Hi Farrukh,

Without data or detailed outputs, it is a tough question to answer but the first thing that comes into my mind in such a situation is that there is a low number of observations, at least for specific behaviours. For example, if you have thousands of individuals but only a few dozens that are relevant to calculate a coefficient in a regression, one may expect the S.E. of such a coefficient to be quite important.

Moreover, in case the residuals would not be normal, asymptotic formulas would not be valid anymore, so in addition to have a high standard error, these interval would not make any sense anymore.

If you want more comments/suggestions, I guess you will have to provide more details about these specific situations where "the S.E. become very large or NAN". R codes are welcome.

Best regards !

Are you trying to do logistic regression?  If so, Very large SE's can appear when there are perfect predictors.  This occurs because of a division-by-zero error; the information matrix blows up.

I USE ADEQUACY MODEL TO FIT THE NEW PROBABILITY MODEL ON APPROPRIATE DATA SETS,THEN I SUFFER THE FOLLOWING PROBLEM.

@some time when we are doing application with R, the S.E become very large or NAN. As such situation what we can do@

 One of my friend reply me that, you change initial value if problem still there then change data set

By using only one scale parameter in new model, we can reduce the S.E .

Are you running some sort of nonlinear optimization?

yes

yes