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st: Hierarchial Poisson Model and Semidefinite Negative Hessian
From
Simone Peart Boyce <[email protected]>
To
[email protected]
Subject
st: Hierarchial Poisson Model and Semidefinite Negative Hessian
Date
Wed, 1 Sep 2010 07:16:36 -0700 (PDT)
Hi,
I am trying to run a count data model on the effect of program participation
on the number of suspensions received by a student. I tried to run a
hierarchial
poisson model with students nested in schools, however, I received the below
error message:
xtmepoisson oss jumpstart propcat2 propcat3 propcat4 propcat5
logit || HS_attend_dur: , var
numerical derivatives are approximate
nearby values are missing
Iteration 0: log likelihood = -1868.4358 (not concave)
Hessian is not negative semidefinite
r(430);
I thought this might be due to the high percentage of zeroes that I have in my
dataset - over 75% - that indicates that none of these students were suspended.
(sum) oss
-------------------------------------------------------------
Percentiles Smallest
1% 0 0
5% 0 0
10% 0 0 Obs 2449
25% 0 0 Sum of Wgt. 2449
50% 0 Mean .3891384
Largest Std. Dev. .9137992
75% 0 7
90% 1 7 Variance .835029
95% 2 8 Skewness 3.341423
99% 4 8 Kurtosis 17.31081
However, I ran a similar regression using xtpoisson, normal re and I am
able to obtain results.
xtset HS
xtpoisson oss jumpstart propcat2 propcat3 propcat4 propcat5
logit , re normal
What explains this discrepancy if the models should be equivalent?
The negative binomial might be better suited for my data given the high
frequency of zeroes. Is there any code for a hierarchial negative binomial?
I wasn't able to find any.
Thanks in advance for your help,
Simone Boyce
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