IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v95y2011i2p205-217.html
   My bibliography  Save this article

A weighted least-squares approach to clusterwise regression

Author

Listed:
  • Rainer Schlittgen

Abstract

No abstract is available for this item.

Suggested Citation

  • Rainer Schlittgen, 2011. "A weighted least-squares approach to clusterwise regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 205-217, June.
    • Handle: RePEc:spr:alstar:v:95:y:2011:i:2:p:205-217
    DOI: 10.1007/s10182-011-0155-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10182-011-0155-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10182-011-0155-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    2. Lau, Kin-nam & Leung, Pui-lam & Tse, Ka-kit, 1999. "A mathematical programming approach to clusterwise regression model and its extensions," European Journal of Operational Research, Elsevier, vol. 116(3), pages 640-652, August.
    3. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    4. Hennig, Christian, 2003. "Clusters, outliers, and regression: fixed point clusters," Journal of Multivariate Analysis, Elsevier, vol. 86(1), pages 183-212, July.
    5. T. Rolf Turner, 2000. "Estimating the propagation rate of a viral infection of potato plants via mixtures of regressions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 371-384.
    6. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    7. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    8. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
    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. 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.
    2. 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.

    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. Sanjeena Subedi & Antonio Punzo & Salvatore Ingrassia & Paul McNicholas, 2015. "Cluster-weighted $$t$$ t -factor analyzers for robust model-based clustering and dimension reduction," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 623-649, November.
    2. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    3. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    4. Sanjeena Subedi & Antonio Punzo & Salvatore Ingrassia & Paul McNicholas, 2013. "Clustering and classification via cluster-weighted factor analyzers," 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. 7(1), pages 5-40, March.
    5. Omerovic, Sanela & Friedl, Herwig & Grün, Bettina, 2022. "Modelling Multiple Regimes in Economic Growth by Mixtures of Generalised Nonlinear Models," Econometrics and Statistics, Elsevier, vol. 22(C), pages 124-135.
    6. Proust-Lima, Cécile & Philipps, Viviane & Liquet, Benoit, 2017. "Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i02).
    7. Abhinandan Dalal & Diganta Mukherjee & Subhrajyoty Roy, 2020. "The Information Content of Taster's Valuation in Tea Auctions of India," Papers 2005.02814, arXiv.org.
    8. Joki, Kaisa & Bagirov, Adil M. & Karmitsa, Napsu & Mäkelä, Marko M. & Taheri, Sona, 2020. "Clusterwise support vector linear regression," European Journal of Operational Research, Elsevier, vol. 287(1), pages 19-35.
    9. Ian Wadsworth & Lisa V. Hampson & Thomas Jaki & Graeme J. Sills & Anthony G. Marson & Richard Appleton, 2020. "A quantitative framework to inform extrapolation decisions in children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 515-534, February.
    10. Utkarsh J. Dang & Antonio Punzo & Paul D. McNicholas & Salvatore Ingrassia & Ryan P. Browne, 2017. "Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 4-34, April.
    11. Angelo Mazza & Antonio Punzo, 2020. "Mixtures of multivariate contaminated normal regression models," Statistical Papers, Springer, vol. 61(2), pages 787-822, April.
    12. D'Urso, Pierpaolo & Santoro, Adriana, 2006. "Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 287-313, November.
    13. Spindler, M., 2014. "“They do know what they are doing ... at least most of them.†Asymmetric Information in the (private) Disability Insurance," Health, Econometrics and Data Group (HEDG) Working Papers 14/16, HEDG, c/o Department of Economics, University of York.
    14. Nguyen, Hien D. & McLachlan, Geoffrey J., 2016. "Linear mixed models with marginally symmetric nonparametric random effects," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 151-169.
    15. Wayne S. DeSarbo & Qian Chen & Ashley Stadler Blank, 2017. "A Parametric Constrained Segmentation Methodology for Application in Sport Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 4(4), pages 37-55, December.
    16. Giuliano Galimberti & Gabriele Soffritti, 2020. "Seemingly unrelated clusterwise linear regression," 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. 14(2), pages 235-260, June.
    17. Adrian O’Hagan & Thomas Brendan Murphy & Luca Scrucca & Isobel Claire Gormley, 2019. "Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap," Computational Statistics, Springer, vol. 34(4), pages 1779-1813, December.
    18. Gianfranco DI VAIO & Michele BATTISTI, 2010. "A Spatially-Filtered Mixture of Beta-Convergence Regression for EU Regions, 1980-2002," Regional and Urban Modeling 284100013, EcoMod.
    19. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    20. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).

    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:spr:alstar:v:95:y:2011:i:2:p:205-217. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.