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Residual-Based Diagnostics for Structural Equation Models

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  • B. N. Sánchez
  • E. A. Houseman
  • L. M. Ryan

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  • B. N. Sánchez & E. A. Houseman & L. M. Ryan, 2009. "Residual-Based Diagnostics for Structural Equation Models," Biometrics, The International Biometric Society, vol. 65(1), pages 104-115, March.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:1:p:104-115
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01022.x
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    References listed on IDEAS

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    1. Houseman E.A. & Ryan L.M. & Coull B.A., 2004. "Cholesky Residuals for Assessing Normal Errors in a Linear Model With Correlated Outcomes," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 383-394, January.
    2. Zhiying Pan & D. Y. Lin, 2005. "Goodness-of-Fit Methods for Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 61(4), pages 1000-1009, December.
    3. Gerhard Arminger & Ronald Schoenberg, 1989. "Pseudo maximum likelihood estimation and a test for misspecification in mean and covariance structure models," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 409-425, September.
    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. Eberly, Lynn E. & Thackeray, Lisa M., 2005. "On Lange and Ryan's plotting technique for diagnosing non-normality of random effects," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 77-85, November.
    6. 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.
    7. D. Y. Lin & L. J. Wei & Z. Ying, 2002. "Model-Checking Techniques Based on Cumulative Residuals," Biometrics, The International Biometric Society, vol. 58(1), pages 1-12, March.
    8. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
    9. Raymond J. Carroll & David Ruppert & Ciprian M. Crainiceanu & Tor D. Tosteson & Margaret R. Karagas, 2004. "Nonlinear and Nonparametric Regression and Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 736-750, January.
    10. E. Andres Houseman & Louise Ryan & Brent Coull, 2004. "Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report," Harvard University Biostatistics Working Paper Series 1019, Berkeley Electronic Press.
    11. E. Andres Houseman & Brent A. Coull & Louise M. Ryan, 2006. "A functional-based distribution diagnostic for a linear model with correlated outcomes," Biometrika, Biometrika Trust, vol. 93(4), pages 911-926, December.
    12. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    13. Jason Roy & Xihong Lin, 2000. "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes," Biometrics, The International Biometric Society, vol. 56(4), pages 1047-1054, December.
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    Cited by:

    1. Gonzales Manteiga, Wenceslao & Maria Dolores, Martinez Miranda & Van Keilegom, Ingrid, 2012. "Goodness-of-fit Test in Parametric Mixed-Effects Models based on the Estimation of the Error Distribution," LIDAM Discussion Papers ISBA 2012022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Zhenzhen Zhang & Thomas M. Braun & Karen E. Peterson & Howard Hu & Martha M. Téllez-Rojo & Brisa N. Sánchez, 2018. "Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 634-650, December.
    3. Eleftherios Giovanis & Oznur Ozdamar, 2023. "Instrumental variables in structural equation modelling: an application on the impact of labour factors on health and standard of livings," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1083-1121, October.
    4. Jakob Peterlin & Nataša Kejžar & Rok Blagus, 2023. "Correct specification of design matrices in linear mixed effects models: tests with graphical representation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 184-210, March.
    5. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    6. Del Giudice, M. & Scuotto, V. & Garcia-Perez, A. & Messeni Petruzzelli, A., 2019. "Shifting Wealth II in Chinese economy. The effect of the horizontal technology spillover for SMEs for international growth," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 307-316.
    7. A. R. Abdul-Aziz & Albert Luguterah & Bashiru I. I. Saeed, 2021. "Comparative Performance of Estimation Maximization Among Residual Estimators: A Structural Equation Modelling Perspective," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 138-138, March.

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