Abstract
This paper aims to find a statistical model for the COVID-19 spread in the United Kingdom and Canada. We used an efficient and superior model for fitting the COVID 19 mortality rates in these countries by specifying an optimal statistical model. A new lifetime distribution with two-parameter is introduced by a combination of inverted Topp-Leone distribution and modified Kies family to produce the modified Kies inverted Topp-Leone (MKITL) distribution, which covers a lot of application that both the traditional inverted Topp-Leone and the modified Kies provide poor fitting for them. This new distribution has many valuable properties as simple linear representation, hazard rate function, and moment function. We made several methods of estimations as maximum likelihood estimation, least squares estimators, weighted least-squares estimators, maximum product spacing, Crame´r-von Mises estimators, and Anderson-Darling estimators methods are applied to estimate the unknown parameters of MKITL distribution. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. also, we applied different data sets to the new distribution to assess its performance in modeling data.
Highlights
Modeling for lifetime distributions have attracted great attention over the years and decades, and their interest has grown over time because the importance of modeling phenomena and pandemics is very important
We made an estimation by using six methods of the modified Kies inverted Topp-Leone (MKITL) parameters, and compared between them to assess the performance of each method by a simulation study
Referring to data set one we can see that MKITL provides the highest P-value, and the lowest W*, A* and lowest Kolmogorov- Smirnov (KS) distance
Summary
Modeling for lifetime distributions have attracted great attention over the years and decades, and their interest has grown over time because the importance of modeling phenomena and pandemics is very important. Researchers in distribution theory prefer to make modeling for data either by introducing a new parameter to make the distribution of interest more flexible or perhaps by producing a new distribution family. Modeling is very interesting in numerous fields, such as industry, engineering, reliability, and medical research, see (Anake et al [1]) for more reading. The inverted distributions have a great importance due to their applicability in many areas such as biological sciences, life test problems, medical, etc. In this paper, we made a statistical modeling for COVID-19 mortality data in two different countries, for more reading about this, see Kumar [9], Khakharia et al [10], Wang [11], Lalmuanawma et al [12] and Bullock et al [13]
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