Archive for Bayesian tests

Revised evidence for statistical standards

Posted in Kids, Statistics, University life with tags , , , , , , , , on December 19, 2013 by xi'an

valizWe just submitted a letter to PNAS with Andrew Gelman last week, in reaction to Val Johnson’s recent paper “Revised standards for statistical evidence”, essentially summing up our earlier comments within 500 words. Actually, we wrote one draft each! In particular, Andrew came up with the (neat) rhetorical idea of alternative Ronald Fishers living in parallel universes who had each set a different significance reference level and for whom alternative Val Johnsons would rise and propose a modification of the corresponding Fisher’s level. For which I made the above graph, left out of the letter and its 500 words. It relates “the old z” and “the new z”, meaning the boundaries of the rejection zones when, for each golden dot, the “old z” is the previous “new z” and “the new z” is Johnson’s transform. We even figured out that Val’s transform was bringing the significance down by a factor of 10 in a large range of values. As an aside, we also wondered why most of the supplementary material was spent on deriving UMPBTs for specific (formal) problems when the goal of the paper sounded much more global…

[speakerdeck https://speakerdeck.com/xianblog/revised-evidence-for-statistical-standards%5D

As I am aware we are not the only ones to have submitted a letter about Johnson’s proposal, I am quite curious at the reception we will get from the editor! (Although I have to point out that all of my earlier submissions of letters to to PNAS got accepted.)

on alternative perspectives and solutions on Bayesian tests

Posted in Statistics, Travel, University life with tags , , , , , , , on December 16, 2013 by xi'an

Here are the slides of my tutorial at O’ Bayes 2013 today, a pot-pourri of various, recent and less recent, criticisms (with, albeit less than usual, a certain proportion of recycled slides):

[speakerdeck https://speakerdeck.com/xianblog/obayes-2013-duke-university-a-tutorial-on-alternative-bayesian-tests%5D

Shravan’s comments on “Valen in Le Monde” [guest post]

Posted in Books, Statistics, University life with tags , , , , , , , on November 22, 2013 by xi'an

[Those are comments sent yesterday by Shravan Vasishth in connection with my post. Since they are rather lengthy, I made them into a post. Shravan is also the author of The foundations of Statistics and we got in touch through my review of the book . I may address some of his points later, but, for now, I find the perspective of a psycholinguist quite interesting to hear.]

Christian, Is the problem for you that the p-value, however low, is only going to tell you the probability of your data (roughly speaking) assuming the null is true, it’s not going to tell you anything about the probability of the alternative hypothesis, which is the real hypothesis of interest.

However, limiting the discussion to (Bayesian) hierarchical models (linear mixed models), which is the type of model people often fit in repeated measures studies in psychology (or at least in psycholinguistics), as long as the problem is about figuring out P(θ>0) or P(θ>0), the decision (to act as if θ>0) is going to be the same regardless of whether one uses p-values or a fully Bayesian approach. This is because the likelihood is going to dominate in the Bayesian model.

Andrew has objected to this line of reasoning by saying that making a decision like θ>0 is not a reasonable one in the first place. That is true in some cases, where the result of one experiment never replicates because of study effects or whatever. But there are a lot of effects which are robust and replicable, and where it makes sense to ask these types of questions.

One central issue for me is: in situations like these, using a low p-value to make such a decision is going to yield pretty similar outcomes compared to doing inference using the posterior distribution. The machinery needed to do a fully Bayesian analysis is very intimidating; you need to know a lot, and you need to do a lot more coding and checking than when you fit an lmer type of model.

It took me 1.5 to 2 years of hard work (=evenings spent not reading novels) to get to the point that I knew roughly what I was doing when fitting Bayesian models. I don’t blame anyone for not wanting to put their life on hold to get to such a point. I find the Bayesian method attractive because it actually answers the question I really asked, namely is θ>0 or θ<0? This is really great, I don’t have beat around the bush any more! (there; I just used an exclamation mark). But for the researcher unwilling (or more likely: unable) to invest the time into the maths and probability theory and the world of BUGS, the distance between a heuristic like a low p-value and the more sensible Bayesian approach is not that large.

uniformly most powerful Bayesian tests???

Posted in Books, Statistics, University life with tags , , , , , , , on September 30, 2013 by xi'an

“The difficulty in constructing a Bayesian hypothesis test arises from the requirement to specify an alternative hypothesis.”

Vale Johnson published (and arXived) a paper in the Annals of Statistics on uniformly most powerful Bayesian tests. This is in line with earlier writings of Vale on the topic and good quality mathematical statistics, but I cannot really buy the arguments contained in the paper as being compatible with (my view of) Bayesian tests. A “uniformly most powerful Bayesian test” (acronymed as UMBT)  is defined as

“UMPBTs provide a new form of default, nonsubjective Bayesian tests in which the alternative hypothesis is determined so as to maximize the probability that a Bayes factor exceeds a specified threshold”

which means selecting the prior under the alternative so that the frequentist probability of the Bayes factor exceeding the threshold is maximal for all values of the parameter. This does not sound very Bayesian to me indeed, due to this averaging over all possible values of the observations x and comparing the probabilities for all values of the parameter θ rather than integrating against a prior or posterior and selecting the prior under the alternative with the sole purpose of favouring the alternative, meaning its further use when the null is rejected is not considered at all and catering to non-Bayesian theories, i.e. trying to sell Bayesian tools as supplementing p-values and arguing the method is objective because the solution satisfies a frequentist coverage (at best, this maximisation of the rejection probability reminds me of minimaxity, except there is no clear and generic notion of minimaxity in hypothesis testing).

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Olli à/in/im Paris

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , on May 27, 2013 by xi'an

Warning: Here is an old post from last October I can at last post since Olli just arXived the paper on which this talk was based (more to come, before or after Olli’s talk in Roma!).

Oliver Ratman came to give a seminar today at our Big’MC seminar series. It was an extension of the talk I attended last month in Bristol:

10:45 Oliver Ratmann (Duke University and Imperial College) – “Approximate Bayesian Computation based on summaries with frequency properties”

Approximate Bayesian Computation (ABC) has quickly become a valuable tool in many applied fields, but the statistical properties obtained by choosing a particular summary, distance function and error threshold are poorly understood. In an effort to better understand the effect of these ABC tuning parameters, we consider summaries that are associated with empirical distribution functions. These frequency properties of summaries suggest what kind of distance function are appropriate, and the validity of the choice of summaries can be assessed on the fly during Monte Carlo simulations. Among valid choices, uniformly most powerful distances can be shown to optimize the ABC acceptance probability. Considering the binding function between the ABC model and the frequency model of the summaries, we can characterize the asymptotic consistency of the ABC maximum-likelhood estimate in general situations. We provide examples from phylogenetics and dynamical systems to demonstrate that empirical distribution functions of summaries can often be obtained without expensive re-simulations, so that the above theoretical results are applicable in a broad set of applications. In part, this work will be illustrated on fitting phylodynamic models that capture the evolution and ecology of interpandemic influenza A (H3N2) to incidence time series and the phylogeny of H3N2’s immunodominant haemagglutinin gene.

I however benefited enormously from hearing the talk again and also from discussing the fundamentals of his approach before and after the talk (in the nearest Aussie pub!). Olli’s approach is (once again!) rather iconoclastic in that he presents ABC as a testing procedure, using frequentist tests and concepts to build an optimal acceptance condition. Since he manipulates several error terms simultaneously (as before), he needs to address the issue of multiple testing but, thanks to a switch between acceptance and rejection, null and alternative, the individual α-level tests get turned into a global α-level test.