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Comparing generalised maximum entropy and partial least squares methods for structural equation models

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  • Enrico Ciavolino
  • Amjad Al-Nasser

Abstract

The generalised maximum entropy (GME) method is presented for estimating structural equation models, where a real data set of the Service & Motor Vehicle Industry in Sweden is used to show the implementation of the method. Monte Carlo simulation comparisons are also made between GME and partial least squares (PLS) methods in the presence of messy data. Three cases are considered: outliers, missing data and multicollinearity. It is shown that this method can be considered a valid alternative to the conventional method of PLS, where the results of GME, in terms of mean squared error, outperform the PLS results in some respects.

Suggested Citation

  • Enrico Ciavolino & Amjad Al-Nasser, 2009. "Comparing generalised maximum entropy and partial least squares methods for structural equation models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(8), pages 1017-1036.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:8:p:1017-1036
    DOI: 10.1080/10485250903009037
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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    Cited by:

    1. Rosa Bernardini Papalia & Enrico Ciavolino, 2015. "Developing a composite index by using spatial latent modelling based on information theoretic estimation," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 989-997, May.
    2. Enrico Ciavolino & Maurizio Carpita & Mariangela Nitti, 2015. "High-order PLS path model with qualitative external information," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1609-1620, July.
    3. Fabio Pollice & Stefano De Rubertis & Enrico Ciavolino & Antonella Ricciardelli, 2011. "The incidence of regional factors on "competitive performance” of universities," Working Papers 37, AlmaLaurea Inter-University Consortium.
    4. Enrico Ciavolino & Sergio Salvatore & Piergiorgio Mossi & Gloria Lagetto, 2019. "High-order PLS path model for multi-group analysis: the prosumership service quality model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2371-2384, September.
    5. Enrico Ciavolino & Maurizio Carpita, 2015. "The GME estimator for the regression model with a composite indicator as explanatory variable," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 955-965, May.
    6. Rosa Bernardini Papalia & Enrico Ciavolino, 2011. "GME Estimation of Spatial Structural Equations Models," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 126-141, April.
    7. Maurizio Carpita & Enrico Ciavolino & Mariangela Nitti, 2019. "The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 287-305, November.
    8. Enrico Ciavolino & Antonio Calcagnì, 2014. "A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3401-3414, November.
    9. Amjad D. Al-Nasser, 2014. "Two steps generalized maximum entropy estimation procedure for fitting linear regression when both covariates are subject to error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1708-1720, August.
    10. E. Ciavolino & A. Calcagnì, 2015. "Generalized cross entropy method for analysing the SERVQUAL model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 520-534, March.

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