A Unified Maximum Likelihood Approach for Analyzing Structural Equation Models with Missing Nonstandard Data  

Oleh:

Sik-Yum, Lee ; Xin-Yuan, Song

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 35 no. 03 (Feb. 2007), page 352-381.
Topik: MCEM Algorithm; Gibbs Sampler; Bayesian Information Criterion; Path Sampling;
Fulltext: SMR vol.35 no.3 p.352 Feb 2007_win.pdf (260.33KB)

Ketersediaan

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

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Isi artikel

In this article, the authors present a unified approach for maximum likelihood analysis of structural equation models that involve subtle model formulations and nonstandard data structures. Based on the idea of data augmentation, they describe a generic Monte Carlo expectation-maximization algorithm for estimation. They propose path sampling for computing the observed data likelihood functions that usually involve complicated integrals and show how to apply this method for computing the Bayesian information criterion for model comparison. An application of the proposed unified approach to a two-level nonlinear structural equation model with missing continuous and ordered categorical data is presented. An illustrative example with a real data set is given.

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