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Inference on the Beta Type I Generalized Half Logistic Distribution under Right-Censored Observation with Application to COVID-19

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  • Phillip Oluwatobi Awodutire
  • Ethelbert Chinaka Nduka
  • Maxwell Azubike Ijomah
  • Oluwatosin Ruth Ilori
  • Oluwafemi Samson Balogun
  • Niansheng Tang

Abstract

In real-life situations, censoring issues do arise due to the incompleteness of data. This article examined the inferences on right-censored beta type I generalized half logistic distribution. In this work, some statistical properties of the beta type I generalized half logistic distribution were derived. Furthermore, the beta type I generalized half logistic distribution was studied under a censoring situation in the presence and absence of covariates. Estimation of model parameters was conducted using the maximum likelihood estimation method. A simulation study was carried out to assess the performance of the parameters of the model in terms of efficiency and consistency. In a real-life application, the model was applied to COVID-19 data and the necessary inferences were drawn.

Suggested Citation

  • Phillip Oluwatobi Awodutire & Ethelbert Chinaka Nduka & Maxwell Azubike Ijomah & Oluwatosin Ruth Ilori & Oluwafemi Samson Balogun & Niansheng Tang, 2022. "Inference on the Beta Type I Generalized Half Logistic Distribution under Right-Censored Observation with Application to COVID-19," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2022, pages 1-13, May.
    • Handle: RePEc:hin:jijmms:6858109
    DOI: 10.1155/2022/6858109
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