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Local influence analysis of nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models

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  • Fu, Ying-Zi
  • Tang, Nian-Sheng
  • Chen, Xing

Abstract

Nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models are proposed to identify the relationship between manifest variables and latent variables in modern educational, medical, social and psychological studies. The nonignorable missing mechanism is specified by a logistic regression model. An EM algorithm is developed to obtain the maximum likelihood estimates of the structural parameters and parameters in the logistic regression model. Assessment of local influence is investigated in nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models on the basis of the conditional expectation of the complete-data log-likelihood function. Some local influence diagnostics are obtained via observations of missing data and latent variables that are generated by the Gibbs sampler and Metropolis-Hastings algorithm on the basis of the conformal normal curvature. A simulation study and a real example are used to illustrate the application of the proposed methodologies.

Suggested Citation

  • Fu, Ying-Zi & Tang, Nian-Sheng & Chen, Xing, 2009. "Local influence analysis of nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3671-3684, August.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:10:p:3671-3684
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    References listed on IDEAS

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    1. Sik-Yum Lee, 2006. "Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 541-564, September.
    2. Sik-Yum Lee & Hong-Tu Zhu, 2002. "Maximum likelihood estimation of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 189-210, June.
    3. Hong‐Tu Zhu & Sik‐Yum Lee, 2001. "Local influence for incomplete data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 111-126.
    4. Sik-Yum Lee & Nian-Sheng Tang, 2004. "Local influence analysis of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 573-592, December.
    5. Sik-Yum Lee & Xin-Yuan Song, 2004. "Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data," Biometrics, The International Biometric Society, vol. 60(3), pages 624-636, September.
    6. W.‐Y. Poon & Y. S. Poon, 1999. "Conformal normal curvature and assessment of local influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 51-61.
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    Cited by:

    1. Zhao Yuanying & Xu Dengke & Duan Xingde & Pang Yicheng, 2014. "Bayesian Subset Selection for Reproductive Dispersion Linear Models," Journal of Systems Science and Information, De Gruyter, vol. 2(1), pages 77-85, February.

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