Modelling to Engineering Data Using a New Class of Continuous Models

In this paper, a new flexible generator of continuous lifespan models referred to as the Topp-Leone Weibull G (TLWG) family is developed and studied. Several mathematical characteristics have been investigated. The new hazard rate of the new model can be “monotonically increasing,” “monotonically de...

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Published in:Journal of function spaces 2021, Vol.2021, p.1-12
Main Authors: Elbatal, I., Alotaibi, Naif
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Language:English
Subjects:
Aging
Algorithms
Life span
Mathematical models
Parameter estimation
Random variables
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description In this paper, a new flexible generator of continuous lifespan models referred to as the Topp-Leone Weibull G (TLWG) family is developed and studied. Several mathematical characteristics have been investigated. The new hazard rate of the new model can be “monotonically increasing,” “monotonically decreasing,” “bathtub,” and “J shape.” The Farlie Gumbel Morgenstern (FGM) and the modified FGM (MFGM) families and Clayton Copula (CCO) are used to describe and display simple type Copula. We discuss the estimation of the model parameters by the maximum likelihood (MLL) estimations. Simulations are carried out to show the consistency and efficiency of parameter estimates, and finally, real data sets are used to demonstrate the flexibility and potential usefulness of the proposed family of algorithms by using the TLW exponential model as example of the new suggested family.
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subjects Aging
Algorithms
Life span
Mathematical models
Parameter estimation
Random variables
title Modelling to Engineering Data Using a New Class of Continuous Models
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