Maximum likelihood estimation is a method that determines values for the parameters of a model. The parameter values are found such that they maximize the likelihood that the process described by the model produced the data that were actually observed.
Full information maximum likelihood is an estimation strategy that allows for us to get parameter estimates even in the presence of missing data
Full information maximum likelihood (FIML) and maximum likelihood estimation (MLE) are both methods used to estimate parameters of a statistical model based on observed data. The main difference between them is that FIML uses all available information in the data, whereas MLE only uses a subset of the data.
FIML is a method that takes into account all observed data, including missing data. It estimates the parameters of a model by maximizing the likelihood function of the observed data, given the model parameters, using all available information. FIML is considered a more desirable method of estimation because it utilizes all available data and does not require the imputation of missing data. This makes it a more efficient and accurate way to estimate model parameters.
On the other hand, MLE is a method that uses only the observed data and does not account for missing data. It estimates the parameters of a model by maximizing the likelihood function of the observed data only. MLE is a widely used method because it is relatively simple and does not require complex computation.
In summary, FIML is a more sophisticated method that can produce more accurate estimates of parameters because it utilizes all available data, including missing data. MLE, on the other hand, is a simpler method that uses only observed data and is widely used because it is easy to implement.
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