## the worst possible proof [X’ed]

Posted in Books, Kids, Statistics, University life with tags , , , , , , on July 18, 2015 by xi'an

Another surreal experience thanks to X validated! A user of the forum recently asked for an explanation of the above proof in Lynch’s (2007) book, Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. No wonder this user was puzzled: the explanation makes no sense outside the univariate case… It is hard to fathom why on Earth the author would resort to this convoluted approach to conclude about the posterior conditional distribution being a normal centred at the least square estimate and with σ²X’X as precision matrix. Presumably, he has a poor opinion of the degree of matrix algebra numeracy of his readers [and thus should abstain from establishing the result]. As it seems unrealistic to postulate that the author is himself confused about matrix algebra, given his MSc in Statistics [the footnote ² seen above after “appropriately” acknowledges that “technically we cannot divide by” the matrix, but it goes on to suggest multiplying the numerator by the matrix

$(X^\text{T}X)^{-1} (X^\text{T}X)$

which does not make sense either, unless one introduces the trace tr(.) operator, presumably out of reach for most readers]. And this part of the explanation is unnecessarily confusing in that a basic matrix manipulation leads to the result. Or even simpler, a reference to Pythagoras’  theorem.

## beware, nefarious Bayesians threaten to take over frequentism using loss functions as Trojan horses!

Posted in Books, pictures, Statistics with tags , , , , , , , , , , , , on November 12, 2012 by xi'an

“It is not a coincidence that textbooks written by Bayesian statisticians extol the virtue of the decision-theoretic perspective and then proceed to present the Bayesian approach as its natural extension.” (p.19)

“According to some Bayesians (see Robert, 2007), the risk function does represent a legitimate frequentist error because it is derived by taking expectations with respect to [the sampling density]. This argument is misleading for several reasons.” (p.18)

During my R exam, I read the recent arXiv posting by Aris Spanos on why “the decision theoretic perspective misrepresents the frequentist viewpoint”. The paper is entitled “Why the Decision Theoretic Perspective Misrepresents Frequentist Inference: ‘Nuts and Bolts’ vs. Learning from Data” and I found it at the very least puzzling…. The main theme is the one caricatured in the title of this post, namely that the decision-theoretic analysis of frequentist procedures is a trick brought by Bayesians to justify their own procedures. The fundamental argument behind this perspective is that decision theory operates in a “for all θ” referential while frequentist inference (in Spanos’ universe) is only concerned by one θ, the true value of the parameter. (Incidentally, the “nuts and bolt” refers to the only case when a decision-theoretic approach is relevant from a frequentist viewpoint, namely in factory quality control sampling.)

“The notions of a risk function and admissibility are inappropriate for frequentist inference because they do not represent legitimate error probabilities.” (p.3)

“An important dimension of frequentist inference that has not been adequately appreciated in the statistics literature concerns its objectives and underlying reasoning.” (p.10)

“The factual nature of frequentist reasoning in estimation also brings out the impertinence of the notion of admissibility stemming from its reliance on the quantifier ‘for all’.” (p.13)

One strange feature of the paper is that Aris Spanos seems to appropriate for himself the notion of frequentism, rejecting the choices made by (what I would call frequentist) pioneers like Wald, Neyman, “Lehmann and LeCam [sic]”, Stein. Apart from Fisher—and the paper is strongly grounded in neo-Fisherian revivalism—, the only frequentists seemingly finding grace in the eyes of the author are George Box, David Cox, and George Tiao. (The references are mostly to textbooks, incidentally.) Modern authors that clearly qualify as frequentists like Bickel, Donoho, Johnstone, or, to mention the French school, e.g., Birgé, Massart, Picard, Tsybakov, none of whom can be suspected of Bayesian inclinations!, do not appear either as satisfying those narrow tenets of frequentism. Furthermore, the concept of frequentist inference is never clearly defined within the paper. As in the above quote, the notion of “legitimate error probabilities” pops up repeatedly (15 times) within the whole manifesto without being explicitely defined. (The closest to a definition is found on page 17, where the significance level and the p-value are found to be legitimate.) Aris Spanos even rejects what I would call the von Mises basis of frequentism: “contrary to Bayesian claims, those error probabilities have nothing to to do with the temporal or the physical dimension of the long-run metaphor associated with repeated samples” (p.17), namely that a statistical  procedure cannot be evaluated on its long term performance… Continue reading