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:

# Le jeu est-illegale dans l'Illinois

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# tirages au sort irlande

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.

# 76ers tirages au sort 2018

C'est en particulier ce que l'on utilise si Y est qualitative. Dans ce cas, on peut chercher P(y=0) ; mais comme une probabilitÃ© est toujours comprise entre 0 et 1, on n'arrivera pas Ã  l'exprimer comme combinaison linÃ©aire de variables quantitatives auxquelles on ajoute du bruit. On applique alors Ã  cette probabilitÃ© une bijection g entre l'intervalle [0;1] et la droite rÃ©elle (on dit que g est un lien). On essaye alors d'exprimer g(P(y=0)) comme combinaison linÃ©aire des variables prÃ©dictives.

# jeux de tirages au sort 8fuse

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.