IWSM2018 Bristol 15-20 July Programme

Short course Sunday 15 July

The conference will open with a short course given by Vanessa Didelez. There is a modest extra charge for shoret course participation.

Keynote Speakers

  • John Aston
  • Iain Currie GLAM: From Graz to Bristol

    Data that can be arranged in an array are common in statistics and then models often have a row-column-depth-... structure. In such cases, data sets and models can be large, and can present computational problems even for modern computers, particularly when smoothing is carried out in a Generalized Linear Model (GLM) framework. A Generalized Linear Array Model or GLAM is a fast low-footprint method of computation for such data sets and models. The GLAM approach and algorithms were first described at the IWSM in Florence in 2004. We describe GLAM and then mention some of its many applications since that time. The IWSM conferences have played a key role in GLAM's development. We tell the story of GLAM through these workshops, starting with the 1999 conference in Graz and finishing in Bristol in 2018.

  • Leo Held p-Values for Credibility

    Analysis of credibility is a reverse-Bayes technique that has been proposed by Matthews (2001) to overcome some of the shortcomings of significance tests. A significant result is deemed credible if current knowledge about the effect size is in conflict with any sceptical prior that would make the effect non-significant. In this paper I formalize the approach and propose to use Bayesian predictive tail probabilities to quantify the evidence for credibility. This gives rise to a p-value for extrinsic credibility, taking into account both the internal and the external evidence for an effect. The assessment of intrinsic credibility leads to a new threshold for ordinary significance that is remarkably close to the recently proposed 0.005 level. Finally, a p-value for intrinsic credibility is proposed that is a simple function of the ordinary p-value for significance and has a direct frequentist interpretation in terms of the replication probability that a future study under identical conditions will give an estimated effect in the same direction as the first study.

  • Catherine Matias A semiparametric extension of the stochastic block model for longitudinal networks

    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals' latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Finally, we demonstrate the performance of our procedure on synthetic experiments, analyse two datasets to illustrate the utility of our approach and comment on competing methods.

  • Hein Putter