Work

On a Randomly Censoring Scheme for Generalized Logistic Distribution with Applications

Year: 2024

Type: article

Abstract: In this paper, we investigate the inferential procedures within both classical and Bayesian frameworks for the generalized logistic distribution under a random censoring model. For randomly censored data, our main goals were to develop maximum likelihood estimators and construct confidence intervals using the Fisher information matrix for the unknown parameters. Additionally, we developed Bayes estimators with gamma priors, addressing both squared error and general entropy loss functions. We also calculated Bayesian credible intervals for the parameters. These methods were applied to two real datasets with random censoring to provide valuable insights. Finally, we conducted a simulation analysis to assess the effectiveness of the estimated values.

Source: Symmetry

DOI: 10.3390/sym16091240

Authors: Mustafa M. Hasaballah, Oluwafemi Samson Balogun, Mahmoud E. Bakr

Institutions: Higher Institute of Engineering, University of Eastern Finland, King Saud University

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: King Saud University