Abstract
AbstractLength-Biased distributions are a special case of the more general form known as weighted distributions. We can exploit the conceptuality of Length-Biased distribution in the development of appropriate models for lifetime data. Its method is adjusting the original probability density function from real data and the expectation of those data. This modification can lead to correct conclusions of the models. Therefore, we introduced the Length-Biased version of the weighted Exponentiated inverted Weibull distribution in this paper. Various properties and the expressions for moments, coefficient of skewness, coefficient of kurtosis, moment generating function, hazard rate function, etc. are derived. The maximum likelihood estimates of the parameters of the proposed distribution are determined. The study results suggest that this distribution is an efficacious model in life time data analysis and other related fields.
Highlights
Weibull (1951), the Swedish Scientist interposed the Weibull distribution
The weighted distributions speculation gives a compositional formulation for the model confinement and data illustration problems
In Rao’s paper, he recognized several situations that can be modeled by weighted distributions
Summary
It is the most widely used distribution for the analyzation of life time data This distribution stipulates the immense encroachment of reliability and quality control such as ball bearing auto mobile components concrete bridges demography actuarial study and electrical insulation. In Rao’s paper, he recognized several situations that can be modeled by weighted distributions These situations refer to instances where the recoded observations cannot be considered as a random sample from the original distributions. Al-Khadim and Hussein (2014) proposed the length biased form of weighted Exponential and Rayleigh distribution. Considering the importance of weighted and Length-Biased distribution, we present the LengthBiased weighted Exponentiated Inverted Weibull Distribution (LBWEIWD) and the sub models which are the special cases of our proposed distribution. The real data-set has been analyzed in Sections 8 and 9 gives some brief conclusion
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
