Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2016

Point and interval estimation for the logistic distribution based on record data

  • Identification data

    Identifier: RP:2440
    Handle: https://hdl.handle.net/20.500.11797/RP2440
    Authors:
    Abdi, MousaValiollahi, RezaAsgharzadeh, Akbar
    Abstract:
    In this paper, based on record data from the two-parameter logistic distribution, the maximum likelihood and Bayes estimators for the two unknown parameters are derived. The maximum likelihood estimators and Bayes estimators can not be obtained in explicit forms. We present a simplemethod of deriving explicit maximum likelihood estimators by approximating the likelihood function. Also, an approximation based on the Gibbs sampling procedure is used to obtain the Bayes estimators. Asymptotic confidence intervals, bootstrap confidence intervals and credible intervals are also proposed. Monte Carlo simulations are performed to compare the performances of the different proposed methods. Finally, one real data set has been analysed for illustrative purposes.

  • Others:

    URV's Author/s: Abdi, Mousa Valiollahi, Reza Asgharzadeh, Akbar
    Keywords: Logistic distribution, record data,maximum likelihood estimator, Bayes estimator, Gibbs sampling
    Abstract: In this paper, based on record data from the two-parameter logistic distribution, the maximum likelihood and Bayes estimators for the two unknown parameters are derived. The maximum likelihood estimators and Bayes estimators can not be obtained in explicit forms. We present a simplemethod of deriving explicit maximum likelihood estimators by approximating the likelihood function. Also, an approximation based on the Gibbs sampling procedure is used to obtain the Bayes estimators. Asymptotic confidence intervals, bootstrap confidence intervals and credible intervals are also proposed. Monte Carlo simulations are performed to compare the performances of the different proposed methods. Finally, one real data set has been analysed for illustrative purposes.
    Journal publication year: 2016
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Logistic distribution, record data,maximum likelihood estimator, Bayes estimator, Gibbs sampling

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