Last morn, I had the surprise of receiving the following email:

This is to inform you that the following abstract has been submitted to the 3rd International Conference of the ERCIM WG on COMPUTING & STATISTICS (ERCIM’10)

Ab#: 114
Title: Goodness of Fit Via Mixtures of Beta distributions
Keywords: nonparametric estimation, posterior conditional predictive p-value.
Abstract: We consider a Bayesian approach to goodness of fit, that is, to the problem of testing whether or not a given parametric model is compatible with the data at hand . Since we are concerned with a goodness of fit problem, it is more of interest to consider a functional distance to the tested model d(F;F) as the basis of our test, rather than the corresponding Bayes factor, since the later puts more emphasis on the parameters. It is both of high interest and of strong difficulty to come up with a satisfactory notion of a Bayesian test for goodness ofit to a distribution or to a family of distributions.

The abstract is a plagiarism of your work.

I am informing you of about this in case the author has tried to plagiarize the whole paper. The same author has submitted a second abstract plagiarizing another paper.  The author uses bogus affiliations and I cannot trace his institution in case he has one.

It is somehow comforting to see that such a gross example of plagiarism can get detected, despite the fact that our paper never got published. Although I am sure there must be conferences that do not apply any filter on the submission…

This paper with Judith Rousseau was once submitted to Series B, but I could not come to complete the requested revision for programming motives, the task of modifying the several thousand lines of C code driving the beta mixture estimation filling me with paralysing dread! This is actually the time when I stopped programming in C (the fact that I ever really programmed in C is actually debatable!). This is unfortunate as the spirit of the paper was quite nice, using an idea borrowed from Verdinelli and Wasserman to build a genuine Bayesian goodness of fit test… I do not think there is much to salvage at this later stage, given the explosion of Bayesian non-parametrics.