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This method provides better prediction and coefficient estimates closer to the true values, than traditional stepwise regression approaches, which impose no regularisation.
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A rotated principal component analysis (RPCA) of both groundwater and precipitation data from October through April is used to define locations where aquifer levels show the greatest variability, with a stepwise regression approach used to define areas where rainfall and groundwater levels show the strongest association.
Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion.
Thirdly, we are well aware of the potential pitfalls [74] of the stepwise regression approach taken in our analysis.
This was carried out following a stepwise regression approach.
We will also apply a stepwise regression approach to assess robustness of our modeling strategy.
Final adjusted models were obtained with a forward stepwise regression approach.
One version was developed using a stepwise regression approach and the other using an item impact approach.
Second, a stepwise regression approach in both directions was applied including all variables, irrespective of their p-values in the univariate approach and interaction terms.
A backward stepwise regression approach was employed to eliminate all independent variables, for which the incremental F-test was not significant (p > 0.05) in both models.
Analyses for all SNPs were repeated adjusting for the most significant SNP to assess whether multiple independent causal variants were present (i.e. a forward stepwise regression approach).
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CEO of Professional Science Editing for Scientists @ prosciediting.com