Archive for letter to the editor

individualised polychotomous logistic regression

Posted in Books, Statistics, University life with tags , , , , , on May 17, 2022 by xi'an

A recent submission to Biometrika made me read the 1984 Biometrika paper of Begg and Gray on the individualisation of polychotomous regression, namely the idea that when considering this model with T categories, the regression parameters could be estimated by considering only the pairs (0,i), 0 being the baseline category (with no parameter), since the (true) probability to be in category i conditional on being in either category 0 or category i is logistic with the same coefficient as in the polychotomous model. While I see no issue with this remark (contrary to the submission author), it is of course producing (much quicker) a different estimate of the polychotomous parameter, when compared with the full likelihood approach. Not only because it does not exploit the entire information contained in the data but also because it operates with a pseudo-likelihood.

unexpected thanks

Posted in Statistics, University life with tags , , , , on April 16, 2022 by xi'an

I received the following email from an author the other day, after rejecting their paper right after submission:

Dear Prof. Christian Robert,

Thank you very much for your assessment of the paper, your candid feedback and also your encouragement to submit the paper to another journal. We value very much this quick and constructive feedback and the time you need to invest to guarantee such a feedback policy.
Thank you for taking the time to consider our submission and best regards,
Which does not happen that often.

folded Normals

Posted in Books, Kids, pictures, R, Running, Statistics with tags , , , , , , , , , , , , on February 25, 2021 by xi'an

While having breakfast (after an early morn swim at the vintage La Butte aux Cailles pool, which let me in free!), I noticed a letter to the Editor in the Annals of Applied Statistics, which I was unaware existed. (The concept, not this specific letter!) The point of the letter was to indicate that finding the MLE for the mean and variance of a folded normal distribution was feasible without resorting to the EM algorithm. Since the folded normal distribution is a special case of mixture (with fixed weights), using EM is indeed quite natural, but the author, Iain MacDonald, remarked that an optimiser such as R nlm() could be called instead. The few lines of relevant R code were even included. While this is a correct if minor remark, I am a wee bit surprised at seeing it included in the journal, the more because the authors of the original paper using the EM approach were given the opportunity to respond, noticing EM is much faster than nlm in the cases they tested, and Iain MacDonald had a further rejoinder! The more because the Wikipedia page mentioned the use of optimisers much earlier (and pointed out at the R package Rfast as producing MLEs for the distribution).

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