IDEAS home Printed from https://ideas.repec.org/p/lmu/msmdpa/1394.html
   My bibliography  Save this paper

Modellselektion in Finite Mixture PLS-Modellen

Author

Listed:
  • Sarstedt, Marko
  • Salcher, André

Abstract

Der folgende Beitrag befasst sich mit dem Problem der Modellselektion im Finite Mixture Partial Least Squares (FIMIX-PLS)-Ansatz. Dieser Ansatz, welcher der Methodengruppe der Mischverteilungsmodelle zuzuordnen ist, ermöglicht eine simultane Schätzung der Modellparameter bei gleichzeitiger Ermittlung von Heterogenität in der Datenstruktur. Ein wesentliches Problem bei der Anwendung ist die Bestimmung der Anzahl der zugrunde liegenden Segmente, welche a priori unbekannt ist. Neben diversen statistischen Testverfahren wird zur Handhabung dieser Modellselektionsproblematik häufig auf so genannte Informationskriterien zurückgegriffen. Ziel des vorliegenden Beitrags ist es herauszuarbeiten, welches Informationskriterium für die Modellselektion in FIMIX-PLS besonders geeignet ist. Hierzu wurde eine Simulationsstudie initiiert, welche die Performanz gebräuchlicher Kriterien vor dem Hintergrund diverser Einflussfaktoren untersucht. Im Rahmen der Studie konnte mit dem Consistent Akaike’s Information Criterion (CAIC) ein Kriterium identifiziert werden, das die übrigen Kriterien in nahezu allen Faktorstufenkombinationen dominiert.

Suggested Citation

  • Sarstedt, Marko & Salcher, André, 2007. "Modellselektion in Finite Mixture PLS-Modellen," Discussion Papers in Business Administration 1394, University of Munich, Munich School of Management.
    • Handle: RePEc:lmu:msmdpa:1394
    as

    Download full text from publisher

    File URL: https://epub.ub.uni-muenchen.de/1394/1/2007-06-05_FIMIX_Working_Paper_final.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    2. Hawkins, Dollena S. & Allen, David M. & Stromberg, Arnold J., 2001. "Determining the number of components in mixtures of linear models," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 15-48, November.
    3. Kamel Jedidi & Harsharanjeet S. Jagpal & Wayne S. DeSarbo, 1997. "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, INFORMS, vol. 16(1), pages 39-59.
    4. Roland T. Rust & Duncan Simester & Roderick J. Brodie & V. Nilikant, 1995. "Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria," Management Science, INFORMS, vol. 41(2), pages 322-333, February.
    5. Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Josef A. Mazanec & Amata Ring, 2011. "Tourism Destination Competitiveness: Second Thoughts on the World Economic Forum Reports," Tourism Economics, , vol. 17(4), pages 725-751, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jan-Michael Becker & Christian Ringle & Marko Sarstedt & Franziska Völckner, 2015. "How collinearity affects mixture regression results," Marketing Letters, Springer, vol. 26(4), pages 643-659, December.
    2. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    3. Ringle, Christian M. & Sarstedt, Marko & Schlittgen, Rainer & Taylor, Charles R., 2013. "PLS path modeling and evolutionary segmentation," Journal of Business Research, Elsevier, vol. 66(9), pages 1318-1324.
    4. Joti kumari & Jai Kumar, 2023. "Influence of motivation on teachers’ job performance," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    5. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2014. "Identifying Small Market Segments with Mixture Regression Models," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 4(4), pages 812-812.
    6. Sarstedt, Marko & Radomir, Lăcrămioara & Moisescu, Ovidiu Ioan & Ringle, Christian M., 2022. "Latent class analysis in PLS-SEM: A review and recommendations for future applications," Journal of Business Research, Elsevier, vol. 138(C), pages 398-407.
    7. Fordellone, Mario & Vichi, Maurizio, 2020. "Finding groups in structural equation modeling through the partial least squares algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    8. Meng Li & Sijia Xiang & Weixin Yao, 2016. "Robust estimation of the number of components for mixtures of linear regression models," Computational Statistics, Springer, vol. 31(4), pages 1539-1555, December.
    9. V. Esposito Vinzi & L. Trinchera & S. Squillacciotti & M. Tenenhaus, 2008. "REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 439-458, September.
    10. Ringle, Christian M., 2006. "Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach," MPRA Paper 10734, University Library of Munich, Germany.
    11. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Determining the Number of Market Segments Using an Experimental Design," FEP Working Papers 263, Universidade do Porto, Faculdade de Economia do Porto.
    12. Kiero Guerra-Peña & Zoilo Emilio García-Batista & Sarah Depaoli & Luis Eduardo Garrido, 2020. "Class enumeration false positive in skew-t family of continuous growth mixture models," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-19, April.
    13. Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
    14. Julian Aichholzer & Sylvia Kritzinger & Carolina Plescia, 2021. "National identity profiles and support for the European Union," European Union Politics, , vol. 22(2), pages 293-315, June.
    15. Adrian Bruhin & Ernst Fehr & Daniel Schunk, 2019. "The many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences," Journal of the European Economic Association, European Economic Association, vol. 17(4), pages 1025-1069.
    16. Yu Ding & Wayne S. DeSarbo & Dominique M. Hanssens & Kamel Jedidi & John G. Lynch & Donald R. Lehmann, 2020. "The past, present, and future of measurement and methods in marketing analysis," Marketing Letters, Springer, vol. 31(2), pages 175-186, September.
    17. Jacky C. K. Ng & Joanne Y. H. Chong & Hilary K. Y. Ng, 2023. "The way I see the world, the way I envy others: a person-centered investigation of worldviews and the malicious and benign forms of envy among adolescents and adults," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    18. Gillian C. Williams & Karen A. Patte & Mark A. Ferro & Scott T. Leatherdale, 2021. "Associations between Longitudinal Patterns of Substance Use and Anxiety and Depression Symptoms among a Sample of Canadian Secondary School Students," IJERPH, MDPI, vol. 18(19), pages 1-14, October.
    19. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "On evasion behaviour in public transport: Dissatisfaction or contagion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 626-651.
    20. Mélissa Lemoine & Gerhard Gmel & Simon Foster & Simon Marmet & Joseph Studer, 2020. "Multiple trajectories of alcohol use and the development of alcohol use disorder: Do Swiss men mature-out of problematic alcohol use during emerging adulthood?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.

    More about this item

    Keywords

    FIMIX PLS; Model Selection; Finite Mixture; Partial Least Squares; PLS; Information Criteria;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:lmu:msmdpa:1394. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mareike Seifried (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.