Sentence examples for mean regression models from inspiring English sources

However, conditional mean regression models may be sensitive to response outliers.

However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response.

However, mean regression models may be sensitive to response outliers and provide no information on factors affecting other distributional points (e.g. upper and lower 5% quantiles) of the response.

In these standard parametric approaches, first, a point prediction for the future BMI value is estimated based on mean regression models with Gaussian distributed errors, then a symmetric PI is constructed around that point based on distributional assumptions.

14In particular, we estimate the following mean regression model: E Y | T, X = α i + θT + f quarter, α q.

We use a polynomial mean regression model (2) Eleft y|x,x^{2}right)=beta_{0}+beta_{1}x+beta_{2}x^{2}, where y is the daily snowfall (cm) and x is the maximum temperature (°C).

Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models.

The least squares method was performed on a polynomial mean regression model Eleft y|x,x^{2}right)=beta_{0}+beta_{1}x+beta_{2}x^{2}, (2) Fig. 1 a The daily snowfalls (cm) in Buffalo between January 1994 and January 2015 4478 dayss).

We compared the quantile regression estimates to the ones obtained by a standard mean regression model.

Multiple event analysis was performed with the use of the proportional means regression model (18).

A multivariate proportional means regression model was used to control for the duration of diabetes, duration of CAD, age, sex, and smoking history at baseline.