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Nonparametric estimation of finite mixtures from repeated measurements

Author

Listed:
  • Koen Jochmans

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique)

  • Stéphane Bonhomme

    (University of Chicago)

  • Jean-Marc Robin

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, Economics department - MIT - Massachusetts Institute of Technology)

Abstract

This paper provides methods to estimate finite mixtures from data with repeated measurements non-parametrically. We present a constructive identification argument and use it to develop simple two-step estimators of the component distributions and all their functionals. We discuss a computationally efficient method for estimation and derive asymptotic theory. Simulation experiments suggest that our theory provides confidence intervals with good coverage in small samples.

Suggested Citation

  • Koen Jochmans & Stéphane Bonhomme & Jean-Marc Robin, 2015. "Nonparametric estimation of finite mixtures from repeated measurements," Post-Print hal-03568247, HAL.
    • Handle: RePEc:hal:journl:hal-03568247
    DOI: 10.1111/rssb.12110
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    Cited by:

    1. Bonhomme, Stéphane & Jochmans, Koen & Robin, Jean-Marc, 2017. "Nonparametric estimation of non-exchangeable latent-variable models," Journal of Econometrics, Elsevier, vol. 201(2), pages 237-248.
    2. Charles Bellemare & Alexander Sebald, 2019. "Measuring Belief-Dependent Preferences without Information about Beliefs," CESifo Working Paper Series 7505, CESifo.
    3. Budanova, Sofya, 2025. "Penalized estimation of finite mixture models," Journal of Econometrics, Elsevier, vol. 249(PB).
    4. Rasmus Lentz & Suphanit Piyapromdee & Jean-Marc Robin, 2018. "On Worker and Firm Heterogeneity in Wages and Employment Mobility: Evidence from Danish Register Data," PIER Discussion Papers 91, Puey Ungphakorn Institute for Economic Research.
    5. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    6. Stephane Bonhomme, 2021. "Teams: Heterogeneity, Sorting, and Complementarity," Papers 2102.01802, arXiv.org.
    7. Qihui Chen & Zheng Fang, 2018. "Improved Inference on the Rank of a Matrix," Papers 1812.02337, arXiv.org, revised Mar 2019.
    8. Victor Aguirregabiria & Alessandro Iaria & Senay Sokullu, 2023. "Identification and Estimation of Demand Models with Endogenous Product Entry and Exit," Working Papers tecipa-755, University of Toronto, Department of Economics.
    9. Jochmans, Koen, 2024. "Nonparametric identification and estimation of stochastic block models from many small networks," Journal of Econometrics, Elsevier, vol. 242(2).
    10. Engel, Christoph, 2020. "Estimating heterogeneous reactions to experimental treatments," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 124-147.
    11. Bonhomme, Stéphane & Jochmans, Koen & Robin, Jean-Marc, 2017. "Nonparametric estimation of non-exchangeable latent-variable models," Journal of Econometrics, Elsevier, vol. 201(2), pages 237-248.
    12. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2017. "Nonparametric estimation of non-exchangeable latent-variable models," Sciences Po publications info:hdl:2441/4m4fqk908d9, Sciences Po.
    13. Jochmans, Koen & Henry, Marc & Salanié, Bernard, 2017. "Inference On Two-Component Mixtures Under Tail Restrictions," Econometric Theory, Cambridge University Press, vol. 33(3), pages 610-635, June.
    14. Michael Levine & Gildas Mazo, 2024. "A smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure," Computational Statistics, Springer, vol. 39(4), pages 1825-1846, June.
    15. Krasnokutskaya, Elena & Song, Kyungchul & Tang, Xun, 2022. "Estimating unobserved individual heterogeneity using pairwise comparisons," Journal of Econometrics, Elsevier, vol. 226(2), pages 477-497.
    16. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    17. Jochmans, Koen & Weidner, Martin, 2024. "Inference On A Distribution From Noisy Draws," Econometric Theory, Cambridge University Press, vol. 40(1), pages 60-97, February.
    18. Xiaotian Zhu & David R. Hunter, 2019. "Clustering via finite nonparametric ICA mixture models," 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. 13(1), pages 65-87, March.
    19. Arthur Lewbel & Xi Qu & Xun Tang, 2024. "Estimating Social Network Models with Link Misclassification," Boston College Working Papers in Economics 1079, Boston College Department of Economics.

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