Sentence examples for generalized regression models from inspiring English sources

Extended generalized regression models facilitate the estimation of the conditional mean, variance or mixture parameter as functions of covariates.

We examined a range of models, including six generalized regression models, four time series models, and three nonparametric models.

In generalized regression models, several approaches can accommodate this lack of independence: (a) random effects; (b) fixed effects; (c) marginal models in the non-Gaussian case; and (d) regression coefficient covariance matrix adjustment.

According to reported problems pointing to risk of dysphagia, about 40% of nursing home residents showed symptoms, as was similarly reported in the study of Humbert et al. 9 The variables that best explain the total score of the DST were similar in both classic and generalized regression models.

During the first stage, to estimate the number of deaths associated with seasonal and pandemic influenza and RSV, we fitted age-specific generalized regression models with a Poisson distribution and an identity link to the number of monthly deaths as previously described (14).

In our study breastfeeding seemed to have a protective effect on the upper percentiles of the BMI estimated by quantile regression (e.g. -0.41 for the 90th percentile, s. table 3), although generalized regression models and GAMLSS did not assess breastfeeding as being significantly associated with the mean BMI (although it was a significant predictor of σ).

Probit models and logistic regression models both belong to the generalized regression model family.

A generalized regression model is also provided by combining the available data of any cross-section.

A probit model is another generalized regression model that is commonly used to fit binary results.

This research utilized generalized regression modelling of lab measurements (from 81 asphalt mixtures) to develop and verify prediction models for |E∗| and δ using only nominal asphalt mix properties that are readily available during the initial mixture design and specification process.

GEE is a generalized regression model that is used to investigate continuous or logistic outcome variables in which the residuals are correlated [45], [46].