@article{MAKHILLJEAS2018131816814,
    title = {Two Parameter Lindley Distribution:Estimating the Reliability Function with
Fuzzy Data},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {18},
    pages = {7670-7676},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.7670.7676},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.7670.7676},
    author = {Nadia and},
    keywords = {Lindley,Bayes estimators,gamma,Monte-Carlo,precautionary,simulation study},
    abstract = {In this study, the maximum likelihood and approximate Bayes estimators to the reliability function of
two parameter Lindley distribution have been derived when the data are shown in fuzzy form. Bayes estimators
have been derived based on informative gamma priors with squared error and precautionary loss functions
according to approximate Lindley’s technique. The generated samples that follow the two parameter Lindley
distribution are converted to fuzzy data based on a specific fuzzy information system. In addition, obtained
estimators to the reliability function have been compared numerically through a Monte-Carlo simulation study
in terms of their integrated mean squared error values.}
    }