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take a profile likelihood approach and replace η by its maximum likelihood estimate, η ^, which also minimises T 2. Asymptotic likelihood theory suggests that T 2 has a χ distribution on q − 1 degrees of freedom.
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The difficulty arises because it is not known, and we must replace it by its maximum-likelihood estimate, (which also minimizes X).
This computational burden is reduced by estimating by maximum likelihood, that is, based on, and.
Such prior information can be incorporated in our procedure without restricting its applicability by generalizing maximum likelihood estimation to maximum a-posterior estimation as discussed in the Additional file 1.
The between study variance (tau squared), was estimated by restricted maximum likelihood, and its significance are taken to test homogeneity of variances.
We investigate SMC methods where the proposal distribution is computed by maximum likelihood or by a linearization approach.
Parameter estimation is by maximum likelihood or by an extension of restricted maximum likelihood.
A phylogenetic tree was generated by maximum likelihood analysis by using PAUP (8 ).
The BL model was calibrated by the maximum likelihood method.
Regression parameters were estimated by the Maximum Likelihood Method.
Deconvolution was performed by the Maximum Likelihood Estimation (MLE) algorithm.
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