Sentence examples for distribution regression model from inspiring English sources

We formally apply the general theory to inference on regression coefficient process in the distribution regression model with a logistic link, where two implementations are analyzed in detail.

The model was defined as γ log link distribution regression model as the rate was expected to be positively skewed with an autoregressive first order matrix representing time dependence within the repeated subject.

Copula distributions are little used in health economics, and are mainly applied to specify the distribution of regression models with more than one dependent variable [ 20– 23].

Estimates are standardized to the WHO age distribution and regression models are also adjusted for age.

Poisson distribution based regression models are standard models for count data, including for infectious disease surveillance data [ 21].

For example, in dental caries research and many other areas, proportions of observations with zero counts are often higher than expected under the Poisson or negative binomial distributions, and regression models based on these distributions may result in biased estimates and poor predictions.

Note that the support of a skew-normal distribution is making it ideal for error distributions for regression models.

Partition curve modelling through particle size distribution and DOE regression model fitting allow a better understanding of the effect of wash water on the aforementioned phenomenon for each mineral fraction.

In order to normalize the distribution, the LOS regression model was developed without two outliers that had LOS of 45 and 62 days.

This enabled us to drawn conclusions that do not overestimate confidence in our results, and allowed the calculation of the full posterior marginal distribution of the regression model parameters.

Finally, conditional on σ h i, the prior distribution for the regression model parameters can be written as, (9) Pr (s h i,   Pa h i,   θ h i | σ h i ) = Pr s ª (s h i ) Pr (Pa h i | s h i ) Pr (θ h i | σ h i,   s h i,   Pa h i ).