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We test the theoretical model using partial least squares (PLS) on survey data collected from the Australian mining and resources sector.
Data from 317 firms located in ten countries across three industries are analyzed to test the research model using partial least squares.
For the construction of the multivariate calibration model using partial least squares (PLS), initially all standard spectra were evaluated by principal component analysis (PCA) with the purpose of observing their distribution and the existence of clusters and outliers.
The same spectral preprocessing protocol was used to generate a quantitative predictive model using partial least squares (PLS) regression analysis.
To obtain the molecular signature we developed a model using partial least square discriminant analysis (PLS-DA) method [ 7].
A more conventional sensitivity analysis of this model, using partial rank correlation coefficients, is reported in [ 9], and was based on 1000 runs of the model.
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Since Cox proportional hazards model used partial likelihood function, temporal bias was avoided.
Enzyme activity levels were modeled using partial least-squares regression (PLS).
Quantitative calibration models using partial least squares (PLS) regression were developed and compared.
The solubility in each mixture region was analysed by fitting quadratic models using partial least squares analysis.
The data was modeled using partial least-squares (PLS) regression and resulted in a robust model with a root mean-square error of prediction of 2.5%.
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model using fractional
model using localized
models using partial
model using imperfect
specimen using partial
model using standard
model using several
model using perceptual
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model using frequency-domain
model incorporating partial
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model using regional
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