Sentence examples for model estimation methods from inspiring English sources

Model estimation methods using such an L 1-norm measure of misfit have been found to perform well in geomagnetic field modelling applications (Lesur et al. 2008; Thomson and Lesur 2007; Walker and Jackson 2000).

Although the selection outcomes of the TIM and Wood model estimation methods may be the same, if we extrapolate this information further, the difference in the accuracy of the estimation will have an effect in the long-term.

We used the restricted maximum likelihood (REML) as our model estimation method and present the estimated means and difference between treatments and their respective standard error means (SEs).

This paper is organized as follows: In section 2, we present a brief introduction to the Fujisaki intonation model and the Mixdorff model estimation method.

Based on theoretical calculations, the Wood model estimation method can prove more sires (Table  2) than the TIM with the same TD sampling regime.

Similarly to the RSAT output, the results are provided to the final user as tables with a 'weight score' column measured by the 'Background model estimation method' (17).

The S-B chi-square test incorporates a scaling correction that considers the model, estimation method and kurtosis values, thereby producing a more trustworthy fit index [ 29].

Although the magnitude of survival estimates differed by model type, directionally, patients who received 5FU had better disease-free survival and overall survival than those who did not, regardless of model estimation method.

Position frequency matrices from both Jaspar (http://jaspar.cgb.ki.se/) and Transfac (http://www.gene-regulation.com/pub/databases.html) libraries were used in matrix-scan where the background model estimation method was based on a Markov order of 0. Organism-specific 'upstream no-orf' background sequences were used (galGal4) with a pseudo-frequency of 0.01 and an upper p-value threshold of 1e−4.

In case this parameter is not known, joint state and parameter-estimation methods can be employed, e.g., using interacting multiple-model estimation methods, or using an extended Kalman filter.

The second Section briefly reviews ordinal logistic [ 8] and multinomial logistic [ 9] models and their model assumptions, and also discusses model coefficient estimation methods for both models and the statistical test for checking the ordinal logistic assumption.