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Therefore missing data for all further analyses were handled using the full information maximum likelihood (FIML) method.
Here the transition functions for mortality and basal area were fitted simultaneously using the full information maximum likelihood (FIML) in order to obtain estimates that are consistent and efficient and contribute in increased precision of model predictions.
The transition functions for mortality and basal area were fitted simultaneously, using the full information maximum likelihood, which produced consistent and efficient estimates contributing to increased precision of model predictions.
Missing values were handled using the full information method described in [17], which allows all available data to be incorporated in the estimation process while taking no active steps toward estimating missing values.
Missing data were handled using the full information maximum likelihood procedure.
We imputed missing data using the full information maximum-likelihood method (Arbuckle 1996).
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The advantage with fractional polynomial functions is that they use the full information and search for the optimal functional form within a flexible class of functions [26]. .
The advantage with fractional polynomial functions is that they use the full information and search for the optimal functional form within a flexible class of functions (Royston and Sauerbrei 2008).
To optimize the data, we used the full information estimator.
Firstly, the calculation did not use the full information of the data.
First, we use the full information of all women available in the data for our calculations.
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