So sánh phương pháp

	Hồi quy có phạt SCAD	Mô hình hóa phương trình cấu trúc Bình phương tối thiểu riêng phần
Lĩnh vực	Trắc lượng tâm lý	Trắc lượng tâm lý
Họ	Latent structure	Latent structure
Năm ra đời	2001	1985
Người khởi xướng	Jianqing Fan, Runze Li	Herman Wold
Loại	Penalized regression with non-concave penalty	Component-based structural equation model
Công trình gốc	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
Tên gọi khác	SCAD	PLS-SEM, PLS path modeling
Liên quan	5	5
Tóm tắt	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.