Least Absolute Deviation Estimation in Structural Equation Modeling  

Oleh:

Siemsen, Enno ; Bolllen, Kenneth A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 36 no. 02 (Nov. 2007), page 227-265.
Topik: Least Absolute Deviation; Structural Equation Modeling; Robust Estimation; Small Sample Research
Fulltext: SMR vol.36 no.2 p.227 Nov 2007_win.pdf (397.45KB)

Ketersediaan

Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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