IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v27y2012i1p51-67.html
   My bibliography  Save this article

A generalized log-normal distribution and its goodness of fit to censored data

Author

Listed:
  • Bhupendra Singh
  • K. Sharma
  • Shubhi Rathi
  • Gajraj Singh

Abstract

No abstract is available for this item.

Suggested Citation

  • Bhupendra Singh & K. Sharma & Shubhi Rathi & Gajraj Singh, 2012. "A generalized log-normal distribution and its goodness of fit to censored data," Computational Statistics, Springer, vol. 27(1), pages 51-67, March.
  • Handle: RePEc:spr:compst:v:27:y:2012:i:1:p:51-67
    DOI: 10.1007/s00180-011-0233-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-011-0233-9
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00180-011-0233-9?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. Chen, Gemai, 1995. "Generalized log-normal distributions with reliability application," Computational Statistics & Data Analysis, Elsevier, vol. 19(3), pages 309-319, March.
    2. Saralees Nadarajah, 2005. "A generalized normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 685-694.
    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. Bhupendra Singh & Neha Choudhary, 2017. "The exponentiated Perks 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 468-478, June.

    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. Martín, J. & Pérez, C.J., 2009. "Bayesian analysis of a generalized lognormal distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1377-1387, February.
    2. Klein, Ingo, 2012. "Quasi-arithmetische Mittelwerte und Normalverteilung," Discussion Papers 89/2010, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    3. García, V.J. & Gómez-Déniz, E. & Vázquez-Polo, F.J., 2010. "A new skew generalization of the normal distribution: Properties and applications," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2021-2034, August.
    4. Müller K. & Richter W.-D., 2016. "Exact distributions of order statistics of dependent random variables from ln,p-symmetric sample distributions, n ∈ {3,4}," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-29, February.
    5. Sandi Baressi Šegota & Nikola Anđelić & Mario Šercer & Hrvoje Meštrić, 2022. "Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data," Mathematics, MDPI, vol. 10(7), pages 1-17, April.
    6. Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    7. Jeong, Hanbat & Lee, Lung-fei, 2024. "Maximum likelihood estimation of a spatial autoregressive model for origin–destination flow variables," Journal of Econometrics, Elsevier, vol. 242(1).
    8. Kapla, Daniel & Fertl, Lukas & Bura, Efstathia, 2022. "Fusing sufficient dimension reduction with neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    9. Lassance, Nathan & Vrins, Frédéric, 2023. "Portfolio selection: A target-distribution approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 302-314.
    10. Tran, Quang Van & Kukal, Jaromir, 2022. "A novel heavy tail distribution of logarithmic returns of cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Tingguo Zheng & Tao Song, 2014. "A Realized Stochastic Volatility Model With Box-Cox Transformation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 593-605, October.
    12. Jayles, Bertrand & Escobedo, Ramon & Cezera, Stéphane & Blanchet, Adrien & Kameda, Tatsuya & Sire, Clément & Théraulaz, Guy, 2020. "The impact of incorrect social information on collective wisdom in human groups," IAST Working Papers 20-106, Institute for Advanced Study in Toulouse (IAST).
    13. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.
    14. Li, Liuling & Mizrach, Bruce, 2010. "Tail return analysis of Bear Stearns' credit default swaps," Economic Modelling, Elsevier, vol. 27(6), pages 1529-1536, November.
    15. Mijeong Kim & Yanyuan Ma, 2019. "Semiparametric efficient estimators in heteroscedastic error models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(1), pages 1-28, February.
    16. Jorge Munoz-Minjares & Osbaldo Vite-Chavez & Jorge Flores-Troncoso & Jorge M. Cruz-Duarte, 2021. "Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians," Mathematics, MDPI, vol. 9(18), pages 1-19, September.
    17. Roger W. Barnard & Kent Pearce & A. Alexandre Trindade, 2018. "When is tail mean estimation more efficient than tail median? Answers and implications for quantitative risk management," Annals of Operations Research, Springer, vol. 262(1), pages 47-65, March.
    18. Punzo, Antonio & Bagnato, Luca, 2021. "Modeling the cryptocurrency return distribution via Laplace scale mixtures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    19. Zhou, Tong & Peng, Yongbo, 2020. "Adaptive Bayesian quadrature based statistical moments estimation for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    20. Bertrand Jayles & Ramon Escobedo & Stéphane Cezera & Adrien Blanchet & Tatsuya Kameda & Clément Sire & Guy Théraulaz, 2020. "The impact of incorrect social information on collective wisdom in human groups," Post-Print hal-03019820, HAL.

    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:compst:v:27:y:2012:i:1:p:51-67. 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.