Transmuted Burr Type X Distribution with Covariates Regression Modeling to Analyze Reliability Data

Title: Transmuted Burr Type X Distribution with Covariates Regression Modeling to Analyze Reliability Data
Creator: Khan, Muhammad Shuaib; King, Robert; Hudson, Irene Lena
Relation: American Journal of Mathematical and Management Sciences Vol. 39, Issue 2, p. 99-121
Publisher Link: http://dx.doi.org/10.1080/01966324.2019.1605320
Publisher: Taylor & Francis
Resource Type: journal article
Date: 2020
Description: This article investigates the potential usefulness of the three-parameter transmuted Burr type X (TBX) distribution for modeling reliability data, and explore its structural properties using simulation. Explicit expressions are derived for moments, incomplete moments, entropies, and mean deviation. The method of maximum likelihood is used for estimating the model parameters. We conduct Monte Carlo simulations, which are used to examine the relative performance of the estimators using MLE in terms of bias and mean square errors. A location-scale regression model based on the log-TBX distribution is proposed for modeling lifetime data. Use of this family of distributions is illustrated for fatigue fracture data and multiple myeloma patient’s data.
Subject: log-transmuted Burr Type X distribution; maximum likelihood estimation; moment estimation; statistical computing
Identifier: http://hdl.handle.net/1959.13/1440363
Identifier: uon:41134
Identifier: ISSN:0196-6324
Language: eng
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