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We also note that in this case the performance of this estimator was slightly superior (smaller variance) to an alternative estimate also derived from truncated Poisson data (Zelterman's estimator).
This estimator was used by de Koning et al. (10 ), who estimated the overdiagnosis rate to be 3%.
James found that this estimator was a better predictor of future win-loss percentage than a team's actual win-loss percentage.
This estimator was first considered by Chang et al.[ 9] and further studied by Jung et al.[ 10] who showed this estimator was the uniformly minimum variance unbiased estimator (UMVUE).
This estimator was termed least absolute shrinkage and selection operator (LASSO) and has recently been suggested for whole-genome prediction [ 32, 40, 41].
To check the possibility that this estimator was biased by the training set of ancestral alternative exons, we repeated our analysis using the second training set of 215 ancestral alternative exons from Sorek et al [ 15].
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The (asymptotic) covariance of this estimator is estimated by evaluating at, where Δ = ∂κ/∂θ.
This estimator is then conveniently applied to maximum likelihood parameter estimation in nonlinear dynamical systems with measurements corrupted by output additive auto and crosscorrelated noise.
Each coefficient in BayesB was estimated from the posterior mean, and a property of this estimator is that all means are non-zero.
If there are too few markers, this estimator is suboptimal and some shrinkage is needed to better estimate the whole-genome relationship [ 12– 12].
An alternative estimate follows from a Bayesian approach, and for our model this estimator is (A2) var = (X ˘ ′ W X ˘ + λ D 2 T D 2 ) − 1.
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