Sentence examples for estimations of the regression from inspiring English sources

To test whether the differences between panel participants and non-participants might have biased the estimations of the regression analyses, we implemented a bivariate probit model with sample selection (which makes it possible to test for the presence of selection bias and to correct it, see the Appendix for a further explanation of this model) (Heckman, 1979, Greene, 2003, Obrizan, 2010).

To further improve the performance of the regression-based methods, we propose shrinkage regression-based methods which use a shrinkage estimator to replace the least square estimator for the estimation of the regression coefficients in the regression model.

As an external validating measure we compared the estimated parametric curve with nonparametric estimation of the regression function and calculated its derivatives with kernel regression estimators and automatically adapted local plug-in bandwidth function.

When the data used to fit a nonparametric regression model are contaminated with outliers, we need to use a robust estimator of scale in order to make robust estimation of the regression function possible.

This is accomplished without prior estimation of the regression function.

This paper considered the estimation of the regression parameters of a general probit regression model.

All things being equal such a condition would lead to biased estimation of the regression equation.

Their dependence on the CDH setting parameters was next examined using the empirical estimation of the regression equation.

Thus, an individual who works throughout the calendar year contributes with twelve observations to the estimation of the regression coefficient.

Multivariate regression analyses comprised the following working steps [27]: Setting the framework for the regression model, estimation of the regression function, verification of the regression function and coefficients as well as verification of the underlying assumptions of the regression model.

The choice of values of the covariates for a given set-up attaining minimum variance for estimation of the regression parameters has attracted attention in recent times.