Does Religious Attendance Moderate the Connection Between Pornography Consumption and Attitudes Toward Women?

Feminist theory and religious doctrines alike often suggest that pornography alters the attitudes of those who consume it, particularly with respect to how consumers view women. Many would assume that pornography would universally encourage sexism and female objectification, but recent evidence has linked pornography use with more gender egalitarian views. Using data from a large-scale, nationally representative survey, we argue that cognitive dissonance among pornography consumers could alter egalitarian attitudes. We found that those who reported consuming pornography had more egalitarian attitudes than those who did not, but this difference was stronger among those who attended religious services more regularly—those who would be likely to experience dissonance when consuming pornography. This pattern was consistent across the three egalitarian attitudes we examined: attitudes toward women in power, women in the workplace, and abortion. Our results suggest that pornography might foster progressive attitudes among those most likely to hold conservative beliefs.     

PubPredict: Prediction of progression and survival in oncology leveraging publications and early efficacy data

In oncology/hematology early phase clinical trials, efficacies were often observed in terms of response rate, depth, timing, and duration. However, the true clinical benefits that eventually support registrational purpose are progression-free survival (PFS) and/or overall survival (OS), the follow-up of which are typically not long enough in early phase trials. This gap imposes challenges in strategies for late phase drug development. In this article, we tackle the question by leveraging published study to establish a quantitative link between early efficacy outcomes and late phase efficacy endpoints. We used solid tumor cancer as disease model. We modeled the disease course of a RECISTv1.1 assessed solid tumor with a continuous Markov chain (CMC) model. We parameterize the transition intensity matrix of a CMC model based on published aggregate-level summary statistics, and then simulate subject-level time-to-event data. The simulated data is shown to have good approximation to published studies. PFS and/or OS could be predicted with the transition intensity matrix modified given clinical knowledge to reflect various assumptions on response rate, depth, timing, and duration. The authors have built a R shiny application named PubPredict, the tool implements the algorithm described above and allows customized features including multiple response levels, treatment crossover and varying follow-up duration. This toolset has been applied to advise phase 3 trial design when only early efficacy data are available from phase 1 or 2 studies.     

Chronic avoidance helps explain the relationship between severity of childhood sexual abuse and psychological distress in adulthood

Recent studies have found that chronic avoidance of unpleasant internal experiences (e.g., thoughts, emotions, memories) is a maladaptive means of affect regulation often adopted by women with a history of sexual victimization in childhood. The primary aim of this study was to replicate and extend previous findings suggesting that higher levels of experiential avoidance may account for the relationship between childhood sexual abuse (CSA) and psychological distress in adulthood. It was hypothesized that, in a sample of undergraduate females (n = 151), the relationship between severity of CSA (e.g., frequency, nature of victimization) and trauma-related psychological distress would be mediated by avoidance. Results supported this hypothesis. Findings are consistent with previous studies, and further suggest that the general tendency to avoid or escape from unpleasant internal experiences may be a specific factor that exacerbates psychological distress among women with a history of sexual victimization in childhood.     

Flat-Top Realized Kernel Estimation of Quadratic Covariation With Nonsynchronous and Noisy Asset Prices

This article develops a general multivariate additive noise model for synchronized asset prices and provides a multivariate extension of the generalized flat-top realized kernel estimators, analyzed earlier by Varneskov (2014), to estimate its quadratic covariation. The additive noise model allows for α-mixing dependent exogenous noise, random sampling, and an endogenous noise component that encompasses synchronization errors, lead-lag relations, and diurnal heteroscedasticity. The various components may exhibit polynomially decaying autocovariances. In this setting, the class of estimators considered is consistent, asymptotically unbiased, and mixed Gaussian at the optimal rate of convergence, n^1/4. A simple finite sample correction based on projections of symmetric matrices ensures positive definiteness without altering the asymptotic properties of the estimators. It, thereby, guarantees the existence of nonlinear transformations of the estimated covariance matrix such as correlations and realized betas, which inherit the asymptotic properties from the flat-top realized kernel estimators. An empirically motivated simulation study assesses the choice of sampling scheme and projection rule, and it shows that flat-top realized kernels have a desirable combination of robustness and efficiency relative to competing estimators. Last, an empirical analysis of signal detection and out-of-sample predictions for a portfolio of six stocks of varying size and liquidity illustrates the use and properties of the new estimators.     

Dose intra-subject escalation to an event (DIETE): A new method for phase 1 dose-finding utilizing systematic intra-subject dose escalation with application to T-cell engagers

T-cell engagers are a class of oncology drugs which engage T-cells to initiate immune response against malignant cells. T-cell engagers have features that are unlike prior classes of oncology drugs (e.g., chemotherapies or targeted therapies), because (1) starting dose level often must be conservative due to immune-related side effects such as cytokine release syndrome (CRS); (2) dose level can usually be safely titrated higher as a result of subject's immune system adaptation after first exposure to lower dose; and (3) due to preventive management of CRS, these safety events rarely worsen to become dose limiting toxicities (DLTs). It is generally believed that for T-cell engagers the dose intensity of the starting dose and the peak dose intensity both correlate with improved efficacy. Existing dose finding methodologies are not designed to efficiently identify both the initial starting dose and peak dose intensity in a single trial. In this study, we propose a new trial design, dose intra-subject escalation to an event (DIETE) design, that can (1) estimate the maximum tolerated initial dose level (MTD1); and (2) incorporate systematic intra-subject dose-escalation to estimate the maximum tolerated dose level subsequent to adaptation induced by the initial dose level (MTD2) with a survival analysis approach. We compare our framework to similar methodologies and evaluate their key operating characteristics.     

Human Non-linguistic Vocal Repertoire: Call Types and Their Meaning

Recent research on human nonverbal vocalizations has led to considerable progress in our understanding of vocal communication of emotion. However, in contrast to studies of animal vocalizations, this research has focused mainly on the emotional interpretation of such signals. The repertoire of human nonverbal vocalizations as acoustic types, and the mapping between acoustic and emotional categories, thus remain underexplored. In a cross-linguistic naming task (Experiment 1), verbal categorization of 132 authentic (non-acted) human vocalizations by English-, Swedish- and Russian-speaking participants revealed the same major acoustic types: laugh, cry, scream, moan, and possibly roar and sigh. The association between call type and perceived emotion was systematic but non-redundant: listeners associated every call type with a limited, but in some cases relatively wide, range of emotions. The speed and consistency of naming the call type predicted the speed and consistency of inferring the caller’s emotion, suggesting that acoustic and emotional categorizations are closely related. However, participants preferred to name the call type before naming the emotion. Furthermore, nonverbal categorization of the same stimuli in a triad classification task (Experiment 2) was more compatible with classification by call type than by emotion, indicating the former’s greater perceptual salience. These results suggest that acoustic categorization may precede attribution of emotion, highlighting the need to distinguish between the overt form of nonverbal signals and their interpretation by the perceiver. Both within- and between-call acoustic variation can then be modeled explicitly, bringing research on human nonverbal vocalizations more in line with the work on animal communication.     

Statistical Modelling and Deconvolution of Yield Meter Data

Abstract.  This paper considers the problem of mapping spatial variation of yield in a field using data from a yield monitoring system on a combine harvester. The unobserved yield is assumed to be a Gaussian random field and the yield monitoring system data is modelled as a convolution of the yield and an impulse response function. This results in an unusual spatial covariance structure (depending on the driving pattern of the combine harvester) for the yield monitoring system data. Parameters of the impulse response function and the spatial covariance function of the yield are estimated using maximum likelihood methods. The fitted model is assessed using certain empirical directional covariograms and the yield is finally predicted using the inferred statistical model.     

Adaptive estimating function inference for nonstationary determinantal point processes

Estimating function inference is indispensable for many common point process models where the joint intensities are tractable while the likelihood function is not. In this article, we establish asymptotic normality of estimating function estimators in a very general setting of nonstationary point processes. We then adapt this result to the case of nonstationary determinantal point processes, which are an important class of models for repulsive point patterns. In practice, often first‐ and second‐order estimating functions are used. For the latter, it is a common practice to omit contributions for pairs of points separated by a distance larger than some truncation distance, which is usually specified in an ad hoc manner. We suggest instead a data‐driven approach where the truncation distance is adapted automatically to the point process being fitted and where the approach integrates seamlessly with our asymptotic framework. The good performance of the adaptive approach is illustrated via simulation studies for non‐stationary determinantal point processes and by an application to a real dataset.     

An Empirical Model of Growth Through Product Innovation

Productivity differences across firms are large and persistent, but the evidence for worker reallocation as an important source of aggregate productivity growth is mixed. The purpose of this paper is to estimate the structure of an equilibrium model of growth through innovation designed to identify and quantify the role of resource reallocation in the growth process. The model is a version of the Schumpeterian theory of firm evolution and growth developed by Klette and Kortum (2004) extended to allow for firm heterogeneity. The data set is a panel of Danish firms that includes information on value added, employment, and wages. The model's fit is good. The estimated model implies that more productive firms in each cohort grow faster and consequently crowd out less productive firms in steady state. This selection effect accounts for 53% of aggregate growth in the estimated version of the model.     

The new PR of states: How nation branding practices affect the security function of public diplomacy

This paper investigates how the role of public relations practice in public diplomacy is undergoing a transformation as a consequence of the influence from nation branding. A case study of the Danish government's response to the so-called Cartoon Crisis illustrates how the threat from international terrorism to national security initially served as a catalyst for new public diplomacy initiatives. But as the initiatives were implemented within a framework of nation branding the focus on risk reduction became subjected to a marketing logic and a new focus on economic objectives took over. The paper points to a possible future status of public diplomacy under the influence of nation branding: Public diplomacy may maintain a function pertinent to national security but as this function is incapable of managing real risks it will only serve as auto-communication that legitimizes security policy towards a domestic audience. In the public diplomacy efforts towards transnational publics the link to national security will completely disappear whereby the public relations of states is transformed to the marketing of states.