IDEAS home Printed from https://ideas.repec.org/a/ibn/ijspjl/v10y2021i4p62.html
   My bibliography  Save this article

Marginalized Maximum Likelihood for Parameters Estimation of the Three Parameter Weibull Distribution

Author

Listed:
  • Ouindllassida Jean-Etienne Ou´edraogo
  • Edoh Katchekpele
  • Simplice Dossou-Gb´et´e

Abstract

The aims of this paper is to propose a new approach for fitting a three-parameter weibull distribution to data from an independent and identically distributed scheme of sampling. This approach use a likelihood function based on the n - 1 largest order statistics. Information loss by dropping the first order statistic is then retrieved via an MM-algorithm which will be used to estimate the model’s parameters. To examine the properties of the proposed estimators, the associated bias and mean squared error were calculated through Monte Carlo simulations. Subsequently, the performance of these estimators were compared with those of two concurrent methods.

Suggested Citation

  • Ouindllassida Jean-Etienne Ou´edraogo & Edoh Katchekpele & Simplice Dossou-Gb´et´e, 2021. "Marginalized Maximum Likelihood for Parameters Estimation of the Three Parameter Weibull Distribution," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(4), pages 1-62, July.
  • Handle: RePEc:ibn:ijspjl:v:10:y:2021:i:4:p:62
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijsp/article/download/0/0/45441/48272
    Download Restriction: no

    File URL: http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/45441
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
    2. Peter Hall & Julian Z. Wang, 2005. "Bayesian likelihood methods for estimating the end point of a distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 717-729, November.
    3. David A. Griffiths, 1980. "Interval Estimation for the Three‐Parameter Lognormal Distribution Via the Likelihood Function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 58-68, March.
    4. Nagatsuka, Hideki & Kamakura, Toshinari & Balakrishnan, N., 2013. "A consistent method of estimation for the three-parameter Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 210-226.
    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. Nagatsuka, Hideki & Kamakura, Toshinari & Balakrishnan, N., 2013. "A consistent method of estimation for the three-parameter Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 210-226.
    2. Castillo, Joan del & Serra, Isabel, 2015. "Likelihood inference for generalized Pareto distribution," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 116-128.
    3. Rasmus Lentz & Jean Marc Robin & Suphanit Piyapromdee, 2018. "On Worker and Firm Heterogeneity in Wages and Employment Mobility: Evidence from Danish Register Data," 2018 Meeting Papers 469, Society for Economic Dynamics.
    4. Songfeng Zheng, 2021. "KLERC: kernel Lagrangian expectile regression calculator," Computational Statistics, Springer, vol. 36(1), pages 283-311, March.
    5. Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," 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. 8(1), pages 45-61, March.
    6. Nguyen Thai An & Nguyen Mau Nam & Xiaolong Qin, 2020. "Solving k-center problems involving sets based on optimization techniques," Journal of Global Optimization, Springer, vol. 76(1), pages 189-209, January.
    7. Vera, J. Fernando & Di­az-Garci­a, Jose A., 2008. "A global simulated annealing heuristic for the three-parameter lognormal maximum likelihood estimation," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5055-5065, August.
    8. Florian Schwendinger & Bettina Grün & Kurt Hornik, 2021. "A comparison of optimization solvers for log binomial regression including conic programming," Computational Statistics, Springer, vol. 36(3), pages 1721-1754, September.
    9. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    10. Pesendorfer, Martin & Cantillon, Estelle, 2007. "Combination Bidding in Multi-Unit Auctions," CEPR Discussion Papers 6083, C.E.P.R. Discussion Papers.
    11. Hannig, Jan & Lai, Randy C.S. & Lee, Thomas C.M., 2014. "Computational issues of generalized fiducial inference," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 849-858.
    12. V. Maume-Deschamps & D. Rullière & A. Usseglio-Carleve, 2018. "Spatial Expectile Predictions for Elliptical Random Fields," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 643-671, June.
    13. Sanjeena Subedi & Drew Neish & Stephen Bak & Zeny Feng, 2020. "Cluster analysis of microbiome data by using mixtures of Dirichlet–multinomial regression models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1163-1187, November.
    14. Lian, Heng & Meng, Jie & Fan, Zengyan, 2015. "Simultaneous estimation of linear conditional quantiles with penalized splines," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 1-21.
    15. Ajinkya Shirurkar & Yogesh Patil & D. Davidson Jebaseelan, 2019. "Reliability improvement of fork biasing spring in MCCB mechanism," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 491-498, August.
    16. van den Burg, G.J.J. & Groenen, P.J.F., 2014. "GenSVM: A Generalized Multiclass Support Vector Machine," Econometric Institute Research Papers EI 2014-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Lv, Xiao-Guang & Jiang, Le & Liu, Jun, 2016. "Deblurring Poisson noisy images by total variation with overlapping group sparsity," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 132-148.
    18. Rasmus Lentz & Suphanit Piyapromdee & Jean-Marc Robin, 2022. "The Anatomy of Sorting - Evidence from Danish Data," SciencePo Working papers Main hal-03869383, HAL.
    19. Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
    20. Vu, Duy & Aitkin, Murray, 2015. "Variational algorithms for biclustering models," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 12-24.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:ijspjl:v:10:y:2021:i:4:p:62. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.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.