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Partial possibilistic regression path modeling: handling uncertainty in path modeling

Author

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  • Rosaria Romano

    (University of Naples Federico II)

  • Francesco Palumbo

    (University of Naples Federico II)

Abstract

The paper presents a new insight of a recently proposed method named partial possibilistic regression path modeling. This method combines the principles of path modeling with those of possibilistic regression to model the net of relations among blocks of variables, where a weighted composite summarizes each block. It assumes that randomness can refer back as the measurement error, which is the error in modeling the relations between the observed variables and the corresponding composite, and the vagueness to the structural error, which is the uncertainty in modeling the relations among the composites behind each block of variables. The comparison of the proposed method with a classical composite-based path model is based on a simulation study. A case study on the use of Wikipedia in higher education illustrates a fruitful usability context of the proposed method.

Suggested Citation

  • Rosaria Romano & Francesco Palumbo, 2021. "Partial possibilistic regression path modeling: handling uncertainty in path modeling," Computational Statistics, Springer, vol. 36(1), pages 615-639, March.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01026-7
    DOI: 10.1007/s00180-020-01026-7
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    References listed on IDEAS

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    1. Claes Cassel & Peter Hackl & Anders Westlund, 1999. "Robustness of partial least-squares method for estimating latent variable quality structures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 435-446.
    2. Michel Tenenhaus & Arthur Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Post-Print hal-00609220, HAL.
    3. Antoni Meseguer‐Artola & Eduard Aibar & Josep Lladós & Julià Minguillón & Maura Lerga, 2016. "Factors that influence the teaching use of Wikipedia in higher education," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1224-1232, May.
    4. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    5. Mohamed Hanafi, 2007. "PLS Path modelling: computation of latent variables with the estimation mode B," Computational Statistics, Springer, vol. 22(2), pages 275-292, July.
    6. Koenker, Roger, 2009. "The Median Is the Message: Wilson and Hilfertys Experiments on the Law of Errors," The American Statistician, American Statistical Association, vol. 63(1), pages 20-25.
    7. Michel Tenenhaus, 2011. "Regularized generalized canonical correlation analysis," Post-Print hal-00578321, HAL.
    8. Kim, Kwang Jae & Moskowitz, Herbert & Koksalan, Murat, 1996. "Fuzzy versus statistical linear regression," European Journal of Operational Research, Elsevier, vol. 92(2), pages 417-434, July.
    9. Arthur Tenenhaus & Michel Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 257-284, April.
    10. Pasquale Dolce & Vincenzo Esposito Vinzi & Natale Carlo Lauro, 2018. "Non-symmetrical composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 759-784, September.
    11. Dodge, Yadolah, 1997. "LAD Regression for Detecting Outliers in Response and Explanatory Variables," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 144-158, April.
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