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Random intercept models, which utilise maximum-likelihood estimation, provide more efficient estimates of effects with unbalanced data than the traditional repeated measures approach [ 81].
Our approach yields more efficient estimates compared to the maximum likelihood estimator, the least squares estimator and the weighted least squares estimator, which do not utilize the conditional moment restrictions.
Correction of that issue in the present work ensured that, at any positive correlation level, all the estimators tested here gave more efficient estimates than those obtained using simple random sampling.
This approach yields more efficient estimates under certain conditions.
However, a parametric model may lead to more efficient estimates than the Cox model under certain conditions.
A matched design often provides more efficient estimates (smaller variances) than an unmatched design given the same sample size.
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This latter method is less biased by random variation and therefore a more efficient estimate of the degree of explanation.
Samples are subsequently taken from each forest stratum to obtain a more efficient estimate of the total population.
This method provides a more efficient estimate of the intervention effect in pretest-post-test trials than traditional methods [ 74].
Variability within a relatively homogeneous block is less than the variability of the entire sample, resulting in a more efficient estimate than without blocking.
WDICH method is useful to estimate odds ratios and provides more efficient parameter estimates than DICH method when responses are continuous.
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