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Re: st: missing value problem in ml estimation
Thank you for your reply, Maarten.
I did end up using the "missing" option in -ml model and it worked
fine. Although I can submit my paper using this "missing" option, I
still feel troubled not knowing the answer of my following question.
What troubles me is that when I did not know the existence of this
"missing" option, I recoded the missing values from "." to 999999999
using "recode ts1 miss=999999999", thinking that it wouldn't matter.
Theoretically, it shouldn't matter because ts1 is only used when y ==
1. When y == 0, the value of ts1 does not enter the likelihood
function. I have made sure whenever y==1, ts1 has a valid value. And
whenever y==0, ts1 is missing. What troubles me is that when the
missing values of ts1 are coded as 2 or 999999999, it gives me
different estimation results.
Could it be that when ml calculates the gradient vector, it uses the
values of the variables that do not enter the likelihood function?
On 1/7/06, Maarten buis <email@example.com> wrote:
> Dear Lian,
> Am I understanding you correctly that you have missing values which you have given numerical
> values like 2 or 99999999? How did you tell Stata that a 2 or a 9999999 means missing? The
> standard code for missing in Stata is ".", if you want more than one missing code you could use
> ".a" till ".z". My guess is that Stata did not recognize your missing codes as missing and treated
> them as real values. see -help missing- for more information on missing observations in Stata.
> -help ml- tells you that the "missing" option in -ml model- "specifies that observations
> containing variables with missing values are not to be eliminated from the estimation sample",
> which is probably not what you want.
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
> visiting adress:
> Buitenveldertselaan 3 (Metropolitan), room Z214
> +31 20 5986715
> Lian Jian wrote:
> > I was using "lf" method to do an ml estimation with my own
> > evaluator (likelihood function). I am using the default
> > modified Newton-Raphson algorithm. In each of my observation,
> > there are some variables with missing values, but these
> > variables do not appear in my likelihood function. In other
> > words, theoretically these missing values should not matter. I
> > have tried using "missing" option in my model command, which
> > worked fine.
> > However, I also tried coding the missing values as different
> > numerical numbers, like 2 or 999999999. Surprisingly, I have
> > gotten different answers by coding the missing values
> > differently, which is utterly odd. As those missing values
> > should not enter my likelihood function at all.
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