Sentence examples for the estimation dataset from inspiring English sources

Instantaneous load estimates are derived from all observation in the estimation dataset using: hat{L}_{AMLE} = exp left( {a_{0} + sumlimits_{j = 1}^{M} {a_{j} X_{j} } } right)H a,b,s^{2},alpha,kappa ) (1 where ( hat{L}_{AMLE} ) is the AMLE instantaneous load estimate, a and b are explanatory variable functions, α and κ are gamma distribution parameters and s 2 is the residual variance [32 34].

No differences in patient characteristics were observed between the estimation dataset and validation dataset.

There were 2855 records in the estimation dataset and 1887 in the validation dataset.

Age was not correlated with the EQ-5D index in the estimation dataset.

This model explained 48% of the variation in the EQ-5D index in the estimation dataset and had an RMSE of 0.170.

A range of multiple variable models was constructed using the estimation dataset with a combination of these variables depending upon their importance based on adjusted R2 values.

However, it is an 'in-sample' measure which means that the expected prediction error is valid only for the exact same experimental conditions as of the parameter estimation dataset [ 10].

PNCCA was next performed on the whole estimation dataset, obtaining 5 SNP variables associated with all the phenotypical intermediate risk factors, this resulted in a model with a canonical correlation of 0.24.

The majority of published algorithms use linear regression by ordinary least squares to estimate the relationship between the QLQ-C30 and EQ-5D where both measures have been used on the same patient population (an estimation dataset).

The dataset was then divided in two by taking a random sample of 60% of the data and separating that data from the remaining 40% to provide an estimation dataset and a validation dataset, respectively.

75% of the data were used as an estimation dataset for building models, and 25% were used as a validation dataset.