data contain zero and positive values. This can be estimated through the confidence intervals of the difference and ratio between two means of delta-Birnbaum-Saunders distributions using various methods, including the generalized confidence interval (GCI), bootstrap confidence interval (BCI), and generalized fiducial confidence interval (GFCI). These methods rely on variance stabilizing transformations (VST), the Wilson and Hannig methods. According to the simulation, for small sample sizes, the GCI based on the VST and Hannig was the most effective method in terms of coverage probabilities and average lengths. For medium sample sizes, it was found that the performance of GFCI based on the VST method was superior to other methods. Additionally, when confronted with a large sample size, the BCI based on the VST method becomes relevant and will be introduced. To demonstrate the efficacy of our proposed methods, we applied them to wind speed data gathered from Chiang Mai Airport station in Thailand from January to February in both 2022 and 2023.

Updated CP*Trends: An Online Tool to Compare Cohort and Period Trends across Cancer Sites.

CP*Trends is a widely used SEER website used to explore temporal effects of period and cohort on cancer incidence and mortality. It provides a graphical display of smoothed rates, and a C-P Score that helps to assess the magnitude of the effect of cohort and period. This update provides results for African Americans and Whites. The C-P Score has an intrinsic bias favoring cohort because there are many more cohorts than periods. An adjusted C-P Score removes some of this advantage. Bootstrap confidence intervals are given, which allow one to see the effects of different sample sizes on the model results. Finally, users may control window size used in the smoothing algorithm, which helps to avoid over smoothing or masking of trends. The method is illustrated using data on cervical cancer incidence trends for African Americans and Whites, 1975-2018. Rates are higher for African Americans, and both races have contributions for cohort. However, the period effect is only strongly evident in Whites. Visual inspection of White trends suggests possible differences for those older and younger than age 50. These methods are applied in an interactive website displaying incidence and mortality trends for over 20 cancer sites in the US.

On the Replicability of the Thermodynamic Modeling of Spectroscopic Titration Data in the Nickel(II) En System

ABSTRACTCharacterizing complicated solution phase systems in situ requires advanced modeling techniques to capture the intricate balances between the many chemical species. Due to the error inherent in any scientific measurement, a spectrophotometric titration experiment with nickel(II) and ethylenediamine (en) was repeated six times using an autotitrator to test the replicability of the data and the consistency of the resulting thermodynamic model. All six datasets could be modeled very tightly (R2 &gt; 99.9999%) with the following eight complexes: [Ni]2+, [Ni2en]4+, [Nien]2+, [Ni2en3]4+, [Nien2]2+, [Ni2en5]4+, [Nien3]2+, and [Nien6]2+. The logK values for the stepwise associative reactions agree with existing literature values for the majority species ([Nienn = 1–3]2+) and matched expectations for the minority species; 95% confidence intervals for each logK value were determined via bootstrapping, which quantifies the variability in the binding constant value that is supported by a given dataset. The repeated experiments, which could not be successfully concatenated together, demonstrate that replication is crucial to capturing all the variability in the logK values. Conversely, bootstrapped confidence intervals across multiple experiments can be readily combined to generate an appropriate range for an experimentally determined binding constant.