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prediction model specification methods, and that using “hybrid” combination factor/shrinkage methods often yields superior …-spread variables in nonlinear prediction model specification. …
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In this paper, the cross-validation methods namely the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$C_{p}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>C</mi> <mi>p</mi> </msub> </math> </EquationSource> </InlineEquation>, PRESS and GCV are presented under the multiple linear regression model when multicollinearity exists and additional information imposes restrictions among the parameters that should hold in exact terms. The selection...</equationsource></equationsource></inlineequation>
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Large-n studies of conflict have produced a large number of statistically significant results but little accurate guidance in terms of anticipating the onset of conflict. The authors argue that too much attention has been paid to finding statistically significant relationships, while too little...
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with a new class of loss-based cross-validated algorithms in prediction of univariate and multivariate outcomes …
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separately in the statistical literature, including multivariate outcome prediction and density estimation based on either … in genomic data analysis: the prediction of biological and clinical outcomes (possibly censored) using microarray gene …
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-validated epsilon-net estimation methodology that covers a broad class of estimation problems, including multivariate outcome prediction …
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