IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v102y2011i4p789-800.html
   My bibliography  Save this article

A numerical method for minimum distance estimation problems

Author

Listed:
  • Cervellera, C.
  • Macciò, D.

Abstract

This paper introduces a general method for the numerical derivation of a minimum distance (MD) estimator for the parameters of an unknown distribution. The approach is based on an active sampling of the space in which the random sample takes values and on the optimization of the parameters of a suitable approximating model. This allows us to derive the MD estimator function for any given distribution, by which we can immediately obtain the MD estimate of the unknown parameters in correspondence to any observed random sample. Convergence of the method is proved when mild conditions on the sampling process and on the involved functions are satisfied, and it is shown that favorable rates can be obtained when suitable deterministic sequences are employed. Finally, simulation results are provided to show the effectiveness of the proposed algorithm on two case studies.

Suggested Citation

  • Cervellera, C. & Macciò, D., 2011. "A numerical method for minimum distance estimation problems," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 789-800, April.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:4:p:789-800
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(10)00246-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. M. Falk, 1983. "Relative efficiency and deficiency of kernel type estimators of smooth distribution functions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 37(2), pages 73-83, June.
    2. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
    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. Roussas, George G., 1995. "Asymptotic normality of a smooth estimate of a random field distribution function under association," Statistics & Probability Letters, Elsevier, vol. 24(1), pages 77-90, July.
    2. Falk, Michael & Reiss, Rolf-Dieter, 2003. "Efficient estimators and LAN in canonical bivariate POT models," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 190-207, January.
    3. Alevizos, Filippos & Bagkavos, Dimitrios & Ioannides, Dimitrios, 2019. "Efficient estimation of a distribution function based on censored data," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 359-364.
    4. Ibirénoyé Romaric Sodjahin & Fabienne Femenia & Obafemi Philippe Koutchade & A. Carpentier, 2022. "On the economic value of the agronomic effects of crop diversification for farmers: estimation based on farm cost accounting data [Valeur économique des effets agronomiques de la diversification de," Working Papers hal-03639951, HAL.
    5. Wang, Xiaoning & Schumitzky, Alan & D'Argenio, David Z., 2007. "Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6614-6623, August.
    6. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    7. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2013. "Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model," Ecological Modelling, Elsevier, vol. 263(C), pages 56-63.
    8. Allassonnière, Stéphanie & Kuhn, Estelle, 2015. "Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 4-19.
    9. Ariane Hanebeck & Bernhard Klar, 2021. "Smooth distribution function estimation for lifetime distributions using Szasz–Mirakyan operators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(6), pages 1229-1247, December.
    10. Sodjahin, Romaric & Carpentier, Alain & Koutchade, Obafèmi Philippe & Femenia, Fabienne, 2022. "On the economic value of the agronomic effects of crop diversification for farmers: Estimation based on farm cost accounting data," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322295, Agricultural and Applied Economics Association.
    11. Laura Azzimonti & Francesca Ieva & Anna Maria Paganoni, 2013. "Nonlinear nonparametric mixed-effects models for unsupervised classification," Computational Statistics, Springer, vol. 28(4), pages 1549-1570, August.
    12. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
    13. Samson, Adeline & Lavielle, Marc & Mentre, France, 2006. "Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1562-1574, December.
    14. Sébastien Benzekry & Clare Lamont & Afshin Beheshti & Amanda Tracz & John M L Ebos & Lynn Hlatky & Philip Hahnfeldt, 2014. "Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-19, August.
    15. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    16. Ralescu, Stefan S. & Puri, Madan L., 1996. "Weak convergence of sequences of first passage processes and applications," Stochastic Processes and their Applications, Elsevier, vol. 62(2), pages 327-345, July.
    17. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.
    18. Ollier, Edouard & Samson, Adeline & Delavenne, Xavier & Viallon, Vivian, 2016. "A SAEM algorithm for fused lasso penalized NonLinear Mixed Effect Models: Application to group comparison in pharmacokinetics," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 207-221.
    19. Mammitzsch Volker, 2007. "Optimal kernels," Statistics & Risk Modeling, De Gruyter, vol. 25(2/2007), pages 1-20, April.
    20. Boubacar Mainassara, Y. & Carbon, M. & Francq, C., 2012. "Computing and estimating information matrices of weak ARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 345-361.

    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:eee:jmvana:v:102:y:2011:i:4:p:789-800. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.