Archive for email
Microsoft cares!
Posted in Travel, University life with tags email, jetlag, Microsoft, Outlook, time zones, University of Warwick on August 2, 2022 by xi'an
unexpected thanks
Posted in Statistics, University life with tags Biometrika, email, letter to the editor, rejection, submission 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.
email footprint
Posted in Travel, University life with tags carbon impact, clouds, email, mass emailing, Salzburg, spams on September 14, 2019 by xi'an
While I was wondering (im Salzburg) at the carbon impact of sending emails with an endless cascade of the past history of exchanges and replies, I found this (rather rudimentary) assessment that, while standard emails had an average impact of 4g, those with long attachments could cost 50g, quoting from Burners-Lee, leading to the fairly astounding figure of an evaluated impact of 1.6 kg a day or more than half a ton per year! Quite amazing when considering that a round flight Paris-Birmingham is producing 80kg. Hence justifying a posteriori my habit of removing earlier emails when replying to them. (It takes little effort to do so, especially in mailers where this feature can be set as the default option.)
about paradoxes
Posted in Books, Kids, Statistics, University life with tags bias, book review, email, Jacobian, Mark Chang, MLE, paradoxes, reparameterisation, scientific inference, The Bayesian Choice, unbiasedness on December 5, 2017 by xi'anAn 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:
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].
can you help?
Posted in Statistics, University life with tags AIC, Bayesian model choice, Bayesian model comparison, Bayesian predictive, DIC, email, model comparison, spams on October 12, 2013 by xi'anAn email received a few days ago:
Can you help me answering my query about AIC and DIC?
I want to compare the predictive power of a non Bayesian model (GWR, Geographically weighted regression) and a Bayesian hierarchical model (spLM).
For GWR, DIC is not defined, but AIC is.
For spLM, AIC is not defined, but DIC is.How can I compare the predictive ability of these two models? Does it make sense to compare AIC of one with DIC of the other?
I did not reply as the answer is in the question: the numerical values of AIC and DIC do not compare. And since one estimation is Bayesian while the other is not, I do not think the predictive abilities can be compared. This is not even mentioning my reluctance to use DIC…as renewed in yesterday’s post.
