A Semi-parametric Bayesian Analysis of Survival Data Based on Lévy-driven Processes

In the presence of covariate information, the proportional hazards model is one of the most popular models. In this paper, in a Bayesian nonparametric framework, we use a Markov (Lévy-driven) process to model the baseline hazard rate. Previous Bayesian nonparametric models have been based on neutral to the right processes, which have a number of drawbacks, such as discreteness of the cumulative hazard function. We allow the covariates to be time dependent functions and develop a full posterior analysis via substitution sampling. A detailed illustration is presented.     

Measuring Religious Identities in Surveys

The purpose of this article is to acquaint the broader publicopinion research audience with what has been a salient issuewithin the community of scholars of religion. We address thequestion of how best to conceptualize and measure religiousidentities in research on contemporary American society. Weconsider the main approaches to the measurement of religiousidentification with regard to their backgrounds, their assumptionsabout the importance of understanding religious identities inhistorically relevant terms, and the practical considerationsof survey measurement. Using data from the General Social Survey,particularly recent innovative efforts to obtain informationon subjective association with particular religious traditionsand/or movements (e.g., Pentecostal, fundamentalist, evangelical,mainline, or liberal Protestant), we compare the two main approaches:the traditional "denominational" approach, where religious identitiesare assumed to be associated with religious denominations, andthe subjective approach, where religious identities are assumedto be captured by a set of "nondenominational" reference categorieslinked to particular historical religious traditions or socialmovements. We conclude that both approaches have substantialpredictive validity, and the most effective strategy for futureresearch may be one that uses a combination of approaches, ratherthan one that relies entirely on a single method of measurement.     

Victim service and justice system representative responses about partner stalking: what do professionals recommend?

Research suggests that partner stalking is associated with reassault and lethality as well as increased psychological distress for victims. However, there is a significant gap in information about stalking interventions and the responses of health, mental health, law enforcement, social services, and criminal justice professionals to women experiencing partner stalking. The purpose of this study is to examine the ideas about appropriate and effective responses to stalking victims from professionals in victim services and the justice system. The study also examined differences among rural and urban representatives because prior studies have shown significant differences between rural and urban areas on experiences and responses to partner violence. A total of 152 key informants (38 urban and 114 rural) were interviewed. Study results suggest a need for more training for victim services and justice system professionals on stalking and service needs of women who experience stalking in the context of partner violence.     

Estimators based on ranks for arma models

In this paper we introduce a new family of robust estimators for ARMA models. These estimators are defined by replacing the residual sample autocovariances in the least squares equations by autocovariances based on ranks. The asymptotic normality of the proposed estimators is provided. The efficiency and robustness properties of these estimators are studied. An adequate choice of the score functions gives estimators which have high efficiency under normality and robustness in the presence of outliers. The score functions can also be chosen so that the resulting estimators are asymptotically as efficient as the maximum likelihood estimators for a given distribution.     

Bayesian inference with misspecified models

This article reviews Bayesian inference from the perspective that the designated model is misspecified. This misspecification has implications in interpretation of objects, such as the prior distribution, which has been the cause of recent questioning of the appropriateness of Bayesian inference in this scenario. The main focus of this article is to establish the suitability of applying the Bayes update to a misspecified model, and relies on representation theorems for sequences of symmetric distributions; the identification of parameter values of interest; and the construction of sequences of distributions which act as the guesses as to where the next observation is coming from. A conclusion is that a clear identification of the fundamental starting point for the Bayesian is described.     

Computing Marginal Likelihoods via Posterior Sampling

This article presents a novel and simple approach to the estimation of a marginal likelihood, in a Bayesian context. The estimate is based on a Markov chain output which provides samples from the posterior distribution and an additional latent variable. It is the mean of this latent variable which provides the estimate for the value of the marginal likelihood.