Browsing by Author "Deepmala"

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Analysis and prediction of COVID-19 spreading through Bayesian modelling with a case study of Uttar Pradesh, India
(Springer, 2022) Deepmala; Nishant Kumar Srivastava; Sanjay Kumar Singh; Umesh Singh
Predicting the dynamics of COVID-19 cases is imperative to enhance the health care system’s capacity, monitor the effects of policy interventions, and control the transmission. With this view, this paper examines the transmission process of the COVID-19 employing three types of confirmed, deceased, and recovered cases in Uttar Pradesh, India. We demonstrated an approach that has the power to sufficiently predict the number of confirmed, deceased, and recovered cases of COVID-19 in the near future, given the past occurrences. We used the logistic and Gompertz non-linear regression model under the Bayesian setup. In this regard, we built the prior distribution of the model using information obtained from some other states of India, which have already reached the advanced stage of COVID-19. This analysis did not consider any changes in government control measures. © 2022, The Author(s), under exclusive licence to Operational Research Society of India.
Inferences based on a balanced joint progressive type-II censoring scheme for Lindley distributed lifetimes
(Taylor and Francis Ltd., 2023) Deepmala; Sanjay Kumar Singh; Umesh Singh
The progressive censoring plan has achieved significant recognition in recent years. Its generalization, termed as the joint progressive censoring scheme, has also received numerous researchers’ attention. Mondal and Kundu proposed a balanced two sample type-II progressive censoring scheme, in which one practices life test in order to compare the lifetime of the products, produced from various lines in an identical experimental environment. This study concentrates on the estimation problem of lifetime Lindley distributions under the classical paradigm within the balanced two samples type-II progressive censoring framework. A simulation study has been conducted to evaluate the considered estimation procedures’ performance through the mean square error and the average length of the asymptotic confidence interval. The optimal censoring scheme is also formulated based on the criteria that rely on Fisher’s information. Finally, for an illustration purpose, a real-life application is presented. © 2021 Taylor & Francis Group, LLC.