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Case-deletion diagnostics for nonlinear structural equation models

delete2003-07-01
delete10
PRE
AI
B
Bin Lu
DOI：10.1207/S15327906MBR3803_05delete
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Abstract

Abstract

En 中文
In this article, a case-deletion procedure is proposed to detect influential observations in a nonlinear structural equation model. The key idea is to develop the diagnostic measures based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. An one-step pseudo approximation is proposed to reduce the computational burden. Building blocks in the diagnostic measures are computed via the observations generated by the MH algorithm. Results from a simulation study and an illustrative real example are presented.
Keywords:
MAXIMUM-LIKELIHOOD
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Journal

M
Multivariate Behavioral Research
IF:
3.5
Papers:
1.8K
Citations:
9.4K

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