方法对比

	SCAD惩罚回归	偏最小二乘结构方程模型
领域	心理测量学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2001	1985
提出者	Jianqing Fan, Runze Li	Herman Wold
类型	Penalized regression with non-concave penalty	Component-based structural equation model
开创性文献	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.	Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445
别名	SCAD	PLS-SEM, PLS path modeling
相关	5	5
摘要	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.	PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data.