Sentence examples for estimation of the weight from inspiring English sources

The univariate (q = 1) Wald test statistic is the ratio of the maximum likelihood estimation of the weight coefficient ŵ p + 1 to an estimate of its standard error.

The estimation of the weight of evidence gives the absolute explanatory power of each predictor but does not provide predictions of extinction nor colonisation.

An estimation of the weight was carried out via a simple observation of known functional attributes present between cancerous and non-cancerous genes.

Phylogenetic targeting boils down to two separate tasks: (1) estimation of the weight ω xy that measures the benefit or our amount of information contributed by including the comparison of species x with species y and (2) the identification of an optimal collection of pairs of species such that they represent independent measurements, i.e., the solution of the corresponding MPP.

In short this prediction is done as follows: the physiotherapist makes an estimation of the weight that can be lifted for 10 20 times; the number of repetitions that can be maximally performed is registered; the percentage of intensity can than be looked up in the Oddvar Holten diagram at the number of repetitions (figure 2) and 1RM can be computed by the formula (figure 3).

Obviously, this makes the pixel-by-pixel estimation of the weighting coefficient impractical.

Once the topology of the model is specified, the estimation of the weights of the networks can be done by means of different algorithms.

Estimation of the weights and weighted-comparisons between GXR and ATX were repeated for each replicate.

The approach includes empirical estimation of the weights through a bootstrap step which accounts for the variation in the estimated weights.

Our focus is on the weight of state transition features, because they account for a large proportion of the whole parameter set and good estimation of the weights are critical for successfully predicting TFBSs.

However, in the current case, the sample size is not considered large enough to (1) provide stable estimation of the weights in the test dataset; and (2) maintain adequate power in the validation dataset to test for the significance of β1.