We now explore another approach to dealing with missing data, based on the maximum likelihood function and used in logistic regression.
The basic premise is that instead of imputing missing data values, we should estimate some population parameters by determining the value that maximizes the likelihood function (actually, the natural log of this function) based on the sample data we have.
Topics
- Basic Concepts and Objectives
- Initialization of FIML
- FIML using Solver
- Multiple Regression using FIML
- Real Statistics FIML data analysis tool
Links
References
Enders, C. K. (2001) The performance of the full information maximum likelihood estimator in multiple regression models with missing data. Educational and Psychological Measurement, Vol. 61 No. 5.
https://asu.elsevierpure.com/en/publications/the-performance-of-the-full-information-maximum-likelihood-estima
Allison, P. D. (2012) Handling missing data by maximum likelihood
https://statisticalhorizons.com/wp-content/uploads/MissingDataByML.pdf
Can I use FIML in a panel random effect model with N = 13 and T= 19. I have missing data about 40%. So, how can I deal with missing data without pairwise/listwise deletion? I tried xtdpdml but it is not executable with my data. An online consultant told me it is better when t < 10 and large N, which is not my case at all.
thanks in advance
Hello Nariman,
I don’t know whether FIML can address your situation. I suggest that you try it.
Charles
May I ask where I can see the application of FIML in logistic regression?
Sorry Kirin, but I haven’t yet implemented FIML for logistic regression.
Charles
Charles – I am just getting into this but, at least on the surface where I am at present, FIML is appropriate ONLY for linear models. More to come if I find differently. RJB