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    Volume 48, Issue 2 (March 2020)

    Estimation and Prediction for the Generalized Half Normal Distribution under Hybrid Censoring

    (Received 10 August 2017; accepted 28 March 2018)

    Published Online: 2020

    CODEN: JTEVAB

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    Abstract

    In this article, we make estimation and prediction inferences for the generalized half normal distribution. The maximum likelihood and Bayes estimators of unknown parameters are obtained based on hybrid Type I censored samples. We obtain asymptotic intervals using the observed Fisher information matrix and also construct bootstrap intervals of unknown parameters. Bayes estimators are obtained under the squared error loss function using different approximation methods. We also construct the highest posterior density intervals of unknown parameters. Further one- and two-sample predictors and prediction intervals of censored observations are discussed. A Monte Carlo simulation study is conducted to compare the performance of the proposed methods. We further analyze a real data set for illustrative purposes. Finally, conclusions are presented.

    Author Information:

    Sultana, Farha
    Department of Mathematics, Indian Institute of Technology Patna, Bihta, Bihar

    Tripathi, Yogesh Mani
    Department of Mathematics, Indian Institute of Technology Patna, Bihta, Bihar


    Stock #: JTE20170721

    ISSN:0090-3973

    DOI: 10.1520/JTE20170721

    Author
    Title Estimation and Prediction for the Generalized Half Normal Distribution under Hybrid Censoring
    Symposium ,
    Committee G03