IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i20p3264-d1501136.html
   My bibliography  Save this article

A Comparison of MLE for Some Index Distributions Based on Censored Samples

Author

Listed:
  • Yunhan Liu

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
    These authors contributed equally to this work.)

  • Changchun Gao

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
    These authors contributed equally to this work.)

  • Xiaofeng Liu

    (Department of Statistics, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321000, China
    These authors contributed equally to this work.)

  • Ping Luo

    (Department of Statistics, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321000, China
    These authors contributed equally to this work.)

  • Jianguo Ren

    (Department of Mathematics, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321000, China
    These authors contributed equally to this work.)

Abstract

This paper elucidates the prerequisites for maximum likelihood estimation (MLE) of parameters within the exponential and scale parameter families. Estimation of these parameters is predicated on data derived from censored samples and seeks to adhere to stochastic ordering principles. The study establishes that for two independent normal distributions and a two-parameter exponential distribution discernible by the distinct parameter sets, the MLEs of the parameters evince a stochastically ordered relationship when evaluated using full datasets. Furthermore, this research is extended to corroborate the persistence of stochastic ordering in the MLEs of such parameters under conditions of fixed censoring of samples.

Suggested Citation

  • Yunhan Liu & Changchun Gao & Xiaofeng Liu & Ping Luo & Jianguo Ren, 2024. "A Comparison of MLE for Some Index Distributions Based on Censored Samples," Mathematics, MDPI, vol. 12(20), pages 1-15, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3264-:d:1501136
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/20/3264/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/20/3264/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fanhui Kong & Heliang Fei, 1996. "Limit theorems for the maximum likelihood estimate under general multiply Type II censoring," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(4), pages 731-755, December.
    2. Guoxin Qiu & Mohammad Z. Raqab, 2024. "On weighted extropy of ranked set sampling and its comparison with simple random sampling counterpart," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(1), pages 378-395, January.
    3. Ya-Hsuan Hu & Takeshi Emura, 2015. "Maximum likelihood estimation for a special exponential family under random double-truncation," Computational Statistics, Springer, vol. 30(4), pages 1199-1229, December.
    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. Pao-sheng Shen & Yi Liu, 2019. "Pseudo maximum likelihood estimation for the Cox model with doubly truncated data," Statistical Papers, Springer, vol. 60(4), pages 1207-1224, August.
    2. Takeshi Emura & Ya-Hsuan Hu & Yoshihiko Konno, 2017. "Asymptotic inference for maximum likelihood estimators under the special exponential family with double-truncation," Statistical Papers, Springer, vol. 58(3), pages 877-909, September.
    3. Achim Dörre & Chung-Yan Huang & Yi-Kuan Tseng & Takeshi Emura, 2021. "Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model," Computational Statistics, Springer, vol. 36(1), pages 375-408, March.
    4. Anna Dembińska & Krzysztof Jasiński, 2021. "Maximum likelihood estimators based on discrete component lifetimes of a k-out-of-n system," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 407-428, June.
    5. Takeshi Emura & Chi-Hung Pan, 2020. "Parametric likelihood inference and goodness-of-fit for dependently left-truncated data, a copula-based approach," Statistical Papers, Springer, vol. 61(1), pages 479-501, February.
    6. Chien-Tai Lin & N. Balakrishnan, 2011. "Asymptotic properties of maximum likelihood estimators based on progressive Type-II censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(3), pages 349-360, November.
    7. Jia-Han Shih & Takeshi Emura, 2018. "Likelihood-based inference for bivariate latent failure time models with competing risks under the generalized FGM copula," Computational Statistics, Springer, vol. 33(3), pages 1293-1323, September.
    8. Shen, Pao-sheng & Hsu, Huichen, 2020. "Conditional maximum likelihood estimation for semiparametric transformation models with doubly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    9. Achim Dörre, 2020. "Bayesian estimation of a lifetime distribution under double truncation caused by time-restricted data collection," Statistical Papers, Springer, vol. 61(3), pages 945-965, June.

    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:gam:jmathe:v:12:y:2024:i:20:p:3264-:d:1501136. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.