Archive for paradoxes

a paradox about likelihood ratios?

Posted in Books, pictures, Statistics, University life with tags , , , , , , , on January 15, 2018 by xi'an

Aware of my fascination for paradoxes (and heterodox publications), Ewan Cameron sent me the link to a recent arXival by Louis Lyons (Oxford) on different asymptotic distributions of the likelihood ratio. Which is full of approximations. The overall point of the note is hard to fathom… Unless it simply plans to illustrate Betteridge’s law of headlines, as suggested by Ewan.

For instance, the limiting distribution of the log-likelihood of an exponential sample at the true value of the parameter τ is not asymptotically Gaussian but almost surely infinite. While the log of the (Wilks) likelihood ratio at the true value of τ is truly (if asymptotically) a Χ² variable with one degree of freedom. That it is not a Gaussian is deemed a “paradox” by the author, explained by a cancellation of first order terms… Same thing again for the common Gaussian mean problem!

about paradoxes

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , on December 5, 2017 by xi'an

An email I received earlier today about statistical paradoxes:

I am a PhD student in biostatistics, and an avid reader of your work. I recently came across this blog post, where you review a text on statistical paradoxes, and I was struck by this section:

“For instance, the author considers the MLE being biased to be a paradox (p.117), while omitting the much more substantial “paradox” of the non-existence of unbiased estimators of most parameters—which simply means unbiasedness is irrelevant. Or the other even more puzzling “paradox” that the secondary MLE derived from the likelihood associated with the distribution of a primary MLE may differ from the primary. (My favourite!)”

I found this section provocative, but I am unclear on the nature of these “paradoxes”. I reviewed my stat inference notes and came across the classic example that there is no unbiased estimator for 1/p w.r.t. a binomial distribution, but I believe you are getting at a much more general result. If it’s not too much trouble, I would sincerely appreciate it if you could point me in the direction of a reference or provide a bit more detail for these two “paradoxes”.

The text is Chang’s Paradoxes in Scientific Inference, which I indeed reviewed negatively. To answer about the bias “paradox”, it is indeed a neglected fact that, while the average of any transform of a sample obviously is an unbiased estimator of its mean (!), the converse does not hold, namely, an arbitrary transform of the model parameter θ is not necessarily enjoying an unbiased estimator. In Lehmann and Casella, Chapter 2, Section 4, this issue is (just slightly) discussed. But essentially, transforms that lead to unbiased estimators are mostly the polynomial transforms of the mean parameters… (This also somewhat connects to a recent X validated question as to why MLEs are not always unbiased. Although the simplest explanation is that the transform of the MLE is the MLE of the transform!) In exponential families, I would deem the range of transforms with unbiased estimators closely related to the collection of functions that allow for inverse Laplace transforms, although I cannot quote a specific result on this hunch.

The other “paradox” is that, if h(X) is the MLE of the model parameter θ for the observable X, the distribution of h(X) has a density different from the density of X and, hence, its maximisation in the parameter θ may differ. An example (my favourite!) is the MLE of ||a||² based on x N(a,I) which is ||x||², a poor estimate, and which (strongly) differs from the MLE of ||a||² based on ||x||², which is close to (1-p/||x||²)²||x||² and (nearly) admissible [as discussed in the Bayesian Choice].

Monty Hall closes the door

Posted in Books, Kids, pictures with tags , , , , , , , , , on October 1, 2017 by xi'an

Among much more dramatic news today, I learned about Monty Hall passing away, who achieved long lasting fame among probabilists for his TV game show leading to the Monty Hall problem, a simple conditional probability derivation often leading to arguments because of the loose wording of the conditioning event. By virtue of Stigler’s Law, the Monty Hall game was actually invented earlier, apparently by the French probabilist Joseph Bertrand, in his Calcul des probabilités. The New York Times article linked with the image points out the role of outfits with the game participants, towards being selected by the host, Monty Hall. And that one show had a live elephant behind a door, instead of a goat, elephant which freaked out..!

Principles of scientific methods [not a book review]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , on November 11, 2014 by xi'an

Mark Chang, author of Paradoxes in Scientific Inference and vice-president of AMAG Pharmaceuticals, has written another book entitled Principles of Scientific Methods. As was clear from my CHANCE review of Paradoxes in Scientific Inference, I did not find much appeal in this earlier book, even after the author wrote a reply (first posted on this blog and later printed in CHANCE). Hence a rather strong reluctance [of mine] to engage into another highly critical review when I received this new opus by the same author. [And the brainwave cover just put me off even further, although I do not want to start a review by criticising the cover, it did not go that well with the previous attempts!]

After going through Principles of Scientific Methods, I became ever more bemused about the reason(s) for writing or publishing such a book, to the point I decided not to write a CHANCE review on it… (But, having spent some Métro rides on it, I still want to discuss why. Read at your own peril!)

Continue reading

on the Jeffreys-Lindley’s paradox (revision)

Posted in Statistics, University life with tags , , , , , , , , , on September 17, 2013 by xi'an

As mentioned here a few days ago, I have been revising my paper on the Jeffreys-Lindley’s paradox paper for Philosophy of Science. It came as a bit of a (very pleasant) surprise that this journal was ready to consider a revised version of the paper given that I have no formal training in philosophy and that the (first version of the) paper was rather hurriedly made of a short text written for the 95th birthday of Dennis Lindley and of my blog post on Aris Spanos’ “Who should be afraid of the Jeffreys-Lindley paradox?“, recently published in Philosophy of Science.  So I found both reviewers very supportive and I am grateful for their suggestions to improve both the scope and the presentation of the paper. It has been resubmitted and rearXived, and I am now waiting for the decision of the editorial team with the appropriate philosophical sense of detachment…

ISBA regional meeting in Varanasi (day 3)

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on January 11, 2013 by xi'an

plaque in the department of mathematical sciences. BHU, Varanasi, Uttar Pradesh, Jan. 10, 2013On the last/my day of the ISBA meeting in Varanasi, I attended a few talks before being kindly driven to the airport (early, too early, but with the unpredictable traffic there, it was better to err on the cautionary side!). In the dynamical model session, Simon Wilson presented a way to approximate posteriors for HMMs based on Chib’s (or Bayes’!) formula, while Jonathan Stroud exposed another approach to state-space model approximation involving a move of the state parameter based on a normal approximation of its conditional given the observable, approximation which seemed acceptable for the cloud analysis model he was processing. Nicolas Chopin then gave a quick introduction to particle MCMC, all the way to SMC². (As a stern chairmain of the session, I know Nicolas felt he did not have enough time but he did a really good job of motivating those different methods, in particular in explaining why the auxiliary variable approach makes the unbiased estimator of the likelihood a valid MCMC method.) Peter Green’s plenary talk was about a emission tomography image analysis whose statistical processing turned into a complex (Bernstein-von Mises) convergence theorem (whose preliminary version I saw in Bristol during Natalia Bochkina’s talk).

boats on the Ganges before sunset, Jan. 8, 2013Overall, as forewarned by and expected from the program, this ISBA meeting was of the highest scientific quality. (I only wish I had had hindi god abilities to duplicate and attend several parallel sessions at the same time!) Besides, much besides!, the wamr attention paid to everyone by the organisers was just simply un-be-lie-vable! The cultural program went in par with the scientific program. The numerous graduate students and faculty involved in the workshop organisation had a minute knowledge of our schedules and locations, and were constantly anticipating our needs and moves. Almost to a fault, i.e. to a point that was close to embarassing for our cultural habits. I am therefore immensely grateful [personally and as former ISBA president] to all those people that contributed to the success of this ISBA meeting and first and foremost to Professor Satyanshu Upadhyay who worked relentlessly towards this goal during many months! (As a conference organiser, I realise I was and am simply unable to provide this level of welcome to the participants, even for much smaller meetings… The contrast with my previous conference in Berlin could not be more extreme as, for a much higher registration fee, the return was very, very limited.) I will forever (at least until my next reincarnation!) keep the memory of this meeting as a very special one, quite besides giving me the opportunity of my first visit to India

ISBA Regional Meeting in Varanasi (day 2)

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on January 10, 2013 by xi'an

boats on the Ganges from a Varanasi ghat, Varanasi, Uttar Pradesh, Jan. 6, 2013A second full day at the ISBA meeting in Varanasi: I attended a non-parametric session with Sonia Petrone talking about mixtures of regressions (more precisely, piecewise linear functions) and Ramses Mena defining stationary processes via a Gibbs-like construction (which I would have liked to have more time to fully understand). Then Jamie Robbins gave a talk related to the paradox raised by Robbins and Ritov and discussed recently by Chris Sims. (Jamie asked for my opinion at the end of the talk, but I had none, considering the problem to be more of an epiphenomenon than a genuine statistical difficulty… I may comment more on this question later, almost feel compelled to by Jamie’s interpelation, but I had not much to say at this stage! It sounds like another of those infinite dimensional problems where the Bayesian solution can get stranded.) I then attended Murray Aitkin’s talk, where he reanalysed the Berkof et al. (2003) dataset using his integrated likelihood. The afternoon was a succession of plenary talks by Susie Bayarri, Fabrizio Ruggeri and Peter Muller. (It could have been called the afternoon of the ISBA past-presidents, as I also talked in this series!) Susie introduced a new notion of effective sample size, call TESS, not in the importance sampling sense of independent-sample-equivalent used in simulation, but in the model comparison sense of information criterion penalising and prior scaling factor. This was the first time I heard about this notion and I found it definitely worth pursuing, in particular in search of a connection with the g-prior. (Nice name too!, connecting to a great book with a quote from Hardy about Tess being the victim of her beauty…) The day ended with a group excursion on boats up the Ganges for attending the sunset (Ganga Aarti, आरती) ceremony at Dasaswamedh Ghat, a ceremony that remained rather esoteric [for me] without the proper explanation.