Outlier detection using difference-based variance estimators in multiple regression

In this article, we propose an outlier detection approach in a multiple regression model using the properties of a difference-based variance estimator. This type of a difference-based variance estimator was originally used to estimate error variance in a non parametric regression model without estimating a non parametric function. This article first employed a difference-based error variance estimator to study the outlier detection problem in a multiple regression model. Our approach uses the leave-one-out type method based on difference-based error variance. The existing outlier detection approaches using the leave-one-out approach are highly affected by other outliers, while ours is not because our approach does not use the regression coefficient estimator. We compared our approach with several existing methods using a simulation study, suggesting the outperformance of our approach. The advantages of our approach are demonstrated using a real data application. Our approach can be extended to the non parametric regression model for outlier detection.     

THE AGENCY OF POLITICS AND SCIENCE

We study a principal‐agent relationship between a politician and a researcher that captures stylized facts regarding the involvement of politics into scientific research. The politician has some ideal policy that he would like to implement, but needs to contract with a researcher to choose a policy that is supported by scientific advice. We study the implemented contracts under symmetric and under asymmetric information about the researcher's ability and concern for reputation, and discuss with which types of researchers the politician will contract. We identify several conflicts between the interests of voters and those of the politician. (JEL D72, D82, D83)     

How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique

In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013–2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

Are they ideological renegades? Fathers' experiences on taking parental leave and gender dynamics in Korea: A qualitative study

Despite their fear of repercussions on career advancement and gender identity, Korean fathers taking parental leave have continually increased. Why do Korean fathers take parental leave in spite of the risk of being stigmatized as less masculine and less ideal workers? Are they willing to be ‘ideological renegades' to be new involved fathers? In‐depth interviews with fathers who took parental leave provide interesting answers. Overall, taking parental leave does not make fathers become ideological renegades since it is mainly utilized in a manner that accommodates the work devotion schema, deviating from the formal policy objectives. This deviant utilization is encouraged and even valued by organizations. This may be part of a psychological contract between organizations and employees, ensuring that employees are dedicated to their career even during parental leave. Furthermore, beyond the legal eligibility of uptake, informal but powerful ‘organizational eligibility' such as high performance or their contribution to the organization, makes not only the likelihood but also the aftermath of taking up different across fathers. As a result, a hierarchy among fathers with class connotations emerges. This hierarchy among fathers, combined with the traditional gender hierarchy, may reinforce the masculine fabric of ideal workers as the norm.     

Discussion on statistical analysis of carcinogenicity studies with an early terminated treated group

High rates of mortality on long term carcinogenicity studies can often result in challenges when it comes to the statistical analysis of tumor incidence. The current regulatory advice often results in treated groups being terminated earlier than the control group. However, this advice rarely considers the impact of this action on the statistical analyses. The nature of these analyses means that groups terminated at different times may not be directly comparable due to age differences of the animals. Here we discuss the issues related to this and investigate several approaches of how to incorporate these groups within the statistical analyses. Although no single method appears to resolve these issues consistently, inclusion of the early terminated group is still informative. Depending on the timing of the early termination, either pooling of the groups into a single terminal kill (TK) interval or reassignment of intervals based purely on time of death (ie, no separate TK interval) appear preferable. However, to draw meaningful conclusions the time of onset of a given tumor must also be considered alongside incident rates and any statistical findings.     

Catching a glimpse: Corona‐life and its micro‐politics in academia

The spread of COVID‐19 acutely challenges and affects not just economic markets, demographic statistics and healthcare systems, but indeed also the politics of organizing and becoming in a new everyday life of academia emerging in our homes. Through a collage of stories, snapshots, vignettes, photos and other reflections of everyday life, this collective contribution is catching a glimpse of corona‐life and its micro‐politics of multiple, often contradicting claims on practices as many of us live, work and care at home. It embodies concerns, dreams, anger, hope, numbness, passion and much more emerging amongst academics from across the world in response to the crisis. As such, this piece manifests a shared need to — together, apart — enact and explore constitutive relations of resistance, care and solidarity in these dis/organizing times of contested spaces, identities and agencies as we are living–working–caring at home during lockdowns.     

Wait Until Your Mother Gets Home! Mothers' and Fathers' Discipline Strategies

From a traditional viewpoint, fathers are seen as the main disciplinarian in the family. However, recent studies suggest that these traditional family role patterns may have changed. In this study, we observed discipline strategies of mothers and fathers toward their sons and daughters. Participants included 242 families with two children (1 and 3 years of age). Findings revealed that parental discipline varied by the age of the children, but that mothers disciplined their children more often than fathers. Fathers, conversely, showed more laxness in response to child non‐compliance. Gender of the children was only related to physical interference, with mothers using more physical interference with boys than fathers, irrespective of birth order. Taken together, these findings highlight the importance of parent gender for parent–child interactions in early childhood, but also suggest that child age should be taken into account as important explanatory factors.     

INFORMATION DISCLOSURE POLICIES: EVIDENCE FROM THE ELECTRICITY INDUSTRY

While theory suggests that information programs may correct market failures and improve welfare, the empirical impacts of these policies remain undetermined. We show that mandatory disclosure programs in the electricity industry achieve stated policy goals. We find that the proportion of fossil fuels decreases, and the proportion of clean fuels increases in response to disclosure programs. However, the programs may produce unintended consequences. For example, programs may make “clean” firms cleaner, while leaving “dirty” firms relatively unchanged. If the marginal benefits of pollution abatement are larger at dirty firms than at clean firms, disclosure programs may induce inefficient abatement allocations. (JEL D83, Q58, D21)