Sentence examples for use of regression methods from inspiring English sources

The efficiency of our method is largely due to the use of regression methods with variable selection in graphical Granger modeling (i.e. Lasso and its variant).

In recent years, the focus on early clinical predictors [ 15, 16] and the use of regression methods to evaluate multiple predictors has increased [ 15- 17].

We use three types of regression methods: ordinary least squares (OLS), IV, and sibling fixed effect (FE) to establish a relation between BMI and hours of work.

A direct comparison between the models produced in this work and other models predicting Vss reported in the literature is complicated by the differences in the datasets and types of regression methods used.

The nature of nested data makes the use of traditional regression methods inappropriate: some of the assumptions inherent in traditional regression methods, including the assumption of independence among individuals within the same group and the assumption of equal variance across groups are not valid in the case of nested data [ 26].

The use of robust regression methods allows reduction of the contribution of extreme data points in a data set.

Compared to previous studies, the multidimensional measurement of risk perception and sense of place, and the use of multiple regression methods, provide some new ideas that can be a reference for similar research and enhance our understanding of farming households' disaster preparedness behaviors and their influencing factors in disaster threat areas.

In the field of medical data processing, the theoretical basis for the use of statistical regression methods is the "law of large numbers".

The purpose of this study was to examine the use of beta regression methods to analyze longitudinal HRQL data.

The common rationale behind the use of spatial regression methods is to minimise the effect of spatial autocorrelation, as was illustrated by the included studies.

6 In such situations, use of standard regression methods to develop risk models could accurately predict outcomes for patients in the dataset used to develop the model, but may often perform less well in a new patient group.