Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Association between sexually explicit messages and sleep health among French sexual minority men

It has been demonstrated that sexual minority men (SMM) participate in sexting. While research has shown that engagement in the exchange of sexually explicit media is associated with poor health outcomes, no previous research has investigated its association with sleep health outcomes. This study sought to examine the association between sexually explicit media and sleep health among SMM, a population that suffers from poor sleep health. A popular geosocial networking application was used to recruit SMM individuals (N?=?580) in the Paris, France, metropolitan area. Multivariate analyses, adjusting for sociodemographics, were used to test the association between the frequency of sexually explicit messaging and three dimensions of sleep health: (1) sleep quality, (2) sleep duration, and (3) two aspects of sleep problems. In multivariate analyses, those who reported engaging in sexually explicit messaging more were more likely to report getting less than seven?hours of sleep (aRR = 1.24; 95% CI = 1.08, 1.43) compared with those who reported engaging in sexually explicit messaging less. No significant associations were found between sexting and sleep quality or reporting sleep problems. Sexually explicit messaging was associated with shorter sleep duration. Intervention targeted at individuals who sext could potentially improve sleep health outcomes.     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

Progressive censoring with fixed censoring times

In this paper, we revisit the progressive Type-I censoring scheme as it has originally been introduced by Cohen [Progressively censored samples in life testing. Technometrics. 1963;5(3):327–339]. In fact, original progressive Type-I censoring proceeds as progressive Type-II censoring but with fixed censoring times instead of failure time based censoring times. Apparently, a time truncation has been added to this censoring scheme by interpreting the final censoring time as a termination time. Therefore, not much work has been done on Cohens's original progressive censoring scheme with fixed censoring times. Thus, we discuss distributional results for this scheme and establish exact distributional results in likelihood inference for exponentially distributed lifetimes. In particular, we obtain the exact distribution of the maximum likelihood estimator (MLE). Further, the stochastic monotonicity of the MLE is verified in order to construct exact confidence intervals for both the scale parameter and the reliability.     

Viral video ‘blood chocolate’ activism,millennial anti-trafficking,and the neoliberal resurgence of shaming

Viral videos offer contemporary activists a nimble, provocative tool with which to disseminate humanitarian messages. This article historicizes anti-trafficking video media that imagine a new potential with which to alter behavioral norms around cocoa and chocolate. The article engages theories and approaches to historical and contemporary modalities of naming and shaming, spanning three centuries to the present day. Videos are examined for style, format, and content, with the view to describing a ‘blood chocolate’ visual and textual metonymy. Recurring themes include transnational power, corporate responsibility, and the horrific scale of exploitation. But viral videos also appear deeply influenced by neoliberal economics that celebrate lawful enterprise and promote consumer-driven solutions to exploitative child labor.     

How much is too much: Does the size of income support transfers affect labor supply?

Cash transfer programs pursue mainly protective objectives, but can also impact rural livelihoods by inducing investments in productive activities and changing household labor allocation. We adopt a continuous treatment approach to quantify how households’ labor supply responds to transfer size. We find a shift from paid labor to own farm labor and find that the transfer size is well within a level that would have disincentive effects on time spent on own farm activities. The switch from paid to own farm labor occurs at lower levels of transfers for labor-constrained households, and at higher levels for non-labor constrained households.     

Contestation on Reddit,Gamergate, and movement barriers

This article investigates how the social media site Reddit hindered the collective action efforts stemming from the Gamergate controversy. Using forum posts from two time-points, one early in the movement and another six months later, this work describes how the Reddit platform impeded movement efforts over time. More specifically, we examine how the nature of Reddit discussions created barriers to each of three key movement processes: narrative construction of collective identity, framing, and boundary work. We argue that unlike social media platforms such as Facebook, some sites–Reddit in this case–may negatively impact movement success depending on its structure. We also discuss the importance of differing ICT structures and implications for online interactions more generally.     

Machine Learning for Drug Overdose Surveillance

ABSTRACT

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan (Herlands, McFowland, Wilson, & Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) enables early detection of emerging patterns in spatio-temporal data, accounting for both the complex, correlated nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan (Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) to 8 years of case-level overdose data from Allegheny County, Pennsylvania. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.     

Inferences About Sexual Orientation: The Roles of Stereotypes,Faces, and The Gaydar Myth

In the present work, we investigated the pop cultural idea that people have a sixth sense, called “gaydar,” to detect who is gay. We propose that “gaydar” is an alternate label for using stereotypes to infer orientation (e.g., inferring that fashionable men are gay). Another account, however, argues that people possess a facial perception process that enables them to identify sexual orientation from facial structure. We report five experiments testing these accounts. Participants made gay-or-straight judgments about fictional targets that were constructed using experimentally manipulated stereotypic cues and real gay/straight people's face cues. These studies revealed that orientation is not visible from the face—purportedly “face-based” gaydar arises from a third-variable confound. People do, however, readily infer orientation from stereotypic attributes (e.g., fashion, career). Furthermore, the folk concept of gaydar serves as a legitimizing myth: Compared to a control group, people stereotyped more often when led to believe in gaydar, whereas people stereotyped less when told gaydar is an alternate label for stereotyping. Discussion focuses on the implications of the gaydar myth and why, contrary to some prior claims, stereotyping is highly unlikely to result in accurate judgments about orientation.