Bayesian survival modelling of university outcomes

Dropouts and delayed graduations are critical issues in higher education systems world wide. A key task in this context is to identify risk factors associated with these events, providing potential targets for mitigating policies. For this, we employ a discrete time competing risks survival model, dealing simultaneously with university outcomes and its associated temporal component. We define survival times as the duration of the student's enrolment at university and possible outcomes as graduation or two types of dropout (voluntary and involuntary), exploring the information recorded at admission time (e.g. educational level of the parents) as potential predictors. Although similar strategies have been previously implemented, we extend the previous methods by handling covariate selection within a Bayesian variable selection framework, where model uncertainty is formally addressed through Bayesian model averaging. Our methodology is general; however, here we focus on undergraduate students enrolled in three selected degree programmes of the Pontificia Universidad Católica de Chile during the period 2000–2011. Our analysis reveals interesting insights, highlighting the main covariates that influence students’ risk of dropout and delayed graduation

Incorporating unobserved heterogeneity in Weibull survival models: A Bayesian approach

Outlying observations and other forms of unobserved heterogeneity can distort inference for survival datasets. The family of Rate Mixtures of Weibull distributions includes subject-level frailty terms as a solution to this issue. With a parametric mixing distribution assigned to the frailties, this family generates flexible hazard functions. Covariates are introduced via an Accelerated Failure Time specification for which the interpretation of the regression coefficients does not depend on the choice of mixing distribution. A weakly informative prior is proposed by combining the structure of the Jeffreys prior with a proper prior on some model parameters. This improper prior is shown to lead to a proper posterior distribution under easily satisfied conditions. By eliciting the proper component of the prior through the coefficient of variation of the survival times, prior information is matched for different mixing distributions. Posterior inference on subject-level frailty terms is exploited as a tool for outlier detection. Finally, the proposed methodology is illustrated using two real datasets, one concerning bone marrow transplants and another on cerebral palsy

Estimating End-use Demand - a Bayesian-approach

Eliminating negative end-use or appliance-consumption estimates and incorporating direct-metering information into the process of generating these estimates-these are two important aspects of conditional demand analysis that will be the focus of this article. In both cases a Bayesian approach seems a natural way of proceeding. What needs to be investigated is whether it is also a viable and effective approach. In addition, such a framework naturally lends itself to prediction. Our application involves the estimation of electrical-appliance consumptions for a sample of Australian households. This application is designed to illustrate the viability of a full Bayesian analysis of the problem

Supplementary protein and gastro-intestinal parasitism in young grazing sheep

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