The Odd Log-Logistic Generalized Half-Normal Lifetime Poisson Model
Abstract
Recently, Corderio et al. (2016) applied a model called odd-logistic generalized half-normal distribution for describing fatigue lifetime data, based on this model, we propose a new wider model with a strong physical motivation called the odd-log-logistic generalized half-normal poisson distribution which is commonly used in reliability studies and modeling maximum of a random number of lifetime variables. Various of its structural properties are derived. The method of maximum likelihood is adapted to estimate the model parameters and its potentiality is illustrated with applications to two real fatigue data sets. For different parameter settings and sample sizes, some simulation studies compare the performance of the new lifetime model.
Keywords
Full Text:
PDFDOI: http://dx.doi.org/10.18187/pjsor.v15i1.2349
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 Pakistan Journal of Statistics and Operation Research

This work is licensed under a Creative Commons Attribution 4.0 International License.
Title
The Odd Log-Logistic Generalized Half-Normal Lifetime Poisson Model
Keywords
Generalized half-normal distribution; Truncated Poisson distribution; Maximum likelihood estimation; Generating function; Survival data
Description
Recently, Corderio et al. (2016) applied a model called odd-logistic generalized half-normal distribution for describing fatigue lifetime data, based on this model, we propose a new wider model with a strong physical motivation called the odd-log-logistic generalized half-normal poisson distribution which is commonly used in reliability studies and modeling maximum of a random number of lifetime variables. Various of its structural properties are derived. The method of maximum likelihood is adapted to estimate the model parameters and its potentiality is illustrated with applications to two real fatigue data sets. For different parameter settings and sample sizes, some simulation studies compare the performance of the new lifetime model.
Date
2019-03-23
Identifier
Source
Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810