The expected and unexpected media effects on youth’s (mis)perceptions of peer norms: Chinese college students’ overestimation of peer smoking prevalence

Youth’s perceptions of peer norms have profound effects on their attitudes and behaviors. Unfortunately, their perceptions of peers tend to be biased. To investigate the role of media in the formation of misperceived peer norms, the current study tested and compared two media-effect mechanisms: the direct exemplar mechanism and the indirect influence of presumed influence (IPI) mechanism. The two mechanisms were tested in a sample (n = 1746) that is representative of college students in one large university in China, based on which the actual and perceived campus smoking rates were examined. The individual-level perceived peer smoking prevalence was then analyzed in comparison with the campus-level actual smoking prevalence. The results revealed serious overestimations of peer smoking prevalence. As predicted, pro-smoking media content significantly heightened smoking prevalence estimates through both exemplar and IPI mechanisms. In contrast, anti-smoking messages did not elicit any intended effect through the IPI mechanism, but showed a boomerang effect through the exemplar mechanism. These findings suggest that both pro- and anti-smoking media content may be responsible for inflated perceptions of peer smoking among youth.     

Geometric stick-breaking processes for continuous-time Bayesian nonparametric modeling

We propose a new class of time dependent random probability measures and show how this can be used for Bayesian nonparametric inference in continuous time. By means of a nonparametric hierarchical model we define a random process with geometric stick-breaking representation and dependence structure induced via a one dimensional diffusion process of Wright-Fisher type. The sequence is shown to be a strongly stationary measure-valued process with continuous sample paths which, despite the simplicity of the weights structure, can be used for inferential purposes on the trajectory of a discretely observed continuous-time phenomenon. A simple estimation procedure is presented and illustrated with simulated and real financial data.     

A New Bayesian Nonparametric Mixture Model

We propose a new mixture model for Bayesian nonparametric inference. Rather than considering extensions from current approaches, such as the mixture of Dirichlet process model, we end up shrinking it, by making the weights less complex. We demonstrate the model and discuss its performance.     

How Routine Patient‐Centered Monitoring Relates to Therapeutic Gains in Family Therapy: A Single‐Case Study

As routine outcome monitoring systems develop, questions emerge about how therapists incorporate feedback into their practice, and how this relates to therapeutic gains. A case of covert grief was monitored in each session with the Personal Questionnaire and the Helpful Aspects of Therapy instruments. At 4 months follow‐up, the Change Interview was administered. Individualized items facilitated access to the private views and needs of each member, which was useful for case formulation and ongoing personalization of the intervention. Qualitative feedback of treatment experiences helped therapists confirm the impacts of interventions, monitor therapeutic alliance, reformulate clinical hypothesis, and plan sessions. Therapists followed a critical triangulation process to decide the clinical meaning of feedback, according to his/her intervention model, expertise, and case‐specific context.     

Modelling failures times with dependent renewal type models via exchangeability

Failure times of a machinery cannot always be assumed independent and identically distributed, eg, if after reparations the machinery is not restored to a same-as-new condition. Framed within the renewal processes approach, a generalization that considers exchangeable inter-arrival times is presented. The resulting model provides a more realistic approach to capture the dependence among events occurring at random times, while retaining much of the tractability of the classical renewal process. Extensions of some classical results and special cases of renewal functions are analysed, in particular, the one corresponding to an exchangeable sequence driven by a Dirichlet process. The proposal is tested through an estimation procedure using simulated data sets and with an application to the reliability of hydraulic subsystems in load-haul-dump machines.     

Poisson-Driven Stationary Markov Models

ABSTRACT

We propose a simple yet powerful method to construct strictly stationary Markovian models with given but arbitrary invariant distributions. The idea is based on a Poisson-type transform modulating the dependence structure in the model. An appealing feature of our approach is the possibility to control the underlying transition probabilities and, therefore, incorporate them within standard estimation methods. Given the resulting representation of the transition density, a Gibbs sampler algorithm based on the slice method is proposed and implemented. In the discrete-time case, special attention is placed to the class of generalized inverse Gaussian distributions. In the continuous case, we first provide a brief treatment of the class of gamma distributions, and then extend it to cover other invariant distributions, such as the generalized extreme value class. The proposed approach and estimation algorithm are illustrated with real financial datasets. Supplementary materials for this article are available online.     

Stationary mixture transition distribution (MTD) models via predictive distributions

This paper combines two ideas to construct autoregressive processes of arbitrary order. The first idea is the construction of first order stationary processes described in Pitt et al. [(2002). Constructing first order autoregressive models via latent processes. Scand. J. Statist.29, 657–663] and the second idea is the construction of higher order processes described in Raftery [(1985). A model for high order Markov chains. J. Roy. Statist. Soc. B.47, 528–539]. The resulting models provide appealing alternatives to model non-linear and non-Gaussian time series.     

Does Centrality Moderate the Relation Between Skin Tone Satisfaction and Psychological Adjustment for Latinx Blacks and Non-Latinx Blacks?

Few studies have examined the similarities and differences between individuals from across the African diaspora in terms of racial identity and skin tone a     

Bayesian non-parametric inference for species variety with a two-parameter Poisson–Dirichlet process prior

Summary.  A Bayesian non-parametric methodology has been recently proposed to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size n , of the species variety featured by an additional sample of size m . Genomic applications pose the additional challenge of having to deal with large values of both n and m . In such a case the computation of the Bayesian non-parametric estimators is cumbersome and prevents their implementation. We focus on the two-parameter Poisson–Dirichlet model and provide completely explicit expressions for the corresponding estimators, which can be easily evaluated for any sizes of n and m . We also study the asymptotic behaviour of the number of new species conditionally on the observed sample: such an asymptotic result, combined with a suitable simulation scheme, allows us to derive asymptotic highest posterior density intervals for the estimates of interest. Finally, we illustrate the implementation of the proposed methodology by the analysis of five expressed sequence tags data sets.