Inference about the Burr Type III Distribution under Type-II Hybrid Censored Data

Authors

Abstract:

This paper presents the statistical inference on the parameters of the Burr type III distribution, when the data are Type-II hybrid censored. The maximum likelihood estimators are developed for the unknown parameters using the EM algorithm method. We provided the observed Fisher information matrix using the missing information principle which is useful for constructing the asymptotic confidence intervals. The Bayesian estimates of the unknown parameters under the assumption of independent gamma priors are obtained using two approximations, namely Lindley's approximation and the Markov Chain Monte Carlo technique. Monte Carlo simulations are performed to observe the behavior of the proposed methods and a real dataset representing is used to illustrate the derived results.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

modeling loss data by phase-type distribution

بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...

Point and Interval Estimation for the Burr Type III Distribution

In this paper, we study the estimation problems for the Burr type III distribution based on a complete sample. The maximum likelihood method is used to derive the point estimators of the parameter. An exact confidence interval and an exact joint confidence region for the parameters are constructed. Two numerical examples with real data set and simulated data, are presented to illustrate the met...

full text

Statistical Evidences in Type-II Censored Data

In this article, we use a measure of expected true evidence for determine the required sample size in type-II censored experiments for obtaining statistical evidence in favor of one hypothesis about the exponential mean against another.

full text

Inference for Lomax Distribution Based on Type-II Progressively Hybrid Censored Data

ABSTRACT The mixture of Type-I and Type-II censoring schemes, called the hybrid censoring scheme is quite common in life-testing or reliability experiments. In this paper, we investigate the estimation of parameters from two-parameter Lomax distribution based on Type-II progressively hybrid censored samples. Maximum likelihood estimates for the distribution parameters and the reliability indice...

full text

Inference for a Skew Normal Distribution Based on Progressively Type-II Censored Samples

In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early or the number of experiments must be limited due to a variety of circumstances (e.g. when expensive, etc.) the samples that arise from such experiments are called censored data. Cohen (1991) was one of the earliest to study a more general censoring scheme called progressive censor...

full text

Inference for the Type-II Generalized Logistic Distribution with Progressive Hybrid Censoring

This article presents the analysis of the Type-II hybrid progressively censored data when the lifetime distributions of the items follow Type-II generalized logistic distribution. Maximum likelihood estimators (MLEs) are investigated for estimating the location and scale parameters. It is observed that the MLEs can not be obtained in explicit forms. We provide the approximate maximum likelihood...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 10  issue 2

pages  209- 233

publication date 2014-03

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023