logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Least absolute deviation estimation in structural equation modeling

delete2007-11-01
delete14
PRE
AI
E
Enno Siemsen *
K
Kenneth A. Bollen
DOI：10.1177/0049124107301946delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Least absolute deviation (LAD) is a well-known criterion to fit statistical models, but little-is known about LAD estimation in structural equation modeling (SEM). To address this gap, the authors use the LAD criterion in SEM by minimizing the sum of the absolute deviations between the observed and the model-implied covariance matrices. Using Monte Carlo simulations, the authors compare the performance of this LAD estimator along several dimensions (bias, efficiency, convergence, frequencies of improper solutions, and absolute percentage deviation) to the full information maximum likelihood (ML) and unweighted least squares (ULS) estimators in structural equation modeling. The results for LAD are mixed: There are special conditions under which the LAD estimator outperforms ML and ULS, but the simulation evidence does not support a general claim that LAD is superior to ML and ULS in small samples.
Keywords:
least absolute deviation
structural equation modeling
robust estimation
small sample research
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

S
Sociological Methods and Research
IF:
6.5
Papers:
1.2K
Citations:
8.6K

Organization

No organization information available