Archive for blogging

xi’an’s number [repost]

Posted in Statistics with tags , , , , on August 18, 2019 by xi'an

“extremely damaging and slanderous blog article”

Posted in Books, pictures, University life with tags , , , , , on April 13, 2019 by xi'an

Yesterday was a first for the ‘Og in that my university legal department received a complaint from a company about one of the posts, that among other things was considered as an “offending” and “extremely damaging and slanderous commentary” on the services proposed by this company, including posting the unsolicited marketing email it has sent me earlier and which induced this comment of mine.  Written in superb legalese of course. As my point had been made when the blog was posted, a while ago, and as I saw no point in bothering my legal department representatives or wasting further time on such nonsense, I removed this terribly damaging entry and hope the poor dears have recovered by now… It’s “a mere attempt at bloggin’, nothing more”, right?!

Bayes Comp 2018 [call for guest posts]

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , on March 26, 2018 by xi'an

As the next MCMski conference, now called Bayes Comp, is starting in Barcelona, Spain, March 26-29, I welcome all guest posts covering the conference, since I am not going to be there! Enjoy!

and here we go!

Posted in Books, Running, Statistics, University life with tags , , , , , on March 16, 2018 by xi'an

On March 1, I have started handling papers for Biometrika as deputy editor, along with Omiros Papaspiliopoulos. With on average one paper a day to handle this means a change in my schedule and presumably less blog posts about recent papers and arXivals if I want to keep my daily morning runs!

surg’Og interest from Serbia

Posted in Statistics with tags , , , , on January 15, 2018 by xi'an

Bayesian spectacles

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , on October 4, 2017 by xi'an

E.J. Wagenmakers and his enthusiastic team of collaborators at University of Amsterdam and in the JASP software designing team have started a blog called Bayesian spectacles which I find a fantastic title. And not only because I wear glasses. Plus, they got their own illustrator, Viktor Beekman, which sounds like the epitome of sophistication! (Compared with resorting to vacation or cat pictures…)

In a most recent post they addressed the criticisms we made of the 72 author paper on p-values, one of the co-authors being E.J.! Andrew already re-addressed some of the address, but here is a disagreement he let me to chew on my own [and where the Abandoners are us!]:

Disagreement 2. The Abandoners’ critique the UMPBTs –the uniformly most powerful Bayesian tests– that features in the original paper. This is their right (see also the discussion of the 2013 Valen Johnson PNAS paper), but they ignore the fact that the original paper presented a series of other procedures that all point to the same conclusion: p-just-below-.05 results are evidentially weak. For instance, a cartoon on the JASP blog explains the Vovk-Sellke bound. A similar result is obtained using the upper bounds discussed in Berger & Sellke (1987) and Edwards, Lindman, & Savage (1963). We suspect that the Abandoners’ dislike of Bayes factors (and perhaps their upper bounds) is driven by a disdain for the point-null hypothesis. That is understandable, but the two critiques should not be mixed up. The first question is Given that we wish to test a point-null hypothesis, do the Bayes factor upper bounds demonstrate that the evidence is weak for p-just-below-.05 results? We believe they do, and in this series of blog posts we have provided concrete demonstrations.

Obviously, this reply calls for an examination of the entire BS blog series, but being short in time at the moment, let me point out that the upper lower bounds on the Bayes factors showing much more support for H⁰ than a p-value at 0.05 only occur in special circumstances. Even though I spend some time in my book discussing those bounds. Indeed, the [interesting] fact that the lower bounds are larger than the p-values does not hold in full generality. Moving to a two-dimensional normal with potentially zero mean is enough to see the order between lower bound and p-value reverse, as I found [quite] a while ago when trying to expand Berger and Sellker (1987, the same year as I was visiting Purdue where both had a position). I am not sure this feature has been much explored in the literature, I did not pursue it when I realised the gap was missing in larger dimensions… I must also point out I do not have the same repulsion for point nulls as Andrew! While considering whether a parameter, say a mean, is exactly zero [or three or whatever] sounds rather absurd when faced with the strata of uncertainty about models, data, procedures, &tc.—even in theoretical physics!—, comparing several [and all wrong!] models with or without some parameters for later use still makes sense. And my reluctance in using Bayes factors does not stem from an opposition to comparing models or from the procedure itself, which is quite appealing within a Bayesian framework [thus appealing per se!], but rather from the unfortunate impact of the prior [and its tail behaviour] on the quantity and on the delicate calibration of the thing. And on a lack of reference solution [to avoid the O and the N words!]. As exposed in the demise papers. (Which main version remains in a publishing limbo, the onslaught from the referees proving just too much for me!)

abandon all o(p) ye who enter here

Posted in Books, Statistics, University life with tags , , , , , , on September 28, 2017 by xi'an

Today appeared on arXiv   a joint paper by Blakeley McShane, David Gal, Andrew Gelman, Jennifer Tackett, and myself, towards the abandonment of significance tests, which is a response to the 72 author paper in Nature Methods that recently made the news (and comments on the ‘Og). Some of these comments have been incorporated in the paper, along with others more on the psychology testing side. From the irrelevance of point null hypotheses to the numerous incentives for multiple comparisons, to the lack of sufficiency of the p-value itself, to the limited applicability of the uniformly most powerful prior principle…

“…each [proposal] is a purely statistical measure that fails to take a more holistic view of the evidence that includes the consideration of the traditionally neglected factors, that is, prior and related evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain.”

One may wonder about this list of grievances and its impact on statistical practice. The paper however suggests two alternatives, one being to investigate the potential impact of (neglected) factors rather than relying on thresholds. Another one, maybe less realistic, unless it is the very same, is to report the entirety of the data associated with the experiment. This makes the life of journal editors and grant evaluators harder, possibly much harder, but it indeed suggests an holistic and continuous approach to data analysis, rather than the mascarade of binary outputs. (Not surprisingly, posting this item of news on Andrew’s blog a few hours ago generated a large amount of discussion.)