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In the table, ML denotes ML estimation using a sliding window with detections, and ML denotes ML estimation using a sliding window with a fixed length.
Given that the data appear normally distributed, maximum likelihood (ML) estimation was employed to estimate model parameters and fit indices.
Maximum likelihood (ML) estimation can be used to estimate the regression of y* on x.
The parameters in (1) are estimated using maximum likelihood (ML) estimation.
Beside the use of ML estimation, moment and quantile based estimators can also be considered.
The model parameters were estimated via maximum likelihood (ML) estimation, using AMOS 7.0.
The parameters of the model were estimated with maximum likelihood (ML) estimation.
If we apply classic maximum likelihood (ML) estimation, we can obtain smooth function estimates that are too rough.
Thus, all parameter estimates were obtained using restricted ML estimation.
The within-gene covariance matrix can be estimated by using maximum likelihood (ML) estimation.
Therefore, ML estimation is a straightforward instrument to derive stable, unbiased estimates in epidemiological studies with incomplete data.
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