Sentence examples for matrix regression technique from inspiring English sources
GDM is a matrix regression technique that predicts biotic dissimilarity (turnover) between sites based upon environmental dissimilarity and geographic distance.
GDM is a versatile technique that was originally developed as a matrix regression technique used to study species beta diversity [ 36].
GDM is a matrix regression technique that predicts biotic dissimilarity across the landscape based on the matrix correlation between biotic dissimilarity and environmental dissimilarity plus geographic distance between sites where the species has been sampled.
To predict the distribution of environmentally associated genetic and phenotypic variation across the landscape, we used GDM (Ferrier et al. 2007), a matrix regression technique that predicts biotic dissimilarity (i.e. beta-diversity) between sites based upon environmental dissimilarity and geographic distance.
A design-of-experiments was used to enable broad-range experimental matrix and carefully selected regression technique was used to develop friction models and friction maps depending on the most relevant contact parameters (p, v, T, Ra).
QAP is a multiple regression technique able to regress a valued matrix on other matrixes and does not assume independence of observations [ 21].
PLS is a multivariate linear regression technique between an input (predictor) matrix, X, and an output response matrix, Y.
More technically, OPLS is a regression technique that correlates X matrix (spectral data) with a continuous response y (measured time) trying to find the regularities in the X data that better correlate with y.
Partial least squares (PLS) regression is a powerful regression technique that uses the latent variable approach to find the fundamental relations between two matrices (X and Y) [ 21– 23].
The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT).
Apply a linear regression technique to estimate PLE.
