Sentence examples for more robust regression from inspiring English sources

While the current study only used caregiver characteristics as control variables, including frailty scores as a control variable might have yielded more robust regression models.

This technique also allows for the identification of a best model and creates regression coefficients based on all the possible models (model-averaged coefficients), which are more robust than typical regression coefficients.

A Bayesian network using all ten clinical and biological descriptors to give a prediction is more robust than logistic regression.

Bootstrapping (500 times) was applied in each try, to ensure more robust estimation of regression parameters, due to small sample size.

Standard Poisson log-normal regression is more robust than the negative binomial regression and the Chambers et al. (2014) estimates, but outperformed by the HQRPLN method.

As noted in [ 40] p. 280, "missing variables are often from severely ill persons on the skewed end of the distribution" thus nonparametric imputation methods could provide more robust results than regressions or maximum likelihood approaches.

Quasi-Poisson regression provides more robust SDs than the standard Poisson regression methods where there is evidence of over dispersion in the model parameters.

It can be seen that negative binomial regression is most vitiated by response outliers, this distortion increasing with C. The Chambers et al. (2014) estimates are more robust than negative binomial regression, but not as robust as the Aeberhard et al. (2014) estimates or the HQRPLN estimates.

In the specific application to supervised learning for convnets, a simple scale-invariant classification stage is more robust than multinomial logistic regression, appears to result in somewhat lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning.

This transformation made the regressions more robust to outlying data points that commonly cause a right-skewed distribution of cost data.

When such overdispersion exists, negative binomial regression employs a more robust method to fit count data than Poisson regression.