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SCAD Penalized Regression/Evidence
Method evidence record

SCAD Penalized Regression

SCAD (Smoothly Clipped Absolute Deviation) is a variable selection and regularization method developed by Fan and Li (2001) that addresses limitations of L1 penalization (lasso). SCAD uses a non-concave penalty that automatically performs variable selection while maintaining oracle properties: it recovers the true underlying model as if the true predictors were known in advance.

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Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Smoothly Clipped Absolute Deviation Penalized Regression
Taxonomic method record · latent-structure / psychometrics
  • Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360. · DOI 10.1198/016214501753382273
  • Zou, H., & Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models. Annals of Statistics, 36(4), 1509-1533. · DOI 10.1214/009053607000000802
  • Wang, H., Li, G., & Tsai, C. L. (2007). Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(1), 63-78. · DOI 10.1111/j.1467-9868.2007.00577.x
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Related methods

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Same method familyExploratory Structural Equation Modelingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMCP Penalized Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyMultiple Factor Analysismachine-suggested · Relational suggestion, not evidence.Same method familyPartial Least Squares Structural Equation Modelingmachine-suggested · Relational suggestion, not evidence.Same method familyRedundancy Analysismachine-suggested · Relational suggestion, not evidence.

Evidence status

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Sources

3 recorded citations, copied from the method source record.

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