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Generalized Lindley Probability Distribution Up to Five Parameters
Subject area: Science,Engineering and Technology · Area of research: Probability & Statistics
Abstract
In this paper, we extend the Alpha Power Transformed Lindley (APT Lindley) distribution model by introducing an additional parameter. This new model represents a distribution that significantly enhances flexibility to express real-life phenomena. The probability density function, cumulative distribution function, hazard function and survival function are derived and discussed. We also explore characteristics such as moments, moment-generating functions and quantile functions. The maximum likelihood estimation method is employed for parameter estimation of the model. This newly proposed distribution is well-suited for real-world (non-fictional) datasets and reveals interesting properties due to the flexible nature of its hazard function. It presents a promising alternative in statistical and probability theory particularly for applications in health, engineering and economics.
Keywords
Alpha Power Transformed, Lindley distribution, Maximum Likelihood estimators, quantile function
How to cite this paper
@article{1710219,
author = {Dr. Malay Sanyal, Sayantani Ghosh},
title = {Generalized Lindley Probability Distribution Up to Five Parameters},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {2},
pages = {753-763},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710219.pdf},
abstract = {In this paper, we extend the Alpha Power Transformed Lindley (APT Lindley) distribution model by introducing an additional parameter. This new model represents a distribution that significantly enhances flexibility to express real-life phenomena. The probability density function, cumulative distribution function, hazard function and survival function are derived and discussed. We also explore characteristics such as moments, moment-generating functions and quantile functions. The maximum likelihood estimation method is employed for parameter estimation of the model. This newly proposed distribution is well-suited for real-world (non-fictional) datasets and reveals interesting properties due to the flexible nature of its hazard function. It presents a promising alternative in statistical and probability theory particularly for applications in health, engineering and economics.},
keywords = {Alpha Power Transformed, Lindley distribution, Maximum Likelihood estimators, quantile function},
month = {August},
}