So sánh phương pháp

	Hồi quy có phạt MCP	Mô hình cấu trúc phương trình thăm dò
Lĩnh vực	Trắc lượng tâm lý	Trắc lượng tâm lý
Họ	Latent structure	Latent structure
Năm ra đời	2010	2009
Người khởi xướng	Cun-Hui Zhang	Tihomir Asparouhov, Bengt Muthén
Loại	Penalized regression with minimax concave penalty	Hybrid exploratory-confirmatory factor modeling
Công trình gốc	Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942.	Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438.
Tên gọi khác	MCP	ESEM
Liên quan	4	5
Tóm tắt	MCP (Minimax Concave Penalty) is a variable selection method developed by Zhang (2010) that uses a concave penalty function for automated feature selection. Like SCAD, MCP addresses bias in lasso by avoiding shrinkage of large coefficients, but uses a different penalty shape that is computationally simpler than SCAD.	Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories.