On efficient estimators of the proportion of true null hypotheses in a multiple testing setup

We consider the problem of estimating the proportion $\theta$ of true null hypotheses in a multiple testing context. The setup is classically modeled through a semiparametric mixture with two components: a uniform distribution on interval $[0,1]$ with prior probability $\theta$ and a nonparametric density $f$. We discuss asymptotic efficiency results and establish that two different cases occur whether $f$ vanishes on a set with non null Lebesgue measure or not. In the first case, we exhibit estimators converging at parametric rate, compute the optimal asymptotic variance and conjecture that no estimator is asymptotically efficient (i.e. attains the optimal asymptotic variance). In the second case, we prove that the quadratic risk of any estimator does not converge at parametric rate. We illustrate those results on simulated data

Nonparametric estimation of the density of the alternative hypothesis in a multiple testing setup. Application to local false discovery rate estimation

In a multiple testing context, we consider a semiparametric mixture model with two components where one component is known and corresponds to the distribution of $p$-values under the null hypothesis and the other component $f$ is nonparametric and stands for the distribution under the alternative hypothesis. Motivated by the issue of local false discovery rate estimation, we focus here on the estimation of the nonparametric unknown component $f$ in the mixture, relying on a preliminary estimator of the unknown proportion $\theta$ of true null hypotheses. We propose and study the asymptotic properties of two different estimators for this unknown component. The first estimator is a randomly weighted kernel estimator. We establish an upper bound for its pointwise quadratic risk, exhibiting the classical nonparametric rate of convergence over a class of H\"older densities. To our knowledge, this is the first result establishing convergence as well as corresponding rate for the estimation of the unknown component in this nonparametric mixture. The second estimator is a maximum smoothed likelihood estimator. It is computed through an iterative algorithm, for which we establish a descent property. In addition, these estimators are used in a multiple testing procedure in order to estimate the local false discovery rate. Their respective performances are then compared on synthetic data

Readability of Online Patient Education Materials for Merkel Cell Carcinoma

The Internet is a significant source of information for patients learning about their health; however, the quality of resources varies widely. The National Institutes of Health recommends that reading material is at or below an eighth-grade level. Our study examines the patient education materials (PEMs) of Merkel cell carcinoma (MCC), a rare and highly aggressive skin cancer. The population most affected by the disease is also the least proficient in health literacy. We analyzed the readability of PEM websites on MCC online and found that most MCC PEMs have not reached an appropriate reading level for patients

Family strategies and dilemmas for low-income rural-urban labour migrants

This policy brief summarises the findings of a qualitative study into the family relations of labour migrants across their peak child-bearing years. It evidences how wives/mothers and husbands/fathers manage their relations with spouse and children when they have to ‘go away’ for work. These strategies and dilemmas have implications for the impact of migration on the wellbeing both now and over the longer term for Vietnam

The classification of heritage tourists: a case of Hue City, Vietnam

Heritage, especially with World Heritage status, is increasingly becoming the main attraction of many tourist destinations. Heritage tourism is also the major tourism product in Hue city, Vietnam. Hitherto, there are almost no official statistics and research pertaining to heritage tourism as well as heritage tourists in Hue. This study aims at providing a preliminary profile of heritage tourists to Hue city and identifying different categories of heritage tourists, with a special focus on package tourists. The international heritage tourists’ profile seems to be similar to official statistics of international arrivals, indicating almost no difference in socio-demographic profile between heritage tourists and general tourists in the context of Hue. Various significant differences were found between international and domestic tourists in terms of tourist characteristics, trip profile and the perception of Hue. Adopting McKercher’s [(2002) Towards a classification of cultural tourists. International Journal of Tourism Research, 4, 29–38] cultural tourist classification, five categories of heritage tourists were identified, including purposeful heritage tourists, sightseeing heritage tourists, casual heritage tourists, incidental heritage tourists and serendipitous heritage tourists. Among these, sightseeing heritage tourists and purposeful heritage tourists were dominant

Spartan Face Mask Detection and Facial Recognition System

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices

Want to feel better, share what you know

In view of the mental health issues associated with the COVID-19 pandemic, this study draws on the theories of proactive coping and altruism to examine how knowledge sharing can be used to address employee performance and mental wellbeing. Emotional intelligence is modelled as a moderator in these relationships. Two studies were conducted in Australia and Vietnam to validate the proposed relationships. The results show that only knowledge donating has a positive effect on employee performance, whereas both types of knowledge sharing are significantly related to positive mental wellbeing. Emotional intelligence exerted significant moderation effects between knowledge donating and positive mental health in the case of Australia, and between collecting and performance in the Vietnam study. This study enriches knowledge sharing literature by integrating into position psychology. The findings have implications for practitioners to adopt a cost-effective means to address mental health and increase job performance

Securing jobs with individual trait and organisational support?

Purpose: In view of the COVID-19 pandemic’s impact on organisations and employees, this study aims to investigate a reverse relationship between role conflict, burnout and job insecurity, and proposed emotional intelligence (EI) and organisational support as individual and organisational factors, respectively, that may moderate this chain relationship. Drawing on conservation of resources (COR) theory, this paper proposes that organisational support as an organisational factor and EI as an individual ability may aid in minimising the perception of the depletion of resources and play a moderating role in conflict–burnout–job insecurity relationships. Design/methodology/approach: This study was undertaken in Australia with a focus on those who were employed and worked during the COVID-19 pandemic. The survey was conducted online using the Qualtrics platform as it offers user-friendly features for respondents. In total, 723 usable responses were generated for data analysis. Structural equation modelling was performed to test the hypotheses of this study. Findings: The results show that role conflict was significantly related to burnout, which in turn led to job insecurity. EI and organisational support reduced the impact of burnout on job insecurity. Originality/value: Theoretically, this research deepens an understanding of COR and role theory and contributes to mental health research and organisational studies. COR depicts individuals’ reservation of resources for desired or expected outcomes. This study approached from a depletion of resources perspective and revealed the consequences for both individuals and organisations. This study also expands role theory and includes job and family-derived roles to deepen the role conflict during the pandemic. Whilst most research taps into the job performance and behaviour domain to understand the impact of role conflict, this study proposed a novel concept of a mediation relationship between role conflict, burnout and job insecurity in line with the status quo of the pandemic. Consequently, this study contributes to job attitude research by approaching the antecedents from a combination of organisational, individual and situational factors because role conflict is reflected as a clash of job demands, family obligations and responsibilities, and the pandemic situation