On 10 Apr 2004 at 9:37, Roger Levy wrote: > Richard Ulrich <[EMAIL PROTECTED]> wrote in message
. . . > Each of my covariates is three-valued. So the situation for which ML > and exact logistic regression were giving me substantially different > results was with a half-dozen covariates, i.e. 3^6=729 possible > covariate vectors, and 300 datapoints, therefore the covariate space > was sparsely populated. I was not including any interaction terms, > and in most cases each datapoint had a unique set of predictor values, > so there were only seven parameters in my model and overfitting is > almost certainly not an issue. > > So to restate my confusion, what I don't understand is the technical > reason why asymptotic ML estimates for parameter confidence intervals Depends on the asymptotics you use. A direct normal approximation for the distribution of the ML estimator might be bad, but you can instead use a chisquare approximation for -2 log likelihood difference (deviance). With modern, fast computers, that is practicable, with likelihood profiling. Likelihood profiling for instance is implemented in R. Kjetil Halvorsen > and p-values would be unreliable in such a situation, since sample > size is relatively large in absolute terms. > > Many thanks for the help. > > Best, > > Roger > . > . > ================================================================= > Instructions for joining and leaving this list, remarks about the > problem of INAPPROPRIATE MESSAGES, and archives are available at: . > http://jse.stat.ncsu.edu/ . > ================================================================= . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
