IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v40y2023i2d10.1007_s00357-023-09433-3.html
   My bibliography  Save this article

Computing Finite Mixture Estimators in the Tails

Author

Listed:
  • Marilena Furno

    (Università degli Studi di Napoli Federico II)

Abstract

The finite mixtures approach identifies homogeneous groups within the sample. The data are aggregated into classes sharing similar patterns without any prior knowledge or assumption on the clustering. These clusters are characterized by group-specific regression coefficients to account for between groups heterogeneity. Two different approaches have been independently defined in the literature to compute this estimator not only at the conditional mean but also in the tails. One approach allows the grouping to change according to the selected location. The other defines the clusters once and for all at the conditional mean, and then moves the estimation to the tails, focusing on cluster specific estimates and allowing between groups comparison. Here we compare the behavior of both approaches, and in addition we consider a closely related estimator based on expectiles, together with few others more robust, quantile-based estimators. A case study on students’ performance concludes the analysis.

Suggested Citation

  • Marilena Furno, 2023. "Computing Finite Mixture Estimators in the Tails," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 267-297, July.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:2:d:10.1007_s00357-023-09433-3
    DOI: 10.1007/s00357-023-09433-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-023-09433-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-023-09433-3?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. Bartolucci, F. & Scaccia, L., 2005. "The use of mixtures for dealing with non-normal regression errors," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 821-834, April.
    2. Bai, Xiuqin & Yao, Weixin & Boyer, John E., 2012. "Robust fitting of mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2347-2359.
    3. Giovanni Compiani & Yuichi Kitamura, 2016. "Using mixtures in econometric models: a brief review and some new results," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 95-127, October.
    Full references (including those not matched with items on IDEAS)

    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. Gabriele Perrone & Gabriele Soffritti, 2023. "Seemingly unrelated clusterwise linear regression for contaminated data," Statistical Papers, Springer, vol. 64(3), pages 883-921, June.
    2. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    3. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.
    4. Lara Delsalle & Oleksii Birulin, 2024. "Family-oriented versus career seekers: mixture regression separation," Empirical Economics, Springer, vol. 67(1), pages 313-335, July.
    5. Leporia, Benedetto & Geuna, Aldo & Mira, Antonietta, 2018. "Scientific Output of US and European Universities Scales Super-linearly with Resources," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201806, University of Turin.
    6. Wu, Qiang & Yao, Weixin, 2016. "Mixtures of quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 162-176.
    7. Atefeh Zarei & Zahra Khodadadi & Mohsen Maleki & Karim Zare, 2023. "Robust mixture regression modeling based on two-piece scale mixtures of normal distributions," 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. 17(1), pages 181-210, March.
    8. Hoshino Tadao & Yanagi Takahide, 2022. "Estimating marginal treatment effects under unobserved group heterogeneity," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 197-216, January.
    9. Hiroko Araki & Juan Nelson Martinez Dahbura, 2021. "The Heterogeneous Relationship Between Financial Education and Investment Behavior in Japan," Keio-IES Discussion Paper Series 2021-018, Institute for Economics Studies, Keio University.
    10. Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 121-137, June.
    11. Djeutem, Edouard & Dunbar, Geoffrey R., 2022. "Uncovered return parity: Equity returns and currency returns," Journal of International Money and Finance, Elsevier, vol. 128(C).
    12. Galimberti, Giuliano & Soffritti, Gabriele, 2014. "A multivariate linear regression analysis using finite mixtures of t distributions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 138-150.
    13. Knut Are Aastveit & Jamie L. Cross & Francesco Furlanetto & Herman K. Van Dijk, 2024. "Taylor Rules with Endogenous Regimes," Working Papers No 04/2024, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Shanshan Qin & Zhenni Tan & Yuehua Wu, 2024. "On robust estimation of hidden semi-Markov regime-switching models," Annals of Operations Research, Springer, vol. 338(2), pages 1049-1081, July.
    15. Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.
    16. Jackson Bunting & Paul Diegert & Arnaud Maurel, 2024. "Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation," Papers 2402.08575, arXiv.org.
    17. Wang, Shangshan & Xiang, Liming, 2017. "Two-layer EM algorithm for ALD mixture regression models: A new solution to composite quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 136-154.
    18. Inmaculada Martinez‐Zarzoso & Antonello Maruotti, 2013. "The environmental Kuznets curve: functional form, time‐varying heterogeneity and outliers in a panel setting," Environmetrics, John Wiley & Sons, Ltd., vol. 24(7), pages 461-475, November.
    19. Benedetto Lepori & Aldo Geuna & Antonietta Mira, 2019. "Scientific output scales with resources. A comparison of US and European universities," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-18, October.
    20. Katherine G. Yewell & Steven B. Caudill & Franklin G. Mixon, Jr., 2014. "Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach," Econometrics, MDPI, vol. 2(1), pages 1-19, February.

    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:jclass:v:40:y:2023:i:2:d:10.1007_s00357-023-09433-3. 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.