Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

The mediation of perceived risk’s impact on destination image and travel intention: An empirical study of Chengdu, China during COVID-19

Perceived risk clearly impacts travel behavior, including destination selection and satisfaction, but it is unclear how or why its effect is only significant in certain cases. This is because existing studies have undervalued the mediating factors of risk aversion, government initiatives, and media influence as well as the multiple forms or dimensions of risk that can mask its direct effect. This study constructs a structural equation model of perceived risk’s impact on destination image and travel intention for a more nuanced model of the perceived risk mechanism in tourism, based on 413 e-questionnaires regarding travel to Chengdu, China during the COVID-19 pandemic, using the Bootstrap method to analyze suppressing effect. It finds that while perceived risk has a significant negative impact on destination image and travel intention, this is complexly mediated so as to appear insignificant. Furthermore, different mediating factors and dimensions of perceived risk operate differently according to their varied combinations in actual circumstances. This study is significant because it provides a theoretical interpretation of tourism risk, elucidates the mechanisms or paths by which perceived risk affects travel intention, and expands a framework for research on destination image and travel intention into the realms of psychology, political, and communication science. It additionally encourages people to pay greater attention to the negative impact of crises and focuses on the important role of internal and external responses in crisis management, which can help improve the effectiveness of crisis management and promote the sustainable development of the tourism industry.

Calibration and prediction for the inexact SIR model

<abstract><p>A Susceptible Infective Recovered (SIR) model is usually unable to mimic the actual epidemiological system exactly. The reasons for this inaccuracy include observation errors and model discrepancies due to assumptions and simplifications made by the SIR model. Hence, this work proposes calibration and prediction methods for the SIR model with a one-time reported number of infected cases. Given that the observation errors of the reported data are assumed to be heteroscedastic, we propose two predictors to predict the actual epidemiological system by modeling the model discrepancy through a Gaussian Process model. One is the calibrated SIR model, and the other one is the discrepancy-corrected predictor, which integrates the calibrated SIR model with the Gaussian Process predictor to solve the model discrepancy. A wild bootstrap method quantifies the two predictors' uncertainty, while two numerical studies assess the performance of the proposed method. The numerical results show that, the proposed predictors outperform the existing ones and the prediction accuracy of the discrepancy-corrected predictor is improved by at least $ 49.95\% $.</p></abstract>

A Review of Bootstrap Methods in Ranked Set Sampling

This chapter provides a brief review of the existing resampling methods for RSS and its implementation to construct a bootstrap confidence interval for the mean parameter. The authors present a brief comparison of these existing methods in terms of their flexibility and consistency. To construct the bootstrap confidence interval, three methods are adopted, namely, bootstrap percentile method, bias-corrected and accelerated method, and method based on monotone transformation along with normal approximation. Usually, for the second method, the accelerated constant is computed by employing the jackknife method. The authors discuss an analytical expression for the accelerated constant, which results in reducing the computational burden of this bias-corrected and accelerated bootstrap method. The usefulness of the proposed methods is further illustrated by analyzing real-life data on shrubs.

Spinning S-matrix bootstrap in 4d

We review unitarity and crossing constraints on scattering amplitudes for particles with spin in four dimensional quantum field theories. As an application we study two to two scattering of neutral spin 1/2 fermions in detail. Assuming Mandelstam analyticity of its scattering amplitude, we use the numerical S-matrix bootstrap method to estimate various non-perturbative bounds on quartic and cubic (Yukawa) couplings.

The mediating effect of social media addiction on the relationship between leisure boredom and loneliness

This study aimed to examine the mediating effect of social media addiction on the relationship between leisure boredom and loneliness. A total of 330 high school students in Istanbul, 212 male (64.2%) and 118 female (35.8%), participated in the study voluntarily. In the study, the "Leisure Boredom Scale" developed by Iso-Ahola and Weissinger (1990) to measure participants' perceptions of boredom in their leisure, the "UCLA Loneliness Scale" developed by Peplau and Cutrona (1980) to measure loneliness level, and the "Social Media Addiction scale for Adolescents" developed by Eijnden, Lemmens and Valkenburg (2016) to measure the social media addiction level, were used as data collection tools. The convenience sampling method, which is one of the random sampling methods, was used in the sample selection and the face-to-face survey technique was preferred. In the analysis of the obtained data, descriptive statistics via the SPSS package program, Pearson Correlation, and regression analysis of the indirect impact approach based on the Bootstrap method via PROCESS v3.5 macro were performed. As a result, it was observed that leisure boredom had statistically significant effects on social media addiction and loneliness, and social media addiction on loneliness. Besides, regarding the main aim of the research, it was determined that social media addiction had a mediating effect on the relationship between leisure boredom and loneliness. ​Extended English summary is in the end of Full Text PDF (TURKISH) file. Özet Bu çalışmanın amacı serbest zamanda sıkılma algısı ile yalnızlık arasındaki ilişkide sosyal medya bağımlılığının aracı etkisinin incelenmesidir. Araştırma grubunu İstanbul’da lise düzeyinde öğrenim gören gönüllü olarak katılım sağlayan 212 erkek (%64,2), ve 118 kadın (%35,8) olmak üzere toplam 330 kişi oluşturmaktadır. Çalışmada katılımcıların serbest zamanlarında sıkılma algılarını ölçmek amacıyla Iso-Ahola ve Weissinger (1990) tarafından geliştirilen Boş Zaman Can Sıkıntısı Ölçeği, yalnızlık düzeyini ölçmek amacıyla Peplau and Cutrona (1980) tarafından geliştirilen UCLA Yalnızlık Ölçeği ve sosyal medya bağımlılık düzeylerini ölçmek amacıyla Eijnden, Lemmens ve Valkenburg (2016) tarafından geliştirilen Ergenler İçin Sosyal Medya Bağımlılığı Ölçeği kullanılmıştır. Örneklem seçiminde tesadüfi örneklem yöntemlerinden olan kolayda örnekleme yöntemi kullanılmış ve yüz yüze anket tekniği tercih edilmiştir. Elde edilen verilerin analizinde SPSS paket programı aracılığıyla tanımlayıcı istatistikler, Pearson Correlation ve PROCESS v3.5 makro aracılığıyla Bootstrap yöntemini temel alan dolaylı etki yaklaşımına ilişkin regresyon analizi kullanılmıştır. Araştırma bulgularına göre, serbest zamanda sıkılma algısının sosyal medya bağımlılığı üzerinde, serbest zamanda sıkılma algısının yalnızlık üzerinde, sosyal medya bağımlılığının yalnızlık üzerinde istatistiksel açıdan anlamlı etkilerinin olduğu görülmüştür. Ayrıca, araştırmanın temel amacına ilişkin olarak serbest zamanda sıkılma algısı ile yalnızlık arasındaki ilişkide sosyal medya bağımlılığının aracılık etkisi olduğu tespit edilmiştir.

A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS

In this paper, we present a new nonparametric method for estimating a conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy to implement and automatically ensures quantile monotonicity by construction. For inference, we propose to use a residual bootstrap method. Our Monte Carlo simulations show that this new estimator compares well with the check-function-based estimator in terms of estimation mean squared error. The bootstrap confidence bands yield adequate coverage probabilities. An empirical example uses a dataset of Canadian high school graduate earnings, illustrating the usefulness of the proposed method in applications.

Responsible Leadership Fuels Innovative Behavior: The Mediating Roles of Socially Responsible Human Resource Management and Organizational Pride

Leaders are under increasing pressure to inspire innovative endeavors in responsible ways. However, whether and how responsible leadership can fuel employee innovative behavior remains unknown. Therefore, drawing on social identity theory and social exchange theory, this study aims to investigate the psychological mechanisms underlying the responsible leadership-innovative behavior relationship. Multi-phase data were collected from 280 employees working in Chinese manufacturing firms to test the hypotheses using hierarchical regression analyses and the bootstrap method. The results reveal that responsible leadership is positively related to innovative behavior. Additionally, perceived socially responsible human resource management (HRM) and organizational pride separately and sequentially mediate the responsible leadership-innovative behavior relationship. This study empirically reveals the effectiveness of responsible leadership and sheds new light on the psychological processes through which it facilitates innovative behavior, revealing the generalizability of responsible leadership and innovative behavior in the Chinese context. Moreover, we respond to the call for incorporating leadership theory into HRM research and further advance the existing knowledge on both antecedents and outcomes of socially responsible HRM. For practical guidance, organizations are encouraged to foster innovation through investment in responsible management practices. Research limitations and implications are also discussed.

The Efficiency Analysis of Large Banks Using the Bootstrap and Fuzzy DEA: A Case of an Emerging Market

In this study, banks’ business performance efficiency was analysed using data envelopment analysis (DEA), with expense categories as inputs and income categories as outputs. By incorporating a bootstrap method and a fuzzy data approach into a DEA model, additional insights and sensitivity analysis of the results were obtained. This study shows how fuzzy and bootstrap DEA can be used for investigating real market problems with uncertain data in an uncertain sample. The empirical analysis was based on the period of 2009–2018 for a sample of seven of Croatia’s largest private banks. The aim of the study was also to interpret the DEA results with regards to the specific market, legal, and macroeconomic conditions, caused by the changes introduced in the last decade. The results, and the changes in the inputs and outputs over time, revealed that the market processes occurring in the observed period had a significant impact on banks’ business performance, but led to a more efficient banking system. Two banks were found to be dominant over the others regardless of the changes in the sample and data fuzziness. DEA results were additionally compared to the most important financial indicators and accounting ratios, as an alternative or additional measure of banks’ efficiency and profitability.