**O**ur paper with Diego Salmerón and Juan Cano using integral priors for binomial regression and objective Bayesian hypothesis testing (one of my topics of interest, see yesterday’s talk!) eventually appeared in Statistica Sinica. This is Volume 25, Number 3, of July 2015 and the table of contents shows an impressively diverse range of topics.

## Archive for binomial regression

## Objective Bayesian hypothesis testing

Posted in Books, Statistics, University life with tags academic journals, binomial regression, integral priors, Objective Bayesian hypothesis testing, Statistica Sinica on June 19, 2015 by xi'an## integral priors for binomial regression

Posted in pictures, R, Statistics, University life with tags binomial regression, Harold Jeffreys, MCMC, Monte Carlo Statistical Methods, Murcia, numerical integration, objective Bayes, simulations, Spain on July 2, 2013 by xi'an**D**iego Salmerón and Juan Antonio Cano from Murcia, Spain *(check the movie linked to the above photograph!)*, kindly included me in their recent integral prior paper, even though I mainly provided (constructive) criticism. The paper has just been arXived.

**A** few years ago (2008 to be precise), we wrote together an integral prior paper, published in * TEST*, where we exploited the implicit equation defining those priors (Pérez and Berger, 2002), to construct a Markov chain providing simulations from both integral priors. This time, we consider the case of a binomial regression model and the problem of variable selection. The integral equations are similarly defined and a Markov chain can again be used to simulate from the integral priors. However, the difficulty therein follows from the regression structure, which makes selecting training datasets more elaborate, and whose posterior is not standard. Most fortunately, because the training dataset is exactly the right dimension, a re-parameterisation allows for a simulation of Bernoulli probabilities, provided a Jeffreys prior is used on those. (This obviously makes the “prior” dependent on the selected training dataset, but it should not overly impact the resulting inference.)