Bootstrap Technique Research Articles

Inferences of accelerated generalized type-I hybrid censoring data under power hazard rate population

In reliability engineering or medical studies, more information about the life expectancy of products or materials is needed. This information under use conditions is more difficult to collect especially under highly reliable case. In this paper, the problem of statistical inference when the lifetime of a product or material has the power failure rate distribution is developed. To save the cost and time induced by units the experiment is designed with respect to partially step-stress accelerated life tests (ALTs) with type-I generalized hybrid censoring scheme (GHCS). The point estimate of the model parameters as well as the accelerated factor are obtained with respect to the maximum likelihood (ML) and Bayes methods. Also, the asymptotic normal properties of ML estimates, two bootstrap techniques and Markov Chain Monte Carlo techniques are used to formulate interval estimators. A real lifetime data set is used to illustrate the application of the proposed model. Finally, a Monte Carlo simulation study is constructed to discuss and assess, the proposed model and estimation methods.

Reliability Assessment for Dual Constant‐Stress Accelerated Life Test Based on Weibull Distribution With Type‐II Censoring

ABSTRACTAccelerated life test (ALT) is a generally feasible and effective approach for evaluating the reliability of highly reliable products at normal operating conditions. This paper considers the reliability assessment for a dual constant‐stress ALT with Type‐II censoring for the Weibull distribution with constant shape parameter and a log‐linear link between the scale parameter and the stress factors. We derive the maximum likelihood estimates, weighted least squares estimates using a certain random variable transformation, and a combined maximum likelihood‐least squares estimates for the unknown parameters. In addition, we construct confidence intervals for the parameters of interest using both asymptotic theory and parametric bootstrap technique. Finally, simulation studies and an illustrative example are presented to demonstrate the effectiveness and feasibility of the proposed methods.

A robust diagnostic approach in mean shifts for multivariate statistical process control

The control chart is very useful and efficient to detect abnormal changes of a process, however it is difficult to identify out-of-control variables that are responsible for the change. Accurate fault diagnosis has become increasingly important, which helps engineer quickly eliminate the root causes of abnormal changes. However, most of existing diagnostic methods were developed under the assumption of normality, of which the performance is affected by the nonnormality. To overcome these limitations, this paper attempts to combine Bootstrap technique to propose a diagnosis method that is robust to underlying distribution, but also applicable to the case of small out-of-control sample size. Bootstrap technique is used to improve the diagnostic ability of the diagnostic method, which is our first attempt. Compared with the existing procedures, numerical simulations favour the proposed procedure. Finally, an example from a bolt production process is provided to illustrate the implementation of the proposed procedure.

Causal mediation analysis: selection with asymptotically valid inference

Abstract Researchers are often interested in learning not only the effect of treatments on outcomes, but also the mechanisms that transmit these effects. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In practice, these assumptions may not hold. We propose a method that considers the confounding functions as nuisance parameters to be estimated using data-adaptive methods. We then use a novel regularization method applied to this objective function to identify a set of important mediators. We consider natural direct and indirect effects as our target parameters. We then proceed to derive the asymptotic properties of our estimators and establish the oracle property under specific assumptions. Asymptotic results are also presented in a local setting, which contrast the proposal with the standard adaptive lasso. We also propose a perturbation bootstrap technique to provide asymptotically valid postselection inference for the mediated effects of interest. The performance of these methods will be discussed and demonstrated through simulation studies.

Clinical Prediction Score of 1-Year Sustained Remission to Methotrexate Combination Therapy in Patients with Early Rheumatoid Arthritis

Objective: To develop a prediction stratification score for the prediction of sustained remission (SR) to methotrexate (MTX) therapy in patients with early rheumatoid arthritis (ERA). Materials and Methods: Prognostic research with clinical prediction score was conducted. All patients with ERA who experienced moderate to high disease activity at a tertiary hospital were included. Multivariable logistic regression models were used to assess each variable predictor. Logistic coefficients of each predictor were used for generating scores. Predictive performance was internally validated using bootstrapping techniques. Results: One hundred seventy-five patients, of which 93 had SR, were included. The prediction score had four final predictors, age less than 65 years (0, 2), initial health assessment questionnaires less than 1 (0, 3), baseline disease activity score in 28 joints-erythrocyte sedimentation rate (DAS28-ESR) (0, 1), and DAS28-ESR at three months less than 3.2 (0, 5) were included in the logistic model. The positive predictive value for the high-risk score, which was greater than 3.5, was 87 (95% CI 76.7 to 93.9) results used in the model as a clinical predictor of MTX response. The SR score had a good predictive performance (AUROC 0.85, 95% CI 0.80 to 0.90). Conclusion: The SR score demonstrated good performance and discrimination for predicting SR to MTX combination therapy in patients with ERA for a safe long term follow up using simplified parameters in a clinical setting.

Correlators of N=4 Supersymmetric Yang-Mills Theory on Real Projective Space at Strong Coupling.

We consider type IIB supergravity on a Z_{2} quotient of AdS_{5}×S^{5} as the holographic dual of strongly coupled 4D N=4 SYM on RP^{4} space with the gauging of charge conjugation. Using bootstrap techniques, we determine all two-point functions of 1/2-BPS operators of arbitrary weights at the leading order in the large central charge expansion.

The effect of foreign language enjoyment on willingness to communicate among Chinese EFL students: conscientiousness as a mediator

IntroductionAn increasing body of research has explored the predictive effect of personality traits and affective factors on EFL learners’ willingness to communicate (WTC). However, more is needed to know about how the sub-facets of individual personality traits influence WTC in the classroom context. Therefore, drawing on positive psychology, this study aims to bridge this gap by examining these roles in a mediation model of WTC incorporating FLE and conscientiousness.MethodsTo this end, 244 Chinese EFL undergraduates from two distinct universities participated in an online survey, completing a composite questionnaire of the three constructs. The bootstrapping technique was employed to test the proposed relationships.ResultsThe findings indicated a positive correlation between both FLE and conscientiousness with WTC. Additionally, conscientiousness significantly mediated the relationship between FLE and WTC, supporting a partial mediation effect. Also, FLE had a direct influence on conscientiousness. These results may have some notable implications for EFL educators.DiscussionThe adoption of a holistic approach that emphasizes affective factors alongside acknowledging individual differences among learners could enhance students’ willingness to communicate in English.

Relationship Between Physical Activity and Subjective Well-Being Among College Students: The Mediating Role of Social Support.

Subjective well-being is a key concept in positive psychology and is associated with a variety of physical and mental health outcomes. Physical activity (PA) is a modifiable health behavior that has been identified as a potential correlate of subjective well-being. However, the underlying mechanisms that connect PA to subjective well-being are not well-understood. This study aimed to examine the association between the PA and various components of subjective well-being in college students, and to determine if social support mediated this association. A total of 1158 college students (46.3% female, mean age = 19.75years) voluntarily completed a set of questionnaires measuring PA, life satisfaction, positive affect, negative affect, and social support in a single-wave assessment. Three sets of mediation analyses were conducted (employing bootstrapping techniques with 10,000 samples), with life satisfaction, positive affect, and negative affect serving as the dependent variables, PA as the independent variable, and social support as the mediator. The findings revealed that the relationships between PA, subjective well-being, and social support differ by the component of subjective well-being. While no association was detected between PA and negative affect, increased PA led to enhanced social support, which in turn contributed to higher levels of life satisfaction and positive affect. As social support was found to mediate the relationship between PA and subjective well-being, future interventions are suggested to foster supportive social environments and develop effective strategies to modify the social experience of PA to maximize their benefits in enhancing subjective well-being.

Brand personality and purchase intention: The dual-mediating effects of brand attachment and nostalgia proneness

We examined the roles of brand attachment and nostalgia proneness in the relationship between the brand personality and purchase intention. Participants were 352 Chinese consumers (during the collection of the questionnaires, age ranges were set, but specific age numbers were not obtained; thus no calculation of standard deviation and mean was made in this study, as age itself has no impact on the research results). Participants completed measures of brand personality, brand attachment, nostalgia proneness, and purchase intention. Following path analysis and bootstrapping techniques, we found a significant positive correlation between purchase intention and brand personality. Moreover, brand attachment and nostalgia proneness functioned as dual mediators in this relationship. These results corroborate the theory that enhancing brand personality strengthens consumers’ emotional attachment and evokes nostalgia, thereby increasing brand purchase intention.

Conditional parametric bootstrap in GLARMA models

Due to situations where asymptotic results are not valid or when we do not know the true distributions of the data set, the bootstrap technique stands out as an effective alternative to make inferences about model parameters. In this context, we introduce the conditional parametric bootstrap, applied to time series data, specifically using the Generalized Linear Autoregressive Moving Average (GLARMA) model. Our interest is to use the procedure to correct possible biases and construct confidence intervals and hypothesis testing for the parameters of the GLARMA model. We conducted simulation studies to evaluate the performance of the procedure in finite samples. The results show that the proposed technique is promising and can be applied in practical situations. An example is the analysis of the monthly number of chronic obstructive pulmonary disease (COPD) cases, which illustrates the potential of the proposed approach in a real situation.

Research on gut microbiota characteristics of PBC patients at different ALBI grades based on machine learning.

The Albumin-Bilirubin (ALBI) score and grade are widely used to stratify patients with primary biliary cholangitis (PBC) into different disease statuses and risk levels. Recent studies have increasingly highlighted the role of gut microbiota in autoimmune liver diseases. This study aimed to investigate the differences in gut microbiota among PBC patients with varying ALBI grades. Clinical data and stool samples were collected from outpatient and inpatient PBC patients between 2019 and 2022. Gut microbiota profiles were obtained using 16S rDNA sequencing of stool samples. We analyzed alpha diversity, beta diversity, LEfSe analysis and pathway function prediction. Additionally, various machine learning methods-including random forest (RF), lasso, gradient boosting machine (GBM) and support vector machine (SVM)-were employed to identify key features and to build and validate predictive models using bootstrap techniques. Clinical characteristics of ALBI grade 1 patients were comparatively better than those of ALBI grade 2 and 3 patients, including multiple laboratory indices. Gut microbiota analysis revealed that species richness and balance were higher in ALBI grade 1 patients. Both the comparison of the most abundant genera and the linear discriminant analysis (LDA) in LEfSe demonstrated that Lachnospira had a higher abundance and better discriminative ability in ALBI grade 1. Pathway function prediction indicated that sulfur metabolism was upregulated in higher ALBI grades. Furthermore, RF identified 10 specific genera, which were then used to build and validate models for discriminating PBC patients according to their ALBI grades. All three models, developed using different machine learning methods, demonstrated good discrimination ability (mean AUC 0.75-0.80). This study highlights significant differences in gut microbiota profiles among PBC patients with different ALBI grades. The increased abundance of Lachnospira and upregulation of sulfur metabolism pathways are notable in patients with lower ALBI grades. The machine learning models developed based on gut microbiota features offer promising tools for discriminating between PBC patients with varying disease severities, which could enhance the precision of treatment strategies.

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.

The Influence of Job Satisfaction and Employee Engagement on Turnover Intention at PT. Dayamitra Telekomunikasi, Tbk. (Study of Mitratel Employees)

The rapid advancement of business in the seamless technology era has disrupted nearly every industry. Amidst this disruption, PT. Dayamitra Telekomunikasi (also known as/abbreviated as “Mitratel”) has encountered challenges, particularly an increasing rate of employee turnover. This turnover is thought to be driven by employee dissatisfaction with various internal and external company factors, along with low engagement levels. Previous research suggests that poor job satisfaction often results in higher turnover intentions, whereas strong employee engagement can help reduce turnover. This study explores the relationship between job satisfaction, employee engagement, and turnover intention at PT Dayamitra Telekomunikasi. The study employed a questionnaire based on an ordinal Likert scale, with data analyzed using the Partial Least Square (PLS) model, which doesn’t require a normal distribution and uses bootstrap techniques to examine correlations between latent variables. The results show that job satisfaction significantly impacts turnover intention, with 76.1% of job satisfaction indicators negatively influencing turnover intentions. However, employee engagement has minimal influence, with only 3.2% of engagement indicators affecting turnover intention. Therefore, while higher job satisfaction reduces turnover, employee engagement does not substantially affect turnover intentions. In summary, the study confirms that job satisfaction is crucial in lowering turnover intention, consistent with previous research, while employee engagement has a negligible effect on turnover rates.

A Comparative Analysis of Advanced Machine Learning Models for the Prediction of Combustion, Emission and Performance Characteristics Using Endoscopic Combustion Flame Image of a Pine Oil–Gasoline Fuelled Spark Ignition Engine

This research focuses on using machine learning to predict the spark ignition engine’s combustion, performance, and emission parameters with bio-fuel blends such as pine oil blend, which significantly diminishes the environmental impact of traditional fuels, reduces the limitations of repeated engine experimentation and addresses the nonlinearities in engine test results contributing to sustainable cleaner fuel and energy solutions. The models used were Ensemble Decision Tree Bagging, Ensemble Least Squares Boosting, Gaussian Process Regression and Support Vector Machine Regression, with good generalization ability. Brake Specific Fuel Consumption data from the test engine trials and endoscopic image flame area data after spark timing at different crank angles (320, 400, 480, 560, and 640 after Spark Timing) were fed into the machine-learning models as predictors. The response variables were Brake thermal efficiency, Unburnt Hydrocarbons, Carbon monoxide, Carbon dioxide, Oxides of nitrogen, maximum In-cylinder pressure, and maximum heat release rate. The bootstrap technique was used to generate numerous datasets from the experimental data for data-driven model training and tested using both interpolative and extrapolative data. The experimental and predicted values for all these algorithms were subjected to repeated hyperparameter optimization trials and the best machine learning method was identified using the performance and error metrics. The Ensemble Least Squares Boost model showed the overall best correlation (R2) in the range of 0.97 - 0.99 for gasoline and pine oil PN20 blend for the predicted versus experimental engine parameters. The root-mean-squared error (RMSE) at maximum load ranged between 0.0086 - 0.3044 for gasoline and 0.0049 - 0.2046 for the Pine oil PN20 fuel blend respectively. Therefore, employing an Ensemble Least Squares Boosting machine learning framework can effectively predict the characteristics of gasoline engines using pine oil and blends. This approach serves as a virtual engine model, efficiently overcoming the limitations and complexities inherent in conventional engine experiments.

Mediating effect of self-efficacy on the relationship between Big Five personality and medication adherence in Chinese hypertensive patients: a national cross-sectional study.

The study aims to evaluate personality characteristics, self-efficacy, and medication adherence in Chinese patients with hypertension, while also investigating how self-efficacy mediates the relationship between personality traits and medication adherence. This investigation included 787 Chinese patients diagnosed with hypertension, as reported in the "2021 China Family Health Index Survey Report." The study employed several assessment tools such as a General Information Questionnaire, the Brief Big Five Inventory (BFI-10), the New General Self-Efficacy Scale (NGSES), and the Medication Adherence Rating Scale (MARS). Statistical analyses encompassed the Mann-Whitney U test, chi-square test, multiple logistic regression, Spearman's rank correlation, standardized regression coefficients, and Bootstrap techniques. (1) Individuals with debt, who also possess high levels of self-efficacy, tend to adhere more consistently to their medication regimens. (2) An analysis of personality traits indicated that Extroversion,Conscientiousness and Openness positively influences self-efficacy,while Agreeableness and Neuroticism negatively impacts it. (3) Self-efficacy plays a mediating role in the indirect relationship between personality traits such as Extroversion and neuroticism and medication adherence. In the context of Chinese adults, both self-efficacy and Extroversion positively influence medication adherence, whereas high levels of neuroticism adversely impact it. Furthermore, self-efficacy serves as a mediating factor in the linkage between personality traits and medication adherence.

Effects of Financial Bootstrapping Techniques on Operational Performance of Small and Medium-Sized Enterprises (SMEs) in Lagos State

Accessing credit from the conventional banks has been one of the major challenges of Small and Medium-Sized Enterprises (SMEs) in achieving their goals and objectives. This has made SMEs not able to achieve their operational objectives from time to time, thus affecting overall performance of the business. The SMEs operators are forced to design methods which funds could be raised to finance the operational activities. The study investigated the effects of financial bootstrapping techniques on the performance of SMEs in Lagos State. Owner-related finance, delaying payments and customer-related finance were adopted as the financial bootstrapping techniques while operational performance is viewed from the angle of business operations. The study employed survey research design with simple random sampling method. Questionnaire was used as a research instrument for primary source of data collection. 226 copies of questionnaires were administered to SMEs operators in Lagos State, but 219 copies were returned. The findings of the regression analysis revealed that owner related finance and customer-related finance have significant effect on operational performance of SMEs in Lagos State. Delaying payments has no significant effect on operational performance of SMEs in Lagos State. The study recommended that SME operators focus more on owner-related and customer-related financing techniques, and that they consider utilising delaying payment techniques.

Reliability analysis of load sharing systems under unequal load-sharing rule with applications

We are examining a setup consisting of several parallel-connected k-out-of-m load sharing systems. The load sharing is modeled using proportional conditional failure rates for component failures and load sharing under the unequal load-sharing rule. We have derived the system reliability for the entire setup. To illustrate this, we examine two cases: one with n systems connected in parallel, each following a 1-out-of-2 configuration, and the other with n systems connected in parallel, each following a 2-out-of-4 configuration. The system components are assumed to be identical with an exponential as well as Weibull baseline distributions. We propose two methods to estimate the baseline parameter and load-sharing parameters. Additionally, we present a confidence interval using bootstrapping techniques, specifically the percentile bootstrap (boot-p). We assess the performance of these parameter estimation and interval estimation methods through simulation studies. Furthermore, we evaluate the practical applicability of the proposed techniques by analyzing two real datasets. The same approach can be applied to a system that includes multiple k-out-of-m load sharing systems, where these systems are connected in either a series or a p-out-of-q configuration.

Boosting tree with bootstrap technique for pre-stress design in cable dome structures

Tensegrity structures, known for their rigidity derived from feasible pre-stresses, present unique challenges in structural engineering. Traditional force-finding methods, though comprehensive, rely heavily on intricate matrix computations, making them computationally intensive and often uncomfortable for considering external loads in practical engineering scenarios. This paper introduces a novel approach to compute pre-stresses in cable dome structures by integrating machine learning and probability theory, collectively termed the boosting tree with bootstrap technique (BTWBT). This method reduces the sample size to as few as 100 per iteration, while improving computational efficiency by randomly generating internal forces. By reframing the force determination as an inverse problem, it ensures that structural displacement converges to zero under feasible pre-stresses. The effectiveness of BTWBT is demonstrated across three distinct cable dome structures: the Geiger dome, Kiewitt dome, and rotating hyperboloid cable dome. Results show that BTWBT achieves the preset displacement requirement (maximum nodal displacement below 0.01 mm) with fewer iterations and reduced computational cost compared to traditional machine learning methods. BTWBT's capability to manage complex structural configurations with minimal data, while incorporating random internal force generation ranges, highlights its potential as a superior alternative for force determination in tensegrity structures.

Predicting Admission to Neonatal Care Unit at Mid-Pregnancy and Delivery Using Data from a General Obstetric Population.

Development and validation of risk prediction models at mid-pregnancy and delivery to predict admission to the neonatal care unit. We used data from all singleton deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019. Admission to the neonatal care unit was assumed if length of stay in the unit was > 24h. Multivariable logistic regression with backward stepwise selection was used to develop the models. Discrimination was assessed using the ROC curve C-statistic, and internal validation was assessed using bootstrapping techniques. We conducted temporal external validation using data from all singleton deliveries at CUMH during 2020. Out of 6,077 women, 5,809 (95.6%) with complete data were included in the analyses. A total of 612 infants (10.54%) were admitted to the neonatal care unit for > 24 hours. Six variables were informative at mid-pregnancy: male infants, maternal smoking, advancing maternal age, maternal overweight/obesity, nulliparity and history of gestational diabetes (C-statistic: 0.600, 95% CI: 0.567, 0.614). Seven variables were informative at delivery: male infants, nulliparity, public antenatal care, gestational age < 39 weeks', non-spontaneous vaginal delivery, premature rupture of membranes and time of birth between 17:01-07.59h (C-statistic: 0.738, 95% CI: 0.715, 0.760). Using these predictors, we developed nomograms to calculate individualised risk of neonatal care unit admission. Bootstrapping indicated good internal performance and external validation suggested good reproducibility. Our nomograms allow the user to quickly estimate individualised risk of neonatal care unit admission. Future research should aim to improve accuracy in early pregnancy to better assist counselling of parents.

Bootstrapping conformal defect operators on a line

We study a conformal field theory with cubic anisotropic symmetry in presence of a line defect. We compute the correlators of the low lying defect operators using Feynman diagrams and derive explicit expressions for the two, three and four point defect correlators at the cubic fixed point in 4 – ϵ dimensions to O(ϵ). We also compute the defect g-function for this setup and demonstrate that this is in agreement with the g-theorem, which states that the g-function is monotonic under the renormalisation group flow along the defect. Next, we focus on conformal bootstrap techniques to determine the CFT data associated with the defect operators, which is the main objective of the paper. We utilize the framework of crossing symmetric Polyakov bootstrap and compute the averaged CFT data to O(ϵ) up to a finite number of ambiguities. We comment on unmixing the CFT data for the double trace operators at O(ϵ) and use this to compute the O(ϵ2) data. Finally, we study these defect correlators non-perturbatively using numerical methods and isolate them near the free theory limit close to four dimensions.