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 cemile ceren sonmez posted on Thursday, June 15, 2017 - 2:36 pm

Is there a way of using the penalized likelihood estimator when running a complex model in Mplus?

We are working with rare events data with binary outcome variable (suicide plan or attempt) with 10-15 independent variables, 5 of which are binary. The cell sizes are unequal, some of our cell sizes are as small as 1-5 and there is one empty cell, some are as large as 300.

Any guidance would be much appreciated.

Thank you very much!
 Tihomir Asparouhov posted on Friday, June 16, 2017 - 8:50 am
Penalized likelihood methods are similar to Bayes estimation where the role of the penalty is taken by the prior, so I would recommend Bayes estimation.

In some situations we also have prior in the ML context see page 11
but I am not sure if you could utilize this.

From your description however I am not sure what methods you have tried and how they failed - I think ML should be fine as well without penalty. Also note that no statistical methodology can "fix" the data. If the events are rare, statistical methodology is not a substitute for the lack of information.
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