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Open image in new window Fig. 7 Modelling and forecasting results by GM 1,1) model, regression model and elasticity coefficient model.
For example, the noisy OR-gate model, regression model and fuzzy model are proposed in many literatures [22, 23, 24, 25].
We developed a nonlinear model regression model selection technique that accommodates variables that differ only by a hyperparameter and can be adapted for various nonlinear model forms.
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As a validity check to the likelihood assumptions made by the linear mixed-effects model, regression models using generalized estimating equations (Diggle et al. 2002) in combination with robust standard error estimates were also fit.
There are many methods available in literature for prediction such as time series models, regression models, models based on Artificial Neural Network and Fuzzy logic.
Cox proportional hazards models (regression models often used to examine time-dependent factors) were then used to predict the time to publication of Cochrane reviews.
Figure 1 Illustration of Spatial Lag regression model vs. OLS regression model.
Probit models and logistic regression models both belong to the generalized regression model family.
The AUC was modelled using a linear regression model.
The same data has been considered to develop the traditional statistical model such as regression model and stochastic models.
In a similar manner, we define a location-scale regression model using the LPCNB regression model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com