Evaluating Fit Indices for Multivariate t-Based Structural Equation Modeling with Data Contamination
In conventional structural equation modeling (SEM), with the presence of even a tiny amount of data contamination due to outliers or influential observations, normal-theory maximum likelihood (ML-Normal) is not efficient and can be severely biased. The multivariate-t-based SEM, which recently got im...
Main Authors: | Mark H. C. Lai, Jiaqi Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2017-07-01
|
Series: | Frontiers in Psychology |
Subjects: | |
Online Access: | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01286/full |
Similar Items
-
On the sensitivity of robust control charts in monitoring contaminated data
by: Kooi Huat Ng, et al.
Published: (2021-12-01) -
Multivariate outlier detection and robustness/
by: 529246 Hubert, Mia, et al. -
Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data
by: Olayemi Joshua Ibidoja, et al.
Published: (2023-02-01) -
Use the robust RFCH method with a polychoric correlation matrix in structural equation modeling When you are ordinal data
by: Omar Salim, et al.
Published: (2022-12-01) -
Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier
by: Magdalena Kircher, et al.
Published: (2023-02-01)