Archive for computational statistics

Resigning from the editorial board of Statistics & Computing [reposted]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on July 10, 2026 by xi'an

Here is an open letter Robin Ryder and seventeen other aeditors of Statistics & Computing posted yesterday:

Along with 17 other Associate Editors, I have just resigned from the editorial board of Statistics and Computing. This was a difficult decision, since it is a great journal, which has published many tremendous articles under the leadership of editor-in-chief Ajay Jasra, and before him David Hand, Gilles Celeux, and Mark Girolami.

Springer Nature recently announced that for all future articles, authors would have to pay Article Processing Charges of $2990. This will impose massive financial barriers for colleagues who do not have access to funding, will create a further gap between institutions, and the journal will eventually lose its appeal as authors publish elsewhere. We do not wish to be part of this system.

The full letter sent to the editor-in-chief is below, with the current list of signatories. Other members of the board who wish to sign it can get it touch.

Dear Ajay,

We are writing to resign as Associate Editors of Statistics and Computing effective 31 December 2026, due to Springer Nature’s recent decision to impose Article Processing Charges (APCs) upon all authors.

We have the greatest admiration for your hard work for this journal and for the scientific community: under your stewardship, and that of previous Editors, the journal has published many articles of extremely high quality and it is a leading journal in our field. It has been an honour to play a small part in this during our time as Associate Editors, and we resign with great regret.

We understand that the decision to introduce APCs was not your own but was imposed by corporate management, and we are sorry that our resignation will put you in a difficult position. Nonetheless, the APCs that Springer Nature will introduce on 1st January 2027 are irreconcilable with our vision of science. They are not compatible with the goal of publishing the best science, whoever the authors; they exclude vast swathes of the scientific community; they are not a responsible use of the taxpayers’ money that funds our research; they are also at odds with the standards in Statistics.

Our decision to resign due to this change should not be taken as support for the existing system. The current academic publishing system is built upon vast quantities of unpaid labour and establishes a financial paywall to view the final research articles. Funding bodies are quite rightly pushing back against this, requiring that publicly funded research be freely accessible. From this perspective the move to “open-access” at Statistics and Computing might seem to have some merit on the surface. However, achieving this through APCs simply moves the financial barrier to a different part of the system. The exploitation still remains, and now Statistics and Computing will no longer publish the best science, both due to financial exclusion of those researchers who cannot afford to pay, and those community-minded researchers who refuse to pay on principle.

We would be very supportive of creating a new, genuinely open journal instead, possibly under the auspices of a learned society. We would welcome the opportunity to work with other members of the editorial board to create such a journal, and would be glad to submit future work there. We hope to have conversations with the editorial board and the community about such a move.

We hope to remain in contact with you. Thank you for your confidence,

Signatories so far:

Pierre Alquier (ESSEC)
Julyan Arbel (Inria Grenoble)
Louis Aslett (Durham University)
Joshua Bon (Adelaide University)
Alice Cleynen (CNRS)
Adrien Corenflos (University of Warwick)
Francesca Romana Crucinio (University of Turin)
Kamélia Daudel (ESSEC)
Ritabrata Dutta (University of Warwick)
Mathieu Gerber (University of Bristol)
Sahani Pathiraja (University of New South Wales)
François Portier (CREST-ENSAI)
Sam Power (University of Bristol)
Robin Ryder (Imperial College London)
Leah South (Queensland University of Technology)
Scott Sisson (University of New South Wales, Sydney)
David Warne (Queensland University of Technology)
Olivier Zahm (Inria Grenoble)

A modern introduction to probability and statistics [book review]

Posted in Books, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , on July 12, 2025 by xi'an

In the plane to Bengaluru, I read through the book A modern introduction to probability and statistics, by Graham Upton—whose Measuring Animal Abundance I reviewed for CHANCE a while ago—, which is based on the earlier Understanding Statistics, written jointly with Ian Cook. (Not to be confused with A modern introduction to probability and statistics by Dekking et al.) The subtitle is understanding statistical principles in the computer age. Sorry, in the age of the computer. While the cover is most pleasant (and modern), as noticed by an AF flight attendant, the contents are very very standard and could have been written decades ago since the main concession to “the” computer age is the inclusion of a few R commands at the end of most chapters. There are even a few distribution tables here and there (in case “the” computer is not available). But there is no other connection with computational statistics or statistical computing.

The classicism of the contents and the intended audience mean there is little therein on which to either object or criticise. The mixture of elementary probability and basic statistics in a single textbook always feels awkward to me and I think I would have trouble teaching solely from this material. Apart from the glaring typo on the variance of the sum of two correlated random variables on page 87, missing the factor 2 in front of the covariance, while correct(ed) p97 (and the inevitable “the the” typo spotted once). My main criticisms are on the potential confusion between samples and populations in the early chapters, when some statistics are used as motivational examples, as for instance in a (hidden) Monte Carlo stabilisation to the limiting values (p57), way before the Law of Large Numbers is introduced,, the variable mileage in mathematical rigour (while being uncertain that first year students can handle integrals and derivatives), the textbook examples, and the amount of the book contents spent on descriptive statistics and even more on the “classical” tests, with no critical perspective on using point nulls or p-values. The book concludes with a four page (benevolent) chapter on Bayesian statistics that is superfluous imho, or even counterproductive since my experience with a rushed introduction to Bayesian principles almost always result in a rejection of said principles. Plus, the illustration with the coin tossing is not particularly helpful since Andrew maintains that one can load a die, but cannot bias a coin. (A similar reservation on the half-page 289 coverage on pseudo-random generation and Monte Carlo principles for computing p-values.)

Minor (mostly idiosyncratic) remarks follow: CLT prior to LLN,   n-1 in sample sd, little to no model criticism (ntbcf goodness of fit), missing an opportunity when mentioning the varying probability of a day being a birthday (p31) in contrast with BDA cover story, and another opportunity to cite the 2024 Ig Nobel Prize for coin tossing around the LLN, an unclear definition for random variables( p53) and a potentially confusing introduction of Poisson distributions through a informal reference to Poisson processes (and no reason why the years of accession of the kings of Sussex and England till Guillaume—making a return on p178 with the Domesday Book—in 1066 should follow such a process as suggested in Figure 3.5), a surprising definition of the constant e as the special case of exp(x) when x=1 and its series expansion (p70), omitting proofs on laws of sums of iid rv’s by introducing moment generating functions rather late, another obscure reference to a 16th German treatise on surveying as a precursor of the CLT (p131), a proof for the normalising constant of the Normal density that will most likely escape most first year students, a introduction of the t, F, and χ² distributions with no mention of their respective densities (pp141-147), never defining a joint Normal distribution density, insisting on unbiasedness without noting that maximum likelihood—with a strange motivation that it “makes the next sample of n observations most likely to resemble the data in the current sample (p228)—estimators are almost always biased, an abundance of footnotes that may prove of little interest for the youngest readers.

[Disclaimer about potential self-plagiarism as usual: this post or an edited version will eventually appear in my Books Review section in CHANCE.]

Alexandre Bouchard-Côté, 2004 CRM-SSC Prize in Statistics winner

Posted in Statistics with tags , , , , , , , , , , , , , , , on May 18, 2024 by xi'an

Marc Beaumont on One World ABC webinar [30 May, 9am]

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on May 17, 2024 by xi'an

For the final talk of this Spring season of the One World ABC webinar, we are very glad to welcome Marc Beaumont, a central figure in the development of ABC methods and inference! (And a coauthor of our ABC-PMC paper.)

Model misspecification in population genomic
Mark Beaumont
University of Bristol
30th May 2024, 9.00am UK time

Abstract
In likelihood-free settings, problematic effects of model misspecification can manifest themselves during computation, leading to nonsensical answers, particularly causing convergence problems in sequential algorithms. This issue has been well studied in the last 10 years, leading to a number of methods for robust inference. In practical applications, likelihood-free methods tend to be applied to the output of complex simulations where there is a choice of summary statistics that can be computed. One approach to handling misspecification is to simply not use summary statistics computed from simulations of the model under the prior that cannot be with those observed in the data. This presentation gives a brief review of methods for observing and handling misspecification in ABC and SBI, and then discusses approaches that we have explored in a population genomic modelling framework.

Warwick Stats recruits

Posted in Statistics, University life with tags , , , , , , , , , on November 2, 2023 by xi'an


The Department of Statistics at the University of Warwick is recruiting:

Assistant Professor, Statistics (3 positions in Applied, Methodological or Theoretical Statistics )

Assistant Professor, Computational Statistics or Machine Learning (2 positions)

Associate Professor (1 position, any area within the Department)

Applicants should have evidence or promise of world-class research excellence and ability to deliver high quality teaching across our broad range of degree programmes. At Associate Professor level, applicants should have an outstanding publication record. Other positive indicators include enthusiasm for engagement with other disciplines, within and outside the Department and, at Associate Professor level, a proven ability to secure research funding. Further details of the requirements for each of the positions can be found at https://warwick.ac.uk/statjobs.

The Department of Statistics is committed to promoting equality and diversity, holding an Athena SWAN Silver award which demonstrates this commitment. We welcome applicants from all sections of the community and will give due consideration to applicants seeking flexible working patterns, and to those who have taken a career break. Further information about working at the University of Warwick, including information about childcare provision, career development and relocation is at https://warwick.ac.uk/services/humanresources/workinghere/.

Informal enquires can be addressed to Professor Jon Forster (J.J.Forster@warwick.ac.uk) or to any other senior member of the Warwick Statistics Department.

Further information about the Department of Statistics: https://warwick.ac.uk/stats
Further information about the University of Warwick: https://www2.warwick.ac.uk/services/humanresources/jobsintro/furtherparticulars