## conditional confusion [X validated]

Posted in Statistics with tags , on January 23, 2023 by xi'an

## Those who live by ChatGPT are destined to get advice of unpredictable quality

Posted in Statistics with tags , , , , , on January 18, 2023 by xi'an ## continuously tough [screenshots]

Posted in Books, Kids, Statistics, University life with tags , , , , , on December 27, 2022 by xi'an  As I was looking to a pointer on continuous random variables for an X validated question, I came across those two “definitions”…

## a [counter]example of minimaxity

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , on December 14, 2022 by xi'an A chance question on X validated made me reconsider about the minimaxity over the weekend. Consider a Geometric G(p) variate X. What is the minimax estimator of p under squared error loss ? I thought it could be obtained via (Beta) conjugate priors, but following Dyubin (1978) the minimax estimator corresponds to a prior with point masses at ¼ and 1, resulting in a constant estimator equal to ¾ everywhere, except when X=0 where it is equal to 1. The actual question used a penalised qaudratic loss, dividing the squared error by p(1-p), which penalizes very strongly errors at p=0,1, and hence suggested an estimator equal to 1 when X=0 and to 0 otherwise. This proves to be the (unique) minimax estimator. With constant risk equal to 1. This reminded me of this fantastic 1984 paper by Georges Casella and Bill Strawderman on the estimation of the normal bounded mean, where the least favourable prior is supported by two atoms if the bound is small enough. Figure 1 in the Negative Binomial extension by Morozov and Syrova (2022) exploits the same principle. (Nothing Orwellian there!) If nothing else, a nice illustration for my Bayesian decision theory course!

## observed vs. complete in EM algorithm

Posted in Statistics with tags , , , , , on November 17, 2022 by xi'an While answering a question related with the EM  algorithm on X validated, I realised a global (or generic) feature of the (objective) E function, namely that $E(\theta'|\theta)=\mathbb E_{\theta}[\log\,f_{X,Z}(x^\text{obs},Z|\theta')|X=x^\text{obs}]$

can always be written as $\log\,f_X(x^\text{obs};\theta')+\mathbb E_{\theta}[\log\,f_{Z|X}(Z|x^\text{obs},\theta')|X=x^\text{obs}]$

therefore always includes the (log-) observed likelihood, at least in this formal representation. While the proof that EM is monotonous in the values of the observed likelihood uses this decomposition as well, in that $\log\,f_X(x^\text{obs};\theta')=\log\,\mathbb E_{\theta}\left[\frac{f_{X,Z}(x^\text{obs},Z;\theta')}{f_{Z|X}(Z|x^\text{obs},\theta)}\big|X=x^\text{obs}\right]$

I wonder if the appearance of the actual target in the temporary target E(θ’|θ) can be exploited any further.