IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2008i12p5055-5065.html
   My bibliography  Save this article

A global simulated annealing heuristic for the three-parameter lognormal maximum likelihood estimation

Author

Listed:
  • Vera, J. Fernando
  • Di­az-Garci­a, Jose A.

Abstract

It is well known that the inclusion of the threshold parameter in a lognormal distribution creates serious complications for parameter estimation; several parameterized schemes and global optimization procedures have been proposed to solve the problem in the maximum likelihood framework. A global Simulated Annealing optimization heuristic is proposed to solve the problem of maximum likelihood estimation in any parameterization scheme for the three-parameter lognormal distribution, as well as for the extended lognormal distribution. Positively and negatively skewed lognormal distributions are considered by introducing a one-parameter conditional estimation procedure in the classical parameterization for the three-parameter lognormal distribution, and a dual reparameterization is introduced for parameters estimation in the extended lognormal distribution. Simulated and real data are analyzed to test the efficiency of the proposed algorithm.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:csdana:v:52:y:2008:i:12:p:5055-5065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00243-0
    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. J. Fernando Vera & Willem J. Heiser & Alex Murillo, 2007. "Global Optimization in Any Minkowski Metric: A Permutation-Translation Simulated Annealing Algorithm for Multidimensional Scaling," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 277-301, September.
    2. Hirose, Hideo, 1997. "Maximum likelihood parameter estimation in the three-parameter log-normal distribution using the continuation method," Computational Statistics & Data Analysis, Elsevier, vol. 24(2), pages 139-152, April.
    3. Richard L. Smith & J. C. Naylor, 1987. "A Comparison of Maximum Likelihood and Bayesian Estimators for the Three‐Parameter Weibull Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 358-369, November.
    4. 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.
    5. Gilli, Manfred & Winker, Peter, 2007. "2nd Special Issue on Applications of Optimization Heuristics to Estimation and Modelling Problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 2-3, September.
    6. Wingo, Dallas R., 1984. "Fitting three-parameter lognormal models by numerical global optimization -- an improved algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 2(1), pages 13-25, June.
    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. Vera, J. Fernando & Macas, Rodrigo & Heiser, Willem J., 2009. "A dual latent class unfolding model for two-way two-mode preference rating data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3231-3244, June.
    2. Román-Román, P. & Torres-Ruiz, F., 2015. "A stochastic model related to the Richards-type growth curve. Estimation by means of simulated annealing and variable neighborhood search," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 579-598.
    3. Santosh B. Rane & Yahya A. M. Narvel, 2016. "Reliability assessment and improvement of air circuit breaker (ACB) mechanism by identifying and eliminating the root causes," 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. 7(1), pages 305-321, December.
    4. Kim, Jin Seon & Yum, Bong-Jin, 2008. "Selection between Weibull and lognormal distributions: A comparative simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 477-485, December.
    5. 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.
    6. Santosh B. Rane & Yahya A.M. Narvel & Niloy Khatua, 2017. "Development of mechanism for mounting secondary isolating contacts (SICs) in air circuit breakers (ACBs) with high operational reliability," 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. 8(2), pages 1816-1831, November.
    7. Gutiérrez-Sánchez, R. & Nafidi, A. & Pascual, A. & Ramos-Ábalos, E., 2011. "Three parameter gamma-type growth curve, using a stochastic gamma diffusion model: Computational statistical aspects and simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(2), pages 234-243.
    8. J. Vera & Rodrigo Macías & Willem Heiser, 2013. "Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 370-396, October.
    9. Nafidi, Ahmed & El Azri, Abdenbi, 2021. "A stochastic diffusion process based on the Lundqvist–Korf growth: Computational aspects and simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 25-38.

    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. Hirose, Hideo, 1997. "Maximum likelihood parameter estimation in the three-parameter log-normal distribution using the continuation method," Computational Statistics & Data Analysis, Elsevier, vol. 24(2), pages 139-152, April.
    2. Hirose, Hideo, 2000. "Maximum likelihood parameter estimation by model augmentation with applications to the extended four-parameter generalized gamma distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 54(1), pages 81-97.
    3. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    4. Boikanyo Makubate & Fastel Chipepa & Broderick Oluyede & Peter O. Peter, 2021. "The Marshall-Olkin Half Logistic-G Family of Distributions With Applications," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 120-120, March.
    5. Haitham M. Yousof & Yusra Tashkandy & Walid Emam & M. Masoom Ali & Mohamed Ibrahim, 2023. "A New Reciprocal Weibull Extension for Modeling Extreme Values with Risk Analysis under Insurance Data," Mathematics, MDPI, vol. 11(4), pages 1-26, February.
    6. A. A. Ogunde & S. T. Fayose & B. Ajayi & D. O. Omosigho, 2020. "Properties, Inference and Applications of Alpha Power Extended Inverted Weibull Distribution," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(6), pages 1-90, November.
    7. Mukhtar M. Salah & M. El-Morshedy & M. S. Eliwa & Haitham M. Yousof, 2020. "Expanded Fréchet Model: Mathematical Properties, Copula, Different Estimation Methods, Applications and Validation Testing," Mathematics, MDPI, vol. 8(11), pages 1-29, November.
    8. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    9. Yuanhua Feng & Wolfgang Karl Härdle, 2021. "Uni- and multivariate extensions of the sinh-arcsinh normal distribution applied to distributional regression," Working Papers CIE 142, Paderborn University, CIE Center for International Economics.
    10. Julius Žilinskas, 2012. "Parallel branch and bound for multidimensional scaling with city-block distances," Journal of Global Optimization, Springer, vol. 54(2), pages 261-274, October.
    11. Joarder, Avijit & Krishna, Hare & Kundu, Debasis, 2011. "Inferences on Weibull parameters with conventional type-I censoring," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 1-11, January.
    12. Hadeel Klakattawi & Dawlah Alsulami & Mervat Abd Elaal & Sanku Dey & Lamya Baharith, 2022. "A new generalized family of distributions based on combining Marshal-Olkin transformation with T-X family," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-29, February.
    13. Sandeep Kumar Maurya & Saralees Nadarajah, 2021. "Poisson Generated Family of Distributions: A Review," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 484-540, November.
    14. Hassan M. Okasha & Abdulkareem M. Basheer & A. H. El-Baz, 2021. "Marshall–Olkin Extended Inverse Weibull Distribution: Different Methods of Estimations," Annals of Data Science, Springer, vol. 8(4), pages 769-784, December.
    15. Acitas, Sukru & Aladag, Cagdas Hakan & Senoglu, Birdal, 2019. "A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 116-127.
    16. Ahtasham Gul & Muhammad Mohsin & Muhammad Adil & Mansoor Ali, 2021. "A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-24, April.
    17. Abdulrahman Abouammoh & Mohamed Kayid, 2020. "A New Family of Extended Lindley Models: Properties, Estimation and Applications," Mathematics, MDPI, vol. 8(12), pages 1-13, December.
    18. Vera, J. Fernando & Macas, Rodrigo & Heiser, Willem J., 2009. "A dual latent class unfolding model for two-way two-mode preference rating data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3231-3244, June.
    19. 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.
    20. Shahram Yaghoobzadeh, 2017. "A new generalization of the Marshall–Olkin Gompertz distribution," 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. 8(2), pages 1580-1587, November.

    More about this item

    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:eee:csdana:v:52:y:2008:i:12:p:5055-5065. 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/locate/csda .

    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.