Risk‐Taking and the Media

In recent years, media formats with risk‐glorifying content, such as video games that simulate illegal street racing (“bang and crash” games), films about extreme sports, and risky stunts have emerged as top sellers of the media industry. A variety of recent studies conducted by several researchers revealed that exposure to risk‐glorifying media content (e.g., video games that simulate reckless driving, smoking and drinking in movies, or depictions that glorify extreme sports) increases the likelihood that recipients will show increased levels of risk‐taking inclinations and behaviors. The present article (1) reviews the latest research on the detrimental impact of risk‐glorifying media on risk‐taking inclinations (cognitions, emotions, behaviors), (2) puts these findings in the theoretical context of recent sociocognitive models on media effects, and (3) makes suggestions to science and policymakers on how to deal with these effects in the future.     

Modeling Repeatable Events Using Discrete‐Time Data: Predicting Marital Dissolution

I join two methodologies by illustrating the application of multilevel modeling principles to hazard‐rate models with an emphasis on procedures for discrete‐time data that contain repeatable events. I demonstrate this application using data taken from the 1995 National Survey of Family Growth (NSFG) to ascertain the relationship between multiple covariates and risk of subsequent marital dissolution. I consider both fixed‐ and random‐effects versions of the multilevel model, as well as a Generalized Estimating Equation alternative to estimating random effects. I compare results obtained from the various estimators, noting why differences occur, and recommend when to choose the various alternatives. I also provide a set of SAS and STATA programs that can be used to analyze the NSFG data.     

Conflict dynamics in collaborative knowledge production. A study of network gatekeeping on Wikipedia

Digitalization has fundamentally changed gatekeeping by allowing users to participate in processes of information control. We study gatekeeping on Wikipedia by analyzing networks of deletions among editors. Factors considered to explain the emergence of these networks are editors’ information production capability, their range of activity, their communicative ties, their geographical location, and their past interactions. Relying on relational event models, we find that the observed gatekeeping processes are not only shaped by the factors related to an editor’s standing in the community, but also to a certain extent by their attribution to content-related groups and some forms of past interactions.     

Perturbation diagnostics of autocorrelation coefficients in non linear mixed-effects models with AR(1) errors based on M-estimation

In this work we propose and analyze non linear mixed-effects models for longitudinal data, which are widely used in the fields of economics, biopharmaceuticals, agriculture, and so on. A robust method to obtain maximum likelihood estimates for the parameters is presented, as well as perturbation diagnostics of autocorrelation coefficient in non linear models based on robust estimates and influence curvature. The obtained results are illustrated by plasma concentrations data presented in Davidian and Giltinan, which was analyzed under the non robust situation.     

Likelihood Imputation

The method of likelihood imputation is devised under the framework of latent structure models where the observation is a statistic of the complete data which can only be specified on a latent basis. The imputed data set is chosen to differ least from the observed one in their information contents—a concept with general implications for the analysis of incomplete-data. In contrast to the standard conditional-mean single imputation, our procedure depends on an entire likelihood region instead of any single point in it, and yields consistent parameter estimators nevertheless. We explain its implementations and illustrate with data from panel surveys and linear regression with censorship. We also discuss its potentials in sensitivity analysis     

Sparsely clustered survival data: application to the evaluation of the safety and effectiveness of medical devices

The effectiveness and safety of implantable medical devices is a critical public health concern. We consider analysis of data in which it is of interest to compare devices but some individuals may be implanted with two or more devices. Our motivating example is based on orthopedic devices, where the same individual can be implanted with as many as two devices for the same joint but on different sides of the body, referred to as bilateral cases. Different methods of analysis are considered in a simulation study and real data example, including both marginal and conditional survival models, fitting single and separate models for bilateral and non-bilateral cases, and combining estimates from these two models. The results of simulations suggest that in the context of orthopedic devices, where implants failures are rare, models fit on both bilateral and non-bilateral cases simultaneously could be quite misleading, and that combined estimates from fitting two separate models performed better under homogeneity. A real data example illustrates the issues surrounding analysis of orthopedic device data with bilateral cases. Our findings suggest that research studies of orthopedic devices should at minimum consider fitting separate models to bilateral and non-bilateral cases.     

Robust multivariate mixture regression models with incomplete data

Multivariate mixture regression models can be used to investigate the relationships between two or more response variables and a set of predictor variables by taking into consideration unobserved population heterogeneity. It is common to take multivariate normal distributions as mixing components, but this mixing model is sensitive to heavy-tailed errors and outliers. Although normal mixture models can approximate any distribution in principle, the number of components needed to account for heavy-tailed distributions can be very large. Mixture regression models based on the multivariate t distributions can be considered as a robust alternative approach. Missing data are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this paper, we propose a multivariate t mixture regression model with missing information to model heterogeneity in regression function in the presence of outliers and missing values. Along with the robust parameter estimation, our proposed method can be used for (i) visualization of the partial correlation between response variables across latent classes and heterogeneous regressions, and (ii) outlier detection and robust clustering even under the presence of missing values. We also propose a multivariate t mixture regression model using MM-estimation with missing information that is robust to high-leverage outliers. The proposed methodologies are illustrated through simulation studies and real data analysis.     

Classification-Error Models and Labor-Market Dynamics

Measurement error is often a severe problem in longitudinal data such as labor-market flows. Two correction methods have been used for labor-market flows, but the methods tend to adjust the data in opposite directions. One method shows a rigid labor market with long unemployment spells, whereas the other shows the opposite. Because the methods appear theoretically similar, one might interpret the differing results as an indictment of both procedures. This article documents and explains the differences between the two methods. More important, it argues that the differences result from the reliance of one method on assumptions that are theoretically implausible and rejected by the data.     

Admissibility of a test procedure in a conditionally specified random model

Admissibility of a test procedure is a desirable property though not a compelling one; while lack of it often results in discarding a given test procedure. In this paper we have proved necessary conditions for admissibility for a test procedure.     

Nonparametric estimation in semi-parametric univariate mixture models

We introduce new estimates of the mixing proportions, locations, and variances of the components of a finite univariate mixture model. We assume that the components are symmetric and differ only in the locations. No parametric model assumptions are imposed on the components. Further, when there is additional information available in the form of training samples that contain information concerning the mixing proportion, the new methods are robust to the symmetry assumption.