Sentence examples for modelling error variance from inspiring English sources

The modelling error variance is also estimated during the inversion procedure.

This scheme firstly develops a fixed region model to select the best set of predictor variables for use in the subsequent regression analyses using an approach that minimises the model error variance while also satisfying a number of statistical selection criteria.

An estimator of the approximate out-of-sample model error variance in an estimate of AGB for a tree j with a known (measured) vector x j of explanatory variables is [70, ch.

Under a simple random sampling design, the model error variance in an estimate of a species specific AGB Mg ha−1 in a stratum or a population of interest is obtained by scaling an estimate of the average model error in a tree-level estimates of AGB with an estimate of stem density [40].

The model error variance in an estimate of AGB Mg ha−1 for species s is hereafter: (hat{lambda }_{s}^{2} tilde{V}left( {hat{{overline{{text{AGB}_{s} }} }}} right) + left( {hat{{overline{{text{AGB}_{s} }} }}} right)^{2} hat{V}left( {hat{lambda }_{s} } right)) [70, p. 228], if we assume a zero covariance between stem density and AGB.

Considering the larger contribution to the model error variance from the former, the overestimation is a concern.

Under an assumption of independence of model errors across species, the model error variance for a group or all species combined are computed as the sum of the variances of individual species.

The error variance in calculating the F-ratios for the other fixed effects (i.e. sex and host plant treatment) and the random effect of population was the model error variance.

An estimator of the approximate out-of-sample model error variance in an estimate of AGB for a tree j with a known (measured) vector x j of explanatory variables is [ 70, ch.

Under a simple random sampling design, the model error variance in an estimate of a species specific AGB Mg ha−1 in a stratum or a population of interest is obtained by scaling an estimate of the average model error in a tree-level estimates of AGB with an estimate of stem density [ 40].

A limitation of our approach is the increased complexity of modelling the error variance.