Point and interval estimation for the logistic distribution based on record data
Asgharzadeh, Akbar (University of Mazandaran (Babolsar, Iran). Department of Statistics)
Valiollahi, Reza (Semnan University (Iran). Department of Mathematics, Statistics and Computer Science)
Abdi, Mousa (Higher Education Complex of Bam. Department of Mathematics and Soft Computing)

Data: 2016
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Logistic distribution ; Record data ; Maximum likelihood estimator ; Bayes estimator ; Gibbs sampling
Publicat a: SORT : statistics and operations research transactions, Vol. 40 Núm. 1 (January-June 2016) , p. 89-112 (Articles) , ISSN 1696-2281

Adreça original: http://www.raco.cat/index.php/SORT/article/view/310070

24 p, 1.2 MB

El registre apareix a les col·leccions:
Articles > Articles publicats > SORT
Articles > Articles de recerca

 Registre creat el 2016-06-21, darrera modificació el 2017-10-14

   Favorit i Compartir