Dear R helpers,

I need to estimate a probit model with box constraints placed on several of
the model parameters.  I have the following two questions:

1) How are the standard errors calclulated in glm
(family=binomial(link="probit")?  I ran a typical probit model using the
glm probit link and the nlminb function with my own coding of the
loglikehood, separately. As nlminb does not produce the hessian matrix, I
used hessian (numDeriv) to calculate it.  However, the standard errors
calculated using hessian function are quite different from the ones
generated by the glm function, although the parameter estimates are very
close.  I was wondering what makes this difference in the estmation of
standard errors and how this computation is carried out in glm.


2) Does any one know how to estimate a constrained probit model in R (to be
specific, I need to restrain the range of three parameters to [-1,1])?
Among the optimation functions, so far nlminb and spg work for my problem,
but neither produces a hessian matrix.  As I mentioned above, if I use
hessian funciton and calculate standard errors manually, the standard
errors seem not right.

Many thanks in advance for your kind help.

Maomao

	[[alternative HTML version deleted]]

