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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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Il s'agit de prédire la valeur d'une variables qualitative, i.e., de mettre les individus dans des classes. (Par exemple : aide au diagnostic médical, reconnaissance des mauvais payeurs par une banque, etc.) On cherche des "fonctions linéaires discirminantes (des combinaisons linéaires dea variables, qui maximisent la variance interclasse et minimisent la variance intraclasse)

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There is a description in V&R2, pp. 237-8., given below. I guess I was teasing people to look up Hauck-Donner phenomenon in our index. (I seem to remember this was new to my co-author too, so you were in good company. This is why it is such a good example of a fact which would be useful to know but hardly anyone does. Don't ask me how I knew: I only know that I first saw this in about 1980.)

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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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There is a description in V&R2, pp. 237-8., given below. I guess I was teasing people to look up Hauck-Donner phenomenon in our index. (I seem to remember this was new to my co-author too, so you were in good company. This is why it is such a good example of a fact which would be useful to know but hardly anyone does. Don't ask me how I knew: I only know that I first saw this in about 1980.)

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