Prediction for Lindley Distribution Based on Type-II Right Censored Samples

Authors

  • Akbar Asgharzadeh Department of Statistics‎, ‎University of Mazandaran‎, ‎Babolsar‎, ‎Iran
  • Hon K. T. ‎Ng Department of Statistical Science‎, ‎Southern Methodist University‎, ‎Dallas‎, ‎USA
Abstract:

‎Lindley distribution has received considerable attention in the statistical literature due to its simplicity‎. ‎In this paper‎, ‎we consider the problem of predicting the failure times of experimental units that are censored in a right-censored sample‎‎ when the underlying lifetime is Lindley distributed‎. ‎The maximum likelihood predictor‎, ‎the Best unbiased predictor and the conditional median predictor are derived‎. ‎Prediction intervals based on these predictors are considered‎. ‎We further propose two resampling-based procedures for obtaining the prediction intervals‎. ‎A numerical example is used to illustrate the methodology developed in this paper‎. ‎Finally‎, ‎a Monte Carlo simulation study is employed to evaluate the performance of different prediction methods‎.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Comparison of three Estimation Procedures for Weibull Distribution based on Progressive Type II Right Censored Data

In this paper, based on the progressive type II right censored data, we consider estimates of MLE and AMLE of scale and shape parameters of weibull distribution. Also a new type of parameter estimation, named inverse estimation, is introdued for both shape and scale parameters of weibull distribution which is used from order statistics properties in it. We use simulations and study the biases a...

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

Bayesian Estimation and Prediction of Generalized Pareto Distribution Based on Type II Censored Samples

The study aims to estimate the parameter of the Generalized Pareto Distribution under Type II censored samples. The Bayes Estimators have been derived under a class of informative and non-informative Priors using different symmetric and asymmetric Loss functions. The Credible Intervals, Highest Posterior Density (HPD) intervals, Posterior Predictive Distributions and Posterior Predictive Interv...

full text

Point and interval estimation for Gaussian distribution, based on progressively Type-II censored samples

The likelihood equations based on a progressively Type-II censored sample from a Gaussian distribution do not provide explicit solutions in any situation except the complete sample case. This paper examines numerically the bias and mean square error of the MLE, and demonstrates that the probability coverages of the pivotal quantities (for location and scale parameters) based on asymptotic -norm...

full text

Exact likelihood inference for Laplace distribution based on Type-II censored samples

We develop exact inference for the location and scale parameters of the Laplace (double exponential) distribution based on their maximum likelihood estimators from a Type-II censored sample. Based on some pivotal quantities, exact confidence intervals and tests of hypotheses are constructed. Upon conditioning first on the number of observations that are below the population median, exact distri...

full text

Entropy Estimation of Generalized Half-Logistic Distribution (GHLD) Based on Type-II Censored Samples

This paper derives the entropy of a generalized half-logistic distribution based on Type-II censored samples, obtains some entropy estimators by using Bayes estimators of an unknown parameter in the generalized half-logistic distribution based on Type-II censored samples and compares these estimators in terms of the mean squared error and the bias through Monte Carlo simulations.

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 16  issue None

pages  1- 19

publication date 2017-12

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