Lindley Regression with application
DOI:
https://doi.org/10.62933/7k7j2657Keywords:
Lindley regression model , Quasi Maximum Likelihood method , Bayesian Information Criterion, Cumulative distribution function, Quantile functionAbstract
In this work, Lindley regression model is studied standing up Lindley distribution which is one of the generalized linear models. It is like with distributions for example Weibull, Exponential , Gamma and other distributions which are positive skew. This is Lindley Distribution. It aims to dealing with variables have positively skewed problems. The model has been composed and then reaching to to a required link function . Parameters are estimated by Maximum Likelihood Method by using Newton-Raphson because it is difficult to obtain closed parameters. In application , real data is used to compare a model depended on exponential distribution and other depended on Lindley`s distribution by using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) after showing by Kolmogorry - Smirnov Test that the dependent variable follows both distributions . The outcomes show that Lindley`s distributions provides accurate results comparing with results of the exponential distribution.
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Copyright (c) 2023 Adnan Mostafa Al-Sinjary (Author); Hayder Sabah Tuama (Translator)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
