Sentence examples for multivariate regression such from inspiring English sources

Furthermore, the results were compared to those produced from classical linear multivariate regression such as partial least squares regression (PLS).

This statistical problem has been considered before, and several methods based on the multivariate regression theory, such as partial least square regression, (PLSR) and principal component regression (PCR) have been used for such situations.

This problem was solved using multivariate regression techniques such as partial least squares (PLS).

For quantitative calibration, multivariate regression methods such as partial least squares (PLS) [42 44] have been used to develop NIR-based models for characterization of heritage materials extensively.

It combines a matching design with a multivariate regression analysis, such that the evaluation draws on a well-comparable control group for the treated students.

While multivariate regression methods such as lasso and ridge regression have been applied for classical association mapping (Hoggart et al., 2008; Malo et al., 2008; Shi et al., 2007; Wu et al., 2009), to our knowledge, our work is the first to consider a multivariate regression framework in an association study involving multiple populations.

We compare the results from our proposed methods, GcFlasso, GwFlasso and GwFlasso, with the ones from the single-marker analysis as well as multivariate regression methods such as ridge regression and lasso that do not use any structural information in the phenotypes.

We thus used techniques of multivariate regression to adjust for such risk factors to the extent possible in analyzing the relationship between duration of daily filgrastim prophylaxis and outcomes of interest.

However, it is difficult to predict rock properties due to the form and spatial distribution of these heterogeneities, also the applicability of traditional analytical techniques such as multivariate regression are limited in this context.

Kowalenko (2014) argues that cp-laws are best understood by drawing on methods from statistics, such as multivariate regression analysis (for a discussion of similar statistical approaches see Hüttemann and Reutlinger 2013, Reutlinger 2014, Roberts 2014, and Strevens 2014).

Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures.