Sentence examples for more auxiliary variables from inspiring English sources

While the D estimator is used when an auxiliary variable (proxy) strictly resembles the survey variable, the GREG estimator is used when one or more auxiliary variables are strongly correlated with the survey variable.

However, it is common to have available one or more auxiliary variables that are correlated, at least to some extent, with the target variable and can be measured relatively easily.

Design-based sampling may be aided also through use of one or more auxiliary variables through which the statistical efficiency of the inventory may be improved, that is, the precision of the estimates increased (Gregoire and Valentine 2008, Chap. 6).

In the case of model-assisted estimation, other models may be used that employ the full power of linear or non-linear regression analysis with one or more auxiliary variables as appropriate.

Using ground-based measurements of the target variable and one or more auxiliary variables obtained from remote imagery of the measurement sites, research then develops a working model system to predict the target variable from the auxiliary variable(s).

Recently, much research effort has revolved around the use of newly available airborne or satellite remote sensing technologies that provide values of one or more auxiliary variables (Köhl et al. 2006; Falkowski et al. 2009); in particular, laser imagery has received much attention for forest inventory.

A model for the relationship between the target variable and one or more auxiliary variable(s) can adequately conform to the trend in Y. Auxiliary data are commonly available for all population elements.

By making full use of the Finsler lemma associated with Projection lemma, two further improved energy-to-peak filtering methods are obtained, where more auxiliary slack variables are introduced to provide extra free degrees.

More research on auxiliary variables in multiple imputation should be performed.

On the basis of the empirical values of relative bias and relative root mean squared error it was concluded that universal kriging and cokriging were more suitable in the presence of strong spatial autocorrelation of the forest variable, while locally weighted regression and k-nearest neighbors were more suitable when the auxiliary variables were well correlated with the response variable.

This relativizes recommendations from other sources, who mostly recommend including more rather than fewer auxiliary variables.