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Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data

Author

Listed:
  • Anurag Pathak

    (Central University of Haryana)

  • Manoj Kumar

    (Central University of Haryana)

  • Sanjay Kumar Singh

    (Banaras Hindu University)

  • Umesh Singh

    (Banaras Hindu University)

Abstract

In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.

Suggested Citation

  • Anurag Pathak & Manoj Kumar & Sanjay Kumar Singh & Umesh Singh, 2022. "Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data," Annals of Data Science, Springer, vol. 9(1), pages 101-119, February.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:1:d:10.1007_s40745-021-00348-7
    DOI: 10.1007/s40745-021-00348-7
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    References listed on IDEAS

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    1. Manoj Kumar & Sanjay Kumar Singh & Umesh Singh, 2016. "Reliability Estimation for poisson-exponential model under Progressive type-II censoring data with binomial removal data," Statistica, Department of Statistics, University of Bologna, vol. 76(1), pages 3-26.
    2. Sanjay Kumar Singh & Umesh Singh & Manoj Kumar, 2013. "Estimation of Parameters of Generalized Inverted Exponential Distribution for Progressive Type-II Censored Sample with Binomial Removals," Journal of Probability and Statistics, Hindawi, vol. 2013, pages 1-12, December.
    3. Aman Khakharia & Vruddhi Shah & Sankalp Jain & Jash Shah & Amanshu Tiwari & Prathamesh Daphal & Mahesh Warang & Ninad Mehendale, 2021. "Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 1-19, March.
    4. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    5. Sanjay Kumar, 2020. "Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis," Annals of Data Science, Springer, vol. 7(3), pages 417-425, September.
    6. Aboma Temesgen & Abdisa Gurmesa & Yehenew Getchew, 2018. "Joint Modeling of Longitudinal CD4 Count and Time-to-Death of HIV/TB Co-infected Patients: A Case of Jimma University Specialized Hospital," Annals of Data Science, Springer, vol. 5(4), pages 659-678, December.
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    Cited by:

    1. Naresh Chandra Kabdwal & Qazi J. Azhad & Rashi Hora, 2024. "Statistical inference of the exponentiated exponential distribution based on progressive type-II censoring with optimal scheme," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3833-3853, August.
    2. Mustapha Muhammad & Lixia Liu & Badamasi Abba & Isyaku Muhammad & Mouna Bouchane & Hexin Zhang & Sani Musa, 2023. "A New Extension of the Topp–Leone-Family of Models with Applications to Real Data," Annals of Data Science, Springer, vol. 10(1), pages 225-250, February.

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