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There is one fairly common circumstance in which both convergence problems and the Hauck-Donner phenomenon (and trouble with \sfn{step}) can occur. This is when the fitted probabilities are extremely close to zero or one. Consider a medical diagnosis problem with thousands of cases and around fifty binary explanatory variables (which may arise from coding fewer categorical factors); one of these indicators is rarely true but always indicates that the disease is present. Then the fitted probabilities of cases with that indicator should be one, which can only be achieved by taking \hat\beta_i = \infty. The result from \sfn{glm} will be warnings and an estimated coefficient of around +/- 10 [and an insignificant t value].
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> From <@uconnvm.uconn.edu:kent@darwin.eeb.uconn.edu> Wed Jan 7 12:51 GMT 1998 > To: ripley@stats.ox.ac.uk (Prof Brian Ripley) > Cc: s-news@utstat.toronto.edu > Subject: Re: Summary of Robust Regression Algorithms > From: kent@darwin.eeb.uconn.edu (Kent E. Holsinger) > > >>>>> "Brian" == Prof Brian Ripley writes: > > Brian> My best example of this not knowing the literature is the > Brian> Hauck-Donner (1977) phenomenon: a small t-value in a > Brian> logistic regression indicates either an insignificant OR a > Brian> very significant effect, but step.glm assumes the first, > Brian> and I bet few users of glm() stop to think. > > All right I confess. This is a new one for me. Could some one explain > the Hauck-Donner effect to me? I understand that the t-values from > glm() are a Wald approximation and may not be terribly reliable, but I > don't understand how a small t-value could indicate "either an > insignificant OR a very significant effect." > > Thanks for the help. It's finding gems like these that make this group > so extraordinarily valuable.

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There is a little-known phenomenon for binomial GLMs that was pointed out by Hauck & Donner (1977: JASA 72:851-3). The standard errors and t values derive from the Wald approximation to the log-likelihood, obtained by expanding the log-likelihood in a second-order Taylor expansion at the maximum likelihood estimates. If there are some \hat\beta_i which are large, the curvature of the log-likelihood at \hat{\vec{\beta}} can be much less than near \beta_i = 0, and so the Wald approximation underestimates the change in log-likelihood on setting \beta_i = 0. This happens in such a way that as |\hat\beta_i| \to \infty, the t statistic tends to zero. Thus highly significant coefficients according to the likelihood ratio test may have non-significant t ratios.

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Depuis 2014, en France, il n'est plus obligatoire de passer par un huissier de justice pour déposer le règlement et le résultat d'un jeu concours/loto/tombola/loterie. Afin d'éviter les litiges il est conseillé de rédiger un règlement, et pour prouver le tirage au sort, le plus facile est de le filmer/caster en direct (facile sur Youtube, Instagram, Twitter ou Facebook).
L'outil est travaillé aussi bien pour un petit nombre de tirages, 2 à 30 tirages, que pour les longues séries 40 à 10 000 tirages. Pour les grands nombres de tirages, l'exécution peut prendre un peu de temps et si le chiffre est trop grand mettre votre navigateur en difficulté; c'est pourquoi nous vous recommandons de ne pas faire de trop nombreux tirages à la fois.
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To expand a little, if |t| is small it can EITHER mean than the Taylor expansion works and hence the likelihood ratio statistic is small OR that |\hat\beta_i| is very large, the approximation is poor and the likelihood ratio statistic is large. (I was using `significant' as meaning practically important.) But we can only tell if |\hat\beta_i| is large by looking at the curvature at \beta_i=0, not at |\hat\beta_i|. This really does happen: from later on in V&R2:

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knnTree Construct or predict with k-nearest-neighbor classifiers, using cross-validation to select k, choose variables (by forward or backwards selection), and choose scaling (from among no scaling, scaling each column by its SD, or scaling each column by its MAD). The finished classifier will consist of a classification tree with one such k-nn classifier in each leaf.

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